project 3 introduction

How to write Introduction for a Project

Why write introduction for a project, tips for writing an introduction , keep it brief but impactful, use factual information, start with a punchline, mention the details, inspiration for the project, it should be in sync with the description, it should be different from the conclusion, language of the project introduction, 1. creative project, 2. business project, 3. research project, 4. college project.

A project is a task done by an individual or a group to achieve a specific aim within a stipulated  time . A project includes many interrelated sub-tasks to reach the final objective. A project may have particular rules to follow for individuals or groups. It also outlines the use of  resources  to complete the project. Any project needs an  introduction  as it mentions the entire details about the project, including the resources used and the timeline in which it was executed.

Like any mail, composition, or letter, a project also needs an introduction, as it introduces the team, the topic, and all other details. With an introduction, the project will retain identity, interest, and professionalism. Hence, a well-thought-out project introduction is imperative.

With an effective introduction, all your hard work can go to good use. As that’s the first para, a person reads, and it needs to be outlined well, or your project may look flat despite all the hours that went into it. A project intro is generally counted as one heading, but there may be cases when subheadings are required. While we are at this point, below are some  tips  for  writing  a goof introduction for a project.

An introduction should be brief, as more details can follow in subsequent paragraphs. The work of opening is to provide inputs that will be discussed later. It should also be noted that the brief here is relative to the entire project’s length. On average, the length of the introduction should be at most twenty per cent of the entire project and not less  than  ten per cent of the total count of words in the project. E.g. a project of 3000 words, will have an introduction between 300(10%) and 600(20%) words.

An introduction for a project should contain factual information. Factual information means information in numbers and figures. This will make the introduction brief and to the point. Numbers in the start  mean  significant information will be passed on, but it will require details in the description part to explain the source of those numbers. Using graphs or pictures will also make the introduction colourful or exciting.

Starting the project, an introduction with  quotes  or figures will give it a good punchline and generate reader interest. But it depends upon the type of project. Quoting a number would be a better option if the topic is related to business. In contrast, a creative project can have a quote from a famous person as the first line. E.g. “A deal of $5 million between company X and Y” will arouse sufficient interest and be impactful for a business project.

A gist of all the details that will follow suit can entice a reader to read further. Many times, if you submit a project that may be insignificant, but even if the superior reads all the relevant  points  covered in the introduction, half your battle is won.

It is a good idea to write about the inspiration for the project. Some ventures, when starting, have to make a project report, and they can briefly write about how they (the individual or group of people behind the project) got the idea. It could be an event that makes for an exciting read. This can also serve as a reminder for other people about how thoughts turn into projects due to inspiration.

The introduction for a project provides specific pointers for the description, and the details of the project should remain in that outline so that they do not look like elements added as an afterthought. The sync of the introduction should be with the description. Since the introduction of a project is written first, the report will have to follow the lead to be relevant.

The conclusion is written at the end and summarizes the whole project. To summarize does not mean repeating the introduction of the project. The introduction should be different from the conclusion. However, the length of the conclusion can be the same as the introduction. Again as in the case of description, the conclusion should follow the lead of the opening but have ending remarks that shapes the completion of project writing.

The language of the introduction of a project should be relevant to the topic of the project. There are many  types  of projects like projects done in college, projects done for professional courses or work, and creative projects. For example, a Business project will have a formal tone, while creative projects will have an informal style.

Examples of introduction for a project 

There are different types of projects, and the  examples  given here can help you with more clarity and help you with introduction writing. To understand the difference better, we will take the same sample and present it differently as per the project type. Here are some examples of writing a project introduction according to the kind of project.

In the below examples, emphasis is given according to the topic. For instance, a creative project gives more importance to the video, while a medicinal research project gives more weightage to the medicinal properties.

Let us take an example of a creative project about making videos on types of  medicinal plants . The creative part of this project is making a video. The Project introduction will contain how videos will be taken, where and the project’s duration. It can start with a quote like “healing comes from nature” and then describe the process of taking videos of medicinal plants. The camera used, and the type of mode used for shooting, the narration part, and light (natural or use of lights) will take a front seat. 

If the same example is taken for a business project introduction, then it will contain lots of factual data. The starting line can be “profit of millions from medicinal plants.” Introducing a business project can be making medicines out of plants and selling them for a profit or on sales projection with projections of numbers and figures. Graphs or charts can be used to portray numerical and statistical information better.

If the same example is for a research project, then the start of the introduction can be “50 types of medicinal plants that can help mankind live a healthy life.” The opening should emphasize what kind of plants can be termed medicinal plants. It will further categorize the plants according to usage or the area they flourish. The introduction can use some pictures related to the healing properties of plants.

The introduction of a college project will again depend on the type of course selected. If the course of the college project is related to biosciences, then the opening will be similar to the above. If the college project is a photo management course, the introduction can be similar to the creative project. Write your college project introduction based on your course and its weightage on your overall marks.

Remember any project; the base remains the same – make it brief and impactful, be it with a quote, facts, or numbers. Ultimately, the motive is to impress a reader or your professor or employer and entice them to read through the entire thing. 

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• Career Development

Project Introductions: What They Are and How To Write Them

Welcome to the blog post on project introduction! Project introduction is a vital step in the overall project process. It is the starting point to carry out a project and can be the difference between a successful project and one that fails. A great project introduction is detailed, concise, and includes information on the goals and objectives of the project, the team involved, and the timeline for completion. Project introductions should also include a thorough description of the project scope, and the deliverables that will be produced. Additionally, all stakeholders involved in the project should be identified and their roles clarified in order for successful delivery of the project. A great project introduction should serve as a blueprint for the project, offering clear direction and focus. Proper communication is key, and the project introduction should be communicated clearly to all parties involved. In this blog post, we will explore the importance of introducing a project and the key components that should be included in order to ensure the success of the project.

• Be clear in what you write: …
• Explain the reasons in the introduction: …
• Explain why it is important to you: …
• It should outline the specific objectives of the project:

What is a project introduction?

A project introduction is a paragraph or several paragraphs that explain the subject of the project. It should contain significant project information that enables the reader to comprehend the project’s goals and parameters. You may use project introductions for:

Project introductions are common in various industries, including:

How to write a project introduction in 12 steps

The steps you can take to write a strong project introduction are as follows:

1. Write the project introduction last

A project introduction should be written after your project is finished because it discusses the key ideas from your research or proposal. This way, the introduction contains accurate, relevant information.

2. Identify the purpose of the project

Your introduction should discuss why you completed the project. Depending on your field and position, your project’s goal might be to:

3. Discuss how you completed the project

Briefly describe in your introduction the approach you took to finish your project, such as the research design. Typically, this is a component of research papers and other technical reports. If you used a double-blind study, a survey of 1,000 participants, or a review of published literature, you might mention that in your introduction.

4. Describe any challenges you faced

You can mention any challenges you encountered while working on your project in your introduction. This aids the reader in comprehending the limitations and scope of your project. Lack of published research, a small number of study participants, or potential biases in self-reported results are some examples of challenges.

5. Provide background information

If appropriate, you can go over significant backstory in your introduction. This gives your readers more context and sheds more light on your motivation. Additionally, you can use background information to persuade your readers to be interested in your project and its outcomes.

If you’re writing about a new piece of technology, for instance, you might mention the significant advancements that made the technology possible or earlier versions of the equipment.

6. Include an outline of the project

You can list the main parts of your project in your introduction. Think about a grant application that a college is submitting, for instance. A sentence like, “This proposal includes a discussion of qualifications, an estimate of associated costs, a list of objectives, and the proposed findings” may be in the introduction. “.

7. Add a thesis statement, if necessary

Typically, the conclusion of your introduction for research papers, reports, and other academic writing includes a thesis statement. The thesis statement outlines the key ideas you’ll cover in your essay or paper. In this report, we examine the success rates of visual, auditory, and verbal instruction in 300 middle school science students. For instance, here is a thesis statement for a research paper about educational styles: “.

8. Be clear and concise

Your introduction should be direct and brief. Aim to keep your introduction to one page or less. Use the introduction to capture readers’ interest and compel them to participate in your project. Avoid repeating information from your project or giving the introduction too many details. Instead, limit your introduction to more general explanations.

9. Consider subheadings

You could use subheadings to help organize your information if your introduction is lengthy. This can improve your content’s readability and clarity. Subheadings in your introduction may include:

10. Write for your audience

Your project’s style and tone should be consistent with the introduction you write. When choosing your vocabulary and technical terminology, keep in mind who your audience is. You want your readers to be able to comprehend what you’ve written.

11. Proofread your introduction

Once you’ve finished writing your introduction, it’s critical to proofread your work for proper grammar and spelling. Consider having a colleague review your introduction to ensure that the content is clear and well-organized.

12. Format your introduction

Include the proper formatting and styling in the final version of your introduction to correspond with the rest of your project and other necessary requirements. For instance, your professor or department will usually specify the style and format you should use if you are submitting a thesis for graduate school. Clients may also include formatting instructions in their request for proposals.

Microsoft Project introduction and roadmap

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• Knowledge Base
• Research paper

Writing a Research Paper Introduction | Step-by-Step Guide

Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.

The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your topic and get the reader interested
• Provide background or summarize existing research
• Position your own approach
• Detail your specific research problem and problem statement
• Give an overview of the paper’s structure

The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.

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Table of contents

Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.

The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.

The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.

For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:

A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:

Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.

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This part of the introduction differs depending on what approach your paper is taking.

In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.

Argumentative paper: Background information

After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.

Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .

Empirical paper: Describing previous research

For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.

This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.

Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.

The next step is to clarify how your own research fits in and what problem it addresses.

Argumentative paper: Emphasize importance

In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.

Empirical paper: Relate to the literature

In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:

• What research gap is your work intended to fill?
• What limitations in previous work does it address?
• What contribution to knowledge does it make?

You can make the connection between your problem and the existing research using phrases like the following.

Now you’ll get into the specifics of what you intend to find out or express in your research paper.

The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).

Argumentative paper: Thesis statement

The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.

Empirical paper: Research question and hypothesis

The research question is the question you want to answer in an empirical research paper.

Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.

A research question can be framed either directly or indirectly.

• This study set out to answer the following question: What effects does daily use of Instagram have on the prevalence of body image issues among adolescent girls?
• We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls.

If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.

For example, the following hypothesis might respond to the research question above:

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The final part of the introduction is often dedicated to a brief overview of the rest of the paper.

In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.

If included, the overview should be concise, direct, and written in the present tense.

• This paper will first discuss several examples of survey-based research into adolescent social media use, then will go on to …
• This paper first discusses several examples of survey-based research into adolescent social media use, then goes on to …

Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.

The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

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Caulfield, J. (2023, March 27). Writing a Research Paper Introduction | Step-by-Step Guide. Scribbr. Retrieved April 3, 2024, from https://www.scribbr.com/research-paper/research-paper-introduction/

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Project 3 Introduction - TEST

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• Assignments

Spring 2024

Time and Location

Mon. & Wed. 12:30 PM - 1:20 PM Pacific Time Jordan Hall room 040 (420-040)

Week 1: Introduction and Acoustic Phonetics

Deliverables

• Assignment 1 released on Mon 4.1.24.

Lecture 1 (Mon 4.1.24)

Course introduction.

lecture slides

Lecture 2 (Wed 4.3.24)

Phonetics: Articulatory phonetics. Acoustics. ARPAbet transcription. Readings:

• J+M Draft Edition Appendix H: Phonetics, online pdf
• Fun read (optional). The Art of Language Invention . David J Peterson. 2015.

Week 2: Speech Synthesis / Text to Speech (TTS)

Course Project Overview released on Mon 4.8.24.

Lecture 3 (Mon 4.8.24)

Some history of ASR, TTS, and dialog. TTS Overview. Text normalization. Letter-to-sound. Prosody. Readings:

• J+M Draft Edition Chapter 16.6: TTS online pdf

Lecture 4 (Wed 4.10.24)

Foundations of TTS: Data collection. Evaluation. Signal processing. Concatenative and parametric approaches. Readings:

• J+M Draft Edition Chapter 16.6: TTS (cont’d) online pdf

Week 3: Course project + TTS with deep learning

• Assignment 1 due by Monday 4.15.24 11:59PM Pacific.
• Assignment 2 released on Monday 4.15.24.

Lecture 5 (Mon 4.15.24)

Course project overview and Q&A. Social meaning extraction as supervised machine learning. Readings:

• Rajesh Ranganath, Dan Jurafsky, and Daniel A. McFarland. . Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates . Computer Speech and Language. 2013.

Lecture 6 (Wed 4.17.24)

Deep learning for TTS. Readings:

• Wang, Y., Skerry-Ryan, R.J., Stanton, D., Wu, Y., Weiss, R.J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S. and Le, Q., Tacotron: Towards end-to-end speech synthesis . arXiv. 2017.
• Oord, A.V.D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K. Wavenet: A generative model for raw audio . arXiv. 2016.
• Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., and Liu, T. Y. Fastspeech: Fast, robust and controllable text to speech . Advances in Neural Information Processing Systems 32. 2019.

Deep Learning Preliminaries. Review on your own as needed depending on your experience so far with deep learning models:

• J+M Draft Edition Chapter 7: Neural Networks and Neural Language Models. pdf
• J+M Draft Edition Chapter 9: RNNs and LSTMs. pdf
• J+M Draft Edition Chapter 10: Transformers and Large Language Models. pdf
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time

Week 4: Speech to Text / Automatic Speech Recognition (ASR)

• Course Project Proposal due by Wednesday 4.24.24 11:59PM Pacific.

Lecture 7 (Mon 4.22.24)

Speech recognition overview: Noisy channel model. Word error rate metrics. Hidden Markov models (HMMs). Readings:

• J+M Draft Edition Chapter 16.1, 16.2, 16.3, 16.5: Automatic Speech Recognition online pdf
• J+M Draft Edition. Appendix A pdf
• J+M Draft Edition. Appendix B pdf
• Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J.R., Jurafsky, D. and Goel, S. Racial disparities in automated speech recognition . Proceedings of the National Academy of Sciences. 2020.
• J+M Draft Edition Chapter 3: N-gram Language Models. pdf
• Lecture videos on introductory NLP including language modeling youtube

Lecture 8 (Wed 4.24.24)

Speech recognition: HMM-DNN systems. Connectionist Temporal Classification (CTC). End-to-end neural ASR. Readings:

• Hinton, Geoffrey, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine. 2012.
• J+M Draft Edition Chapter 16.4: CTC online pdf
• Graves, A., and Jaitly, N. Towards end-to-end speech recognition with recurrent neural networks. ICML . 2014.
• Maas, A. , Xie, Z. , Jurafsky, D., & Ng, A. Lexicon-free conversational speech recognition with neural networks. ACL-HLT . 2015. (* indicates equal contribution)
• Chan, W., Jaitly, N., Le, Q.V. and Vinyals, O. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition ICASSP. 2016. arXiv preprint.
• Prabhavalkar, R., Hori, T., Sainath, T. N., Schlüter, R., and Watanabe, S. End-to-end speech recognition: A survey . IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2023.

Week 5: State-of-the-art ASR and customizing ASR for products

• Assignment 2 due by Wednesday 5.1.24 11:59PM Pacific.

Lecture 9 (Mon 4.29.24)

State-of-the-art deep learning approaches for speech recognition. Conformer. Whisper. Fine tuning base models. Readings:

• Gulati, A., Qin, J., Chiu, C.C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y. and Pang, R. Conformer: Convolution-augmented transformer for speech recognition . arXiv. 2020. 2.TBD

Lecture 10 (Wed 5.1.24)

Guest Lecture: Ello : A case study in building spoken language products. Readings:

• T. Bluche, M. Primet, & T. Gisselbrecht. Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks .ArXiv. 2020.
• K. Audhkhasi, A. Rosenberg, A. Sethy, B. Ramabhadran, & B. Kingsbury. End-to-End ASR-free Keyword Search from Speech . IEEE J. Signal Processing 2017.
• N. Sacchi, A. Nanchen, M. Jaggi, & M. Cerňak. Open-Vocabulary Keyword Spotting with Audio and Text Embeddings . Interspeech 2019.

Week 6: Foundation models and non-English languages

• Assignment 3 released on Mon 5.6.24.

Lecture 11 (Mon 5.6.24)

Guest Lecture: Foundation models for spoken language. Dr. Karen Livescu Readings

• Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. Wav2vec 2.0: A framework for self-supervised learning of speech representations . Advances in Neural Information Processing Systems, 33. 2020.

Lecture 12 (Wed 5.8.24)

Week 7: non-english spoken language understanding cont’d + project check-ins, lecture 13 (mon 5.13.24), lecture 14 (wed 5.15.24).

Project check-ins during class. Each group will speak for ~2 minutes about progress and planned work

Week 8: Introduction to spoken dialog + project check-ins

• Assignment 3 due by Monday 5.20.24 11:59PM Pacific.

Lecture 15 (Mon 5.20.24)

Lecture 16 (wed 5.22.24).

Overview of dialog: Human conversation. Task-oriented dialog. Dialog system design. GUS and frame-based dialog systems. Readings:

• J+M Draft Edition Chapter 15: Dialogue Systems and Chatbots, online pdf

Week 9: Spoken dialog with LLMs

• Course Project Milestone due by Monday 5.27.24 11:59PM Pacific.

Memorial Day. NO CLASS (Mon 5.27.24)

Lecture 17 (wed 5.29.24).

Guest lecture: Developing spoken dialog systems with LLMs. Gridspace .

Week 10 : Spoken dialog development & final poster session

Lecture 18 (mon 6.3.24).

Case study: Alexa Skills Kit in the era of LLMs. Readings:

• Understand Custom Skills . You do not need to cover adding visual components to skills.
• Interaction Model Design

Final project poster session (Wed 6.5.24)

Present posters at in-person session during lecture time. Location TBD

Course Project Report due by Saturday 6.8.24 by 11:59 PM Pacific. No late days allowed

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Module 4: Protecting Your Data in Transit

Introduction.

What happens to your data after you send a text, fire off an email, access a shared cloud doc, or enter a query in your search engine? How does a text message make it from your phone to a friend’s? How do the edits that you make in a shared cloud doc appear for your collaborators almost instantaneously?

This is the concept of “ data in transit ,” or “ data in motion . ” For most people, this concept probably feels like a black box.

Module 4 will demystify the process of “data in transit” by helping you to understand what happens to your data after you hit “send” on a text message or enter a query into your search engine. Even more importantly, you’ll learn about what actions you can take to prevent threat actors from accessing your data while it’s in motion.

Module 4 covers the following topics:

• Topic 4.0 – How to communicate securely on your mobile device
• Topic 4.1 – Tips to stay safe while surfing the web, Part 1: Web browser settings
• Topic 4.2 – Tips to stay safe while surfing the web, Part 2: Accessing websites securely
• Topic 4.3 – Get the most out of cloud storage & services while minimizing the risk

Topic 4.0 How to Communicate Securely on Your Mobile Device

Follow these best practices for securely communicating on your mobile device and when sending data over email.

Topic 4.1 Tips to Stay Safe while Surfing the Web, Part 1: Web Browser Settings

Implement these best practices to help better mitigate malware risks when using web browsers.

Topic 4.2 Tips to Stay Safe while Surfing the Web, Part 2: Accessing Websites Securely

Follow these steps to help ensure a secure connection to an authentic website.

Topic 4.3 Get the Most out of Cloud Storage and Services while Minimizing the Risk

Become more informed of cloud services while better minimizing security risks.

Project Upskill Glossary

Learn more about the terms and concepts referenced in Project Upskill to help users improve their defense against common cybersecurity threats.

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How to Write a Project Proposal (Examples & Template Included)

Table of Contents

What is a project proposal, types of project proposals, project proposal vs. project charter, project proposal vs. business case, project proposal vs. project plan, project proposal outline, how to write a project proposal, project proposal example, project proposal tips.

• ProjectManager & Project Proposals

A project proposal is a project management document that’s used to define the objectives and requirements of a project. It helps organizations and external project stakeholders agree on an initial project planning framework.

The main purpose of a project proposal is to get buy-in from decision-makers. That’s why a project proposal outlines your project’s core value proposition; it sells value to both internal and external project stakeholders. The intent of the proposal is to grab the attention of stakeholders and project sponsors. Then, the next step is getting them excited about the project summary.

Getting into the heads of the audience for which you’re writing the project proposal is vital: you need to think like the project’s stakeholders to deliver a proposal that meets their needs.

We’ve created a free project proposal template for Word to help structure documents, so you don’t have to remember the process each time.

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Project Proposal Template

Use this free Project Proposal Template for Word to manage your projects better.

In terms of types of project proposals, you can have one that’s formally solicited, informally solicited or a combination. There can also be renewal and supplemental proposals. Here’s a brief description of each of them.

• Solicited project proposal: This is sent as a response to a request for proposal (RFP) . Here, you’ll need to adhere to the RFP guidelines of the project owner.
• Unsolicited project proposal: You can send project proposals without having received a request for a proposal. This can happen in open bids for construction projects , where a project owner receives unsolicited project proposals from many contractors.
• Informal project proposal: This type of project proposal is created when a client asks for an informal proposal without an RFP.
• Renewal project proposal: You can use a renewal project proposal when you’re reaching out to past customers. The advantage is that you can highlight past positive results and future benefits.
• Continuation project proposal: A continuation project proposal is sent to investors and stakeholders to communicate project progress.
• Supplemental project proposal: This proposal is sent to investors to ask for additional resources during the project execution phase.

A project proposal is a detailed project document that’s used to convince the project sponsor that the project being proposed is worth the time, money and effort to deliver it. This is done by showing how the project will address a business problem or opportunity. It also outlines the work that will be done and how it will be done.

A project charter can seem like the same thing as a project proposal as it also defines the project in a document. It identifies the project objectives, scope, goals, stakeholders and team. But it’s done after the project has been agreed upon by all stakeholders and the project has been accepted. The project charter authorizes the project and documents its requirements to meet stakeholders’ needs.

A business case is used to explain why the proposed project is justified. It shows that the project is worth the investment of time and money. It’s more commonly used in larger companies in the decision-making process when prioritizing one project over another.

The business case answers the questions: what is the project, why should it be taken up, who will be involved and how much will it cost? It’s therefore related to a project proposal, but the project proposal comes before the business case and is usually part of the larger proposal.

Again, the project proposal and the project plan in this case are very similar documents. It’s understandable that there would be some confusion between these two project terms. They both show how the project will be run and what the results will be. However, they’re not the same.

The project proposal is a document that aims to get a project approved and funded. It’s used to convince stakeholders of the viability of the project and their investment. The project plan, on the other hand, is made during the planning phase of the project, once it’s been approved. It’s a detailed outline of how the project will be implemented, including schedule, budget, resources and more.

All the elements in the above project proposal outline are present in our template. This free project proposal template for Word will provide you with everything you need to write an excellent project proposal. It will help you with the executive summary, project process, deliverables, costs—even terms and conditions. Download your free template today.

There are several key operational and strategic questions to consider, including:

• Executive summary: This is the elevator pitch that outlines the project being proposed and why it makes business sense. While it also touches on the information that’ll follow in the project proposal, the executive summary should be brief and to the point.
• Project background: This is another short part of the proposal, usually only one page, which explains the problem you’ll solve or the opportunity you’re taking advantage of with the proposed project. Also, provide a short history of the business to put the company in context to the project and why it’s a good fit.
• Project vision & success criteria: State the goal of the project and how it aligns with the goals of the company. Be specific. Also, note the metrics used to measure the success of the project.
• Potential risks and mitigation strategies: There are always risks. Detail them here and what strategies you’ll employ to mitigate any negative impact as well as take advantage of any positive risk.
• Project scope & deliverables: Define the project scope, which is all the work that has to be done and how it will be done. Also, detail the various deliverables that the project will have.
• Set SMART goals: When setting goals, be SMART. That’s an acronym for specific, measurable, achievable, relevant and time-bound. All your goals would be defined by those five things.
• Project approach: Define the approach you’ll use for the contract. There are several different types of contracts used in construction , for example, such as lump sum, cost plus, time and materials, etc. This is also a good place to describe the delivery method you’ll use.
• Expected benefits: Outline the benefits that will come from the successful completion of the project.
• Project resource requirements: List the resources, such as labor, materials, equipment, etc., that you’ll need to execute the project if approved.
• Project costs & budget: Detail all the costs, including resources, that’ll be required to complete the project and set up a budget to show how those costs will be spent over the course of the project.
• Project timeline: Lay out the project timeline , which shows the project from start to finish, including the duration of each phase and the tasks within it, milestones, etc.

In addition to these elements, it’s advisable to use a cover letter, which is a one-page document that helps you introduce your project proposal and grab the attention of potential clients and stakeholders.

To make the best proposal possible, you’ll want to be thorough and hit on all the points we’ve listed above. Here’s a step-by-step guide to writing a persuasive priority proposal.

1. Write an Executive Summary

The executive summary provides a quick overview of the main elements of your project proposal, such as your project background, project objectives and project deliverables, among other things. The goal is to capture the attention of your audience and get them excited about the project you’re proposing. It’s essentially the “elevator pitch” for the project life cycle. It should be short and to the point.

The executive summary should be descriptive and paint a picture of what project success looks like for the client. Most importantly, it should motivate the project client; after all, the goal is getting them to sign on the dotted line to get the project moving!

2. Provide a Project Background

The project background is a one-page section of your project proposal that explains the problem that your project will solve. You should explain when this issue started, its current state and how your project will be the ideal solution.

• Historic data: The history section outlines previously successful projects and those that could have run more smoothly. By doing so, this section establishes precedents and how the next project can be more effective using information from previous projects.
• Solution: The solution section addresses how your project will solve the client’s problem. Accordingly, this section includes any project management techniques , skills and procedures your team will use to work efficiently.

3. Establish a Project Vision & Success Criteria

You’ll need to define your project vision. This is best done with a vision statement, which acts as the north star for your project. It’s not specific as much as it’s a way to describe the impact your company plans to make with the project.

It’s also important to set up success criteria to show that the project is in fact doing what it’s proposed to do. Three obvious project success criteria are the triple constraint of cost, scope and time. But you’ll need to set up a way to measure these metrics and respond to them if they’re not meeting your plan.

4. Identify Potential Risks and Mitigation Strategies

To reduce the impact of risk in your project, you need to identify what those risks might be and develop a plan to mitigate them . List all the risks, prioritize them, describe what you’ll do to mitigate or take advantage of them and who on the team is responsible for keeping an eye out for them and resolving them.

5. Define Your Project Scope and Project Deliverables

The project scope refers to all the work that’ll be executed. It defines the work items, work packages and deliverables that’ll be delivered during the execution phase of your project life cycle. It’s important to use a work breakdown structure (WBS) to define your tasks and subtasks and prioritize them.

6. Set SMART Goals for Your Project Proposal

The best mindset when developing goals and objectives for your project proposal is to use the SMART system :

• Specific – Make sure your goals and objectives are clear, concise and specific to the task at hand.
• Measurable – Ensure your goals and objectives are measurable so it’s obvious to see when things are on track and going well, and conversely, when things are off track and issues need to be addressed. Measurable goals make it easy to develop the milestones you’ll use to track the progress of the project and identify a reasonable date for completion and/or closure.
• Attainable – It’s important every project has a “reach” goal. Hitting this goal would mean an outstanding project that extends above and beyond expectations. However, it’s important that the project’s core goal is attainable, so morale stays high and the job gets done with time and resources to spare.
• Relevant – Make sure all of your goals are directly relevant to the project and address the scope within which you’re working.
• Time-Based – Timelines and specific dates should be at the core of all goals and objectives. This helps keep the project on track and ensures all project team members can manage the work that’s ahead of them.

7. Explain What’s Your Project Approach

Your project approach defines the project management methodology , tools and governance for your project. In simple terms, it allows project managers to explain to stakeholders how the project will be planned, executed and controlled successfully.

8. Outline The Expected Benefits of Your Project Proposal

If you want to convince internal stakeholders and external investors, you’ll need to show them the financial benefits that your project could bring to their organization. You can use cost-benefit analysis and projected financial statements to demonstrate why your project is profitable.

9. Identify Project Resource Requirements

Project resources are critical for the execution of your project. The project proposal briefly describes what resources are needed and how they’ll be used. Later, during the planning phase, you’ll need to create a resource management plan that’ll be an important element of your project plan. Project requirements are the items, materials and resources needed for the project. This section should cover both internal and external needs.

10. Estimate Project Costs and Project Budget

All the resources that you’ll need for your project have a price tag. That’s why you need to estimate those costs and create a project budget . The project budget needs to cover all your project expenses, and as a project manager, you’ll need to make sure that you adhere to the budget.

11. Define a Project Timeline

Once you’ve defined your project scope, you’ll need to estimate the duration of each task to create a project timeline. Later during the project planning phase , you’ll need to create a schedule baseline, which estimates the total length of your project. Once the project starts, you’ll compare your actual project schedule to the schedule baseline to monitor progress.

Now let’s explore some project proposal examples to get a better understanding of how a project proposal would work in the real world. For this example, let’s imagine a city that’s about to build a rapid transit system. The city government has the funds to invest but lacks the technical expertise and resources that are needed to build it, so it issues a request for proposal (RFP) document and sends it to potential builders.

Then, the construction companies that are interested in executing this rapid transit project will prepare a project proposal for the city government. Here are some of the key elements they should include.

• Project background: The construction firm will provide an explanation of the challenges that the project presents from a technical perspective, along with historical data from similar projects that have been completed successfully by the company.
• Project vision & success criteria: Write a vision statement and explain how you’ll track the triple constraint to ensure the successful delivery of the project.
• Potential risks and mitigation strategies: List all risks and how they’ll be mitigated, and be sure to prioritize them.
• Project scope & deliverables: The work that’ll be done is outlined in the scope, including all the deliverables that’ll be completed over the life cycle of the project.
• Set SMART goals: Use the SMART technique to define your project goals by whether they’re specific, measurable, achievable, relevant and time-bound.
• Project approach: Define the methodology that the project manager will employ to manage the project. Also, figure out what type of contract will be used to define the project.
• Expected benefits: Show how the project will deliver advantages to the company and define what these benefits are in a quantifiable way.
• Project resource requirements: List all the resources, such as labor, materials, equipment, etc., needed to execute the project.
• Project costs & budget: Estimate the cost of the project and lay that out in a project budget that covers everything from start to finish.
• Project timeline: Outline the project schedule, including phases, milestones and task duration on a visual timeline.

Whatever project proposal you’re working on, there are a few tips that apply as best practices for all. While above we suggested a project proposal template that would have a table of contents, meaning it would be many pages long, the best-case scenario is keeping the proposal to one or two pages max. Remember, you’re trying to win over stakeholders, not bore them.

Speaking of project stakeholders , do the research. You want to address the right ones. There’s no point in doing all the work necessary to write a great proposal only to have it directed to the wrong target audience. Whoever is going to read it, though, should be able to comprehend the proposal. Keep the language simple and direct.

When it comes to writing, get a professional. Even a business document like a project proposal, business case or executive summary will suffer if it’s poorly constructed or has typos. If you don’t want to hire a professional business writer, make sure you get someone on your project team to copy, edit and proof the document. The more eyes on it, the less likely mistakes will make it to the final edition.

While you want to keep the proposal short and sweet, it helps to sweeten the pot by adding customer testimonials to the attachments. Nothing sells a project plan better than a customer base looking for your product or service.

ProjectManager & Project Proposals

ProjectManager allows you to plan proposals within our software. You can update tasks for the project proposal to signify where things stand and what’s left to be done. The columns allow you to organize your proposal by section, creating a work breakdown structure (WBS) of sorts.

When building a project proposal, it’s vital to remember your target audience. Your audience includes those who are excited about the project, and see completion as a gain for their organization. Conversely, others in your audience will see the project as a pain and something to which they aren’t looking forward. To keep both parties satisfied, it’s essential to keep language factual and concise.

Our online kanban boards help you think through that language and collaborate on it effectively with other team members, if necessary. Each card shows the percentage completed so everyone in the project management team is aware of the work done and what’s left to be done.

As you can see from the kanban board above, work has begun on tasks such as product documentation and design. Tasks regarding stakeholder feedback, ideation, market research and more have been completed, and there’s a good start on the engineering drawings, 3D rendering, supply chain sourcing and translation services.

A PDF is then attached to the card, and everyone added to the task receives an email notifying them of the change. This same process can be used throughout the life-cycle of the project to keep the team updated, collaborating, and producing a first-class project proposal. In addition to kanban boards, you can also use other project management tools such as Gantt charts , project dashboards, task lists and project calendars to plan, schedule and track your projects.

Project proposals are just the first step in the project planning process. Once your project is approved, you’ll have to solidify the plan, allocate and manage resources, monitor the project, and finally hand in your deliverables. This process requires a flexible, dynamic and robust project management software package. ProjectManager is online project management software that helps all your team members collaborate and manage this process in real-time. Try our award-winning software with this free 30-day trial .

Deliver your projects on time and under budget

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Project: Video Submission

Project video presentation.

Due: Wednesday, 4/24

General Directions

The project video presentation is intended to provide a high-level overview of your project to an audience of your peers (that is, individuals who have a reasonable knowledge of data science but are not experts in your particular project topic). The presentation should demonstrate your ability to communicate the significance and interpret the findings of your research project. The presentation should stand on its own so that it makes sense to someone who has not read your proposal or prototype.

Your group should create a video recording of your presentation in which every group member speaks and in which you use a visual aid such as presentation slides. The easiest way to do this is to simply hold a zoom call with all members of your project group, share your screen with your presentation slides, and record either locally or to the cloud ( see Zoom recording help information ). If this is not possible, you can also record portions individually and combine the recordings (though this will require additional editing work). In the end, we will ask for a URL to your complete recording, so you can either provide a share link to a zoom cloud recording or you can record locally and then upload your recording to Duke Box, Warpwire, or any other cloud platform that we can access such that we can view your recording directly online (we should not need to download to view the recording). Ensure that anyone with the link can view the recording.

In terms of length, the presentation should be between 8 and 12 minutes . You can have as many slides as are necessary, but a typical pace has 1-2 slides per minute, so 8-24 slides total would be reasonable. Your slides should prioritize well-labeled figures or visualizations and use text sparingly to emphasize important points. The text should also be large enough that it is reasonably easy to read. When you are finished, you will submit a pdf of your slides to Gradescope under the assignment “Project Video Presentation.” Be sure to include your names and NetIDs in your final document and use the group submission feature on Gradescope.  Your first slide should include the URL where we can view the recording of your presentation.

• E (Exemplary, 10pts) – Video presentation is between 8 and 12 minutes.
• S (Satisfactory, 9 pts) – Video presentation is over 12 minutes.
• N (Not yet, 6pts) – Video presentation does not reach 8 minutes.
• U (Unassessable, 2pts) –  Video presentation is missing or does not demonstrate meaningful effort.

Part 0: Title Slide

The very first slide of your presentation should be a title slide containing at least the below information. It does not need to be in the actual video recording.

• A descriptive title of your project/presentation, not “CS216 Presentation Video”
• Names of all group members
• URL to the video recording of your presentation
• E (Exemplary, 10pts) – Work that meets all requirements.
• S (Satisfactory, 9pts) – The title is not descriptive but meets all other requirements.
• N (Not yet, 6pts) – Does not meet all requirements.

Part 1: Introduction and Research Questions

Your presentation should begin by introducing your topic generally and posing your research questions. Provide some explanation of the relevance or motivation of your research questions .

• E (Exemplary, 20pts) – General introduction to topic and clearly defined research questions and their motivations.
• S (Satisfactory, 19pts) – General introduction to topic and clearly defined research questions. Discussion of motivations may be missing.
• N (Not yet, 12pts) – General introduction to topic. Research questions and motivations are not clearly defined.
• U (Unassessable, 4pts) – Introduction and research questions are missing or do not demonstrate meaningful effort.

Part 2: Data Sources

Discuss the data you collected and used to answer your research questions. Be specific: name the datasets you are using, the information they contain, and where they were collected from/how they were prepared.

• E (Exemplary, 20pts) – Origins of data are properly specified, cited, and include discussion of what information they contain. Any relevant data wrangling, cleaning, or other data preparation is explained.
• S (Satisfactory, 19pts) – Origins of data are properly specified, cited, and include discussion of what information they contain. Any relevant data wrangling, cleaning, or other data preparation may be missing or could be improved.
• N (Not yet, 12pts) – Poorly specified data sources and lack of discussion of preparing the dataset.
• U (Unassessable, 4pts) – Discussion of data sources and data preparation are missing or do not demonstrate meaningful effort.

Part 3: Results

Describe your results. Where possible, provide well-labeled and legible charts/figures in your slides to summarize results instead of verbose text. Interpret the results in the context of your research questions. It may not be possible to describe every individual result from your project in a brief amount of time. Focus on the most important and essential results for addressing your research questions. Please note that a screenshot of your dataset does not count as a table or figure and should not be included in your video presentation.

Unlike your final report, it is not generally possible to describe your methods in sufficient detail in a short presentation so that an informed audience member could reproduce your results. Instead, you should focus on your results and their interpretation, and only discuss methods at a high level such as may be necessary to interpret the results.

Example of Interpreting results

Do not: “When we conducted our hypothesis test, we found that p < 0.05, so our results are significant.”

Do: “Since our p-value is significant, we could determine that generation 1 pokemon have a different popularity than all other pokemon. And since the mean popularity of generation 1 is higher than the mean of all the other pokemon, we can conclude that generation 1 is on average more popular.” [The slide shows the p-value]

• E (Exemplary, 20pts) – Most important and essential results are thoroughly discussed using labeled tables or figures followed by an interpretation of the results in the context of the research questions. 
• S (Satisfactory, 19pts) – Results are thoroughly discussed using labeled tables or figures followed by an interpretation of the results in the context of the research questions. Maybe missing an important result that should have been included.
• N (Not yet, 12pts) – Results are discussed using tables with missing labels or lacking interpretation in the context of the research questions.
• U (Unassessable, 4pts) – Results are missing or do not demonstrate meaningful effort. 

Part 4: Limitations and Future Work

You should briefly discuss any important limitations or caveats to your results with respect to answering your research questions. For example, if you don’t have as much data as you would like or are unable to fairly evaluate the performance of a predictive model, explain and contextualize those limitations. You may want to consider any ethical implications or acknowledge potential biases in the results. 

Finally, provide a brief discussion of future work. This could explain how future research might address the limitations you outline, or it could pose additional follow-up research questions based on your results so far. In short, explain how an informed audience member (such as a peer in the class) could improve on and extend your results.

• E (Exemplary, 20pts) – Comprehensive and explicit discussion of important limitations and caveats to results. Brief discussion of future work and how results could be extended and improved upon.
• S (Satisfactory, 19pts) – Comprehensive and explicit discussion of important limitations and caveats to results. Discussion of future work and how results could be extended and improved upon may lack some specification.
• N (Not yet, 12pts) – Incomplete discussion of important limitations and caveats to results. Discussion of future work and how results could be extended and improved upon may lack some specification.
• U (Unassessable, 4pts) – Limitations and future work are missing or do not demonstrate meaningful effort.

Checklist Before You Submit:

• Is your video presentation between 8 and 12 minutes in length?
• A title of your project/presentation
• Do you feel as if this part meets the requirements of E (Exemplary) or S (Satisfactory) ?

Author Joey Scarpa

Posted March 30, 2024 — 4:30 pm

Categories Project

Next Project: Video Submission

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Project 3: Reinforcement Learning

Table of contents, introduction, question 1 (6 points): value iteration, question 2 (5 points): policies, question 3 (6 points): q-learning, question 4 (2 points): epsilon greedy, question 5 (2 point): q-learning and pacman, question 6 (4 points): approximate q-learning.

In this project, you will implement value iteration and Q-learning. You will test your agents first on Gridworld (from class), then apply them to a simulated robot controller (Crawler) and Pacman.

As in previous projects, this project includes an autograder for you to grade your solutions on your machine. This can be run on all questions with the command:

It can be run for one particular question, such as q2, by:

It can be run for one particular test by commands of the form:

The code for this project contains the following files, available as a zip archive .

Files to Edit and Submit : You will fill in portions of valueIterationAgents.py, qlearningAgents.py, and analysis.py during the assignment. Please do not change the other files in this distribution or submit any of our original files other than these files.

Evaluation : Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation – not the autograder’s judgements – will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.

Academic Dishonesty : We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else’s code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don’t try. We trust you all to submit your own work only; please don’t let us down. If you do, we will pursue the strongest consequences available to us.

Getting Help : You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, section, and the discussion forum are there for your support; please use them. If you can’t make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don’t know when or how to help unless you ask.

Discussion : Please be careful not to post spoilers.

To get started, run Gridworld in manual control mode, which uses the arrow keys:

You will see the two-exit layout from class. The blue dot is the agent. Note that when you press up, the agent only actually moves north 80% of the time. Such is the life of a Gridworld agent!

You can control many aspects of the simulation. A full list of options is available by running:

The default agent moves randomly

You should see the random agent bounce around the grid until it happens upon an exit. Not the finest hour for an AI agent.

Note: The Gridworld MDP is such that you first must enter a pre-terminal state (the double boxes shown in the GUI) and then take the special ‘exit’ action before the episode actually ends (in the true terminal state called TERMINAL_STATE , which is not shown in the GUI). If you run an episode manually, your total return may be less than you expected, due to the discount rate ( -d to change; 0.9 by default).

Look at the console output that accompanies the graphical output (or use -t for all text). You will be told about each transition the agent experiences (to turn this off, use -q ).

As in Pacman, positions are represented by (x, y) Cartesian coordinates and any arrays are indexed by [x][y] , with 'north' being the direction of increasing y , etc. By default, most transitions will receive a reward of zero, though you can change this with the living reward option ( -r ).

Recall the value iteration state update equation:

Write a value iteration agent in ValueIterationAgent , which has been partially specified for you in valueIterationAgents.py . Your value iteration agent is an offline planner, not a reinforcement learning agent, and so the relevant training option is the number of iterations of value iteration it should run (option -i ) in its initial planning phase. ValueIterationAgent takes an MDP on construction and runs value iteration for the specified number of iterations before the constructor returns.

Value iteration computes \(k\)-step estimates of the optimal values, \(V_k\). In addition to runValueIteration , implement the following methods for ValueIterationAgent using \(V_k\):

• computeActionFromValues(state) computes the best action according to the value function given by self.values.
• computeQValueFromValues(state, action) returns the Q-value of the (state, action) pair given by the value function given by self.values .

These quantities are all displayed in the GUI: values are numbers in squares, Q-values are numbers in square quarters, and policies are arrows out from each square.

Important: Use the “batch” version of value iteration where each vector \(V_k\) is computed from a fixed vector \(V_{k−1}\) (like in lecture), not the “online” version where one single weight vector is updated in place. This means that when a state’s value is updated in iteration \(k\) based on the values of its successor states, the successor state values used in the value update computation should be those from iteration \(k−1\) (even if some of the successor states had already been updated in iteration \(k\)). The difference is discussed in Sutton & Barto in Chapter 4.1 on page 91.

Note : A policy synthesized from values of depth \(k\) (which reflect the next \(k\) rewards) will actually reflect the next \(k+1\) rewards (i.e. you return \(\pi_{k+1}\)). Similarly, the Q-values will also reflect one more reward than the values (i.e. you return \(Q_{k+1}\)).

You should return the synthesized policy \(\pi_{k+1}\).

Hint : You may optionally use the util.Counter class in util.py , which is a dictionary with a default value of zero. However, be careful with argMax : the actual argmax you want may be a key not in the counter!

Note : Make sure to handle the case when a state has no available actions in an MDP (think about what this means for future rewards).

To test your implementation, run the autograder:

The following command loads your ValueIterationAgent , which will compute a policy and execute it 10 times. Press a key to cycle through values, Q-values, and the simulation. You should find that the value of the start state ( V(start) , which you can read off of the GUI) and the empirical resulting average reward (printed after the 10 rounds of execution finish) are quite close.

Hint : On the default BookGrid , running value iteration for 5 iterations should give you this output:

Grading : Your value iteration agent will be graded on a new grid. We will check your values, Q-values, and policies after fixed numbers of iterations and at convergence (e.g. after 100 iterations).

Consider the DiscountGrid layout, shown below. This grid has two terminal states with positive payoff (in the middle row), a close exit with payoff +1 and a distant exit with payoff +10. The bottom row of the grid consists of terminal states with negative payoff (shown in red); each state in this “cliff” region has payoff -10. The starting state is the yellow square. We distinguish between two types of paths: (1) paths that “risk the cliff” and travel near the bottom row of the grid; these paths are shorter but risk earning a large negative payoff, and are represented by the red arrow in the figure below. (2) paths that “avoid the cliff” and travel along the top edge of the grid. These paths are longer but are less likely to incur huge negative payoffs. These paths are represented by the green arrow in the figure below.

In this question, you will choose settings of the discount, noise, and living reward parameters for this MDP to produce optimal policies of several different types. Your setting of the parameter values for each part should have the property that, if your agent followed its optimal policy without being subject to any noise, it would exhibit the given behavior. If a particular behavior is not achieved for any setting of the parameters, assert that the policy is impossible by returning the string 'NOT POSSIBLE' .

Here are the optimal policy types you should attempt to produce:

• Prefer the close exit (+1), risking the cliff (-10)
• Prefer the close exit (+1), but avoiding the cliff (-10)
• Prefer the distant exit (+10), risking the cliff (-10)
• Prefer the distant exit (+10), avoiding the cliff (-10)
• Avoid both exits and the cliff (so an episode should never terminate)

To see what behavior a set of numbers ends up in, run the following command to see a GUI:

To check your answers, run the autograder:

question2a() through question2e() should each return a 3-item tuple of (discount, noise, living reward) in analysis.py .

Note : You can check your policies in the GUI. For example, using a correct answer to 3(a), the arrow in (0,1) should point east, the arrow in (1,1) should also point east, and the arrow in (2,1) should point north.

Note : On some machines you may not see an arrow. In this case, press a button on the keyboard to switch to qValue display, and mentally calculate the policy by taking the arg max of the available qValues for each state.

Grading : We will check that the desired policy is returned in each case.

Note that your value iteration agent does not actually learn from experience. Rather, it ponders its MDP model to arrive at a complete policy before ever interacting with a real environment. When it does interact with the environment, it simply follows the precomputed policy (e.g. it becomes a reflex agent). This distinction may be subtle in a simulated environment like a Gridword, but it’s very important in the real world, where the real MDP is not available.

You will now write a Q-learning agent, which does very little on construction, but instead learns by trial and error from interactions with the environment through its update(state, action, nextState, reward) method. A stub of a Q-learner is specified in QLearningAgent in qlearningAgents.py , and you can select it with the option '-a q' . For this question, you must implement the update , computeValueFromQValues , getQValue , and computeActionFromQValues methods.

Note : For computeActionFromQValues , you should break ties randomly for better behavior. The random.choice() function will help. In a particular state, actions that your agent hasn’t seen before still have a Q-value, specifically a Q-value of zero, and if all of the actions that your agent has seen before have a negative Q-value, an unseen action may be optimal.

Important : Make sure that in your computeValueFromQValues and computeActionFromQValues functions, you only access Q values by calling getQValue . This abstraction will be useful for question 10 when you override getQValue to use features of state-action pairs rather than state-action pairs directly.

With the Q-learning update in place, you can watch your Q-learner learn under manual control, using the keyboard:

Recall that -k will control the number of episodes your agent gets to learn. Watch how the agent learns about the state it was just in, not the one it moves to, and “leaves learning in its wake.” Hint: to help with debugging, you can turn off noise by using the --noise 0.0 parameter (though this obviously makes Q-learning less interesting). If you manually steer Pacman north and then east along the optimal path for four episodes, you should see the following Q-values:

Grading : We will run your Q-learning agent and check that it learns the same Q-values and policy as our reference implementation when each is presented with the same set of examples. To grade your implementation, run the autograder:

Complete your Q-learning agent by implementing epsilon-greedy action selection in getAction , meaning it chooses random actions an epsilon fraction of the time, and follows its current best Q-values otherwise. Note that choosing a random action may result in choosing the best action - that is, you should not choose a random sub-optimal action, but rather any random legal action.

You can choose an element from a list uniformly at random by calling the random.choice function. You can simulate a binary variable with probability p of success by using util.flipCoin(p) , which returns True with probability p and False with probability 1-p .

After implementing the getAction method, observe the following behavior of the agent in GridWorld (with epsilon = 0.3).

Your final Q-values should resemble those of your value iteration agent, especially along well-traveled paths. However, your average returns will be lower than the Q-values predict because of the random actions and the initial learning phase.

You can also observe the following simulations for different epsilon values. Does that behavior of the agent match what you expect?

With no additional code, you should now be able to run a Q-learning crawler robot:

If this doesn’t work, you’ve probably written some code too specific to the GridWorld problem and you should make it more general to all MDPs.

This will invoke the crawling robot from class using your Q-learner. Play around with the various learning parameters to see how they affect the agent’s policies and actions. Note that the step delay is a parameter of the simulation, whereas the learning rate and epsilon are parameters of your learning algorithm, and the discount factor is a property of the environment.

Time to play some Pacman! Pacman will play games in two phases. In the first phase, training , Pacman will begin to learn about the values of positions and actions. Because it takes a very long time to learn accurate Q-values even for tiny grids, Pacman’s training games run in quiet mode by default, with no GUI (or console) display. Once Pacman’s training is complete, he will enter testing mode. When testing, Pacman’s self.epsilon and self.alpha will be set to 0.0, effectively stopping Q-learning and disabling exploration, in order to allow Pacman to exploit his learned policy. Test games are shown in the GUI by default. Without any code changes you should be able to run Q-learning Pacman for very tiny grids as follows:

Note that PacmanQAgent is already defined for you in terms of the QLearningAgent you’ve already written. PacmanQAgent is only different in that it has default learning parameters that are more effective for the Pacman problem ( epsilon=0.05, alpha=0.2, gamma=0.8 ). You will receive full credit for this question if the command above works without exceptions and your agent wins at least 80% of the time. The autograder will run 100 test games after the 2000 training games.

Hint : If your QLearningAgent works for gridworld.py and crawler.py but does not seem to be learning a good policy for Pacman on smallGrid , it may be because your getAction and/or computeActionFromQValues methods do not in some cases properly consider unseen actions. In particular, because unseen actions have by definition a Q-value of zero, if all of the actions that have been seen have negative Q-values, an unseen action may be optimal. Beware of the argMax function from util.Counter !

To grade your answer, run:

Note : If you want to experiment with learning parameters, you can use the option -a , for example -a epsilon=0.1,alpha=0.3,gamma=0.7 . These values will then be accessible as self.epsilon , self.gamma and self.alpha inside the agent.

Note : While a total of 2010 games will be played, the first 2000 games will not be displayed because of the option -x 2000 , which designates the first 2000 games for training (no output). Thus, you will only see Pacman play the last 10 of these games. The number of training games is also passed to your agent as the option numTraining .

Note : If you want to watch 10 training games to see what’s going on, use the command:

During training, you will see output every 100 games with statistics about how Pacman is faring. Epsilon is positive during training, so Pacman will play poorly even after having learned a good policy: this is because he occasionally makes a random exploratory move into a ghost. As a benchmark, it should take between 1000 and 1400 games before Pacman’s rewards for a 100 episode segment becomes positive, reflecting that he’s started winning more than losing. By the end of training, it should remain positive and be fairly high (between 100 and 350).

Make sure you understand what is happening here: the MDP state is the exact board configuration facing Pacman, with the now complex transitions describing an entire ply of change to that state. The intermediate game configurations in which Pacman has moved but the ghosts have not replied are not MDP states, but are bundled in to the transitions.

Once Pacman is done training, he should win very reliably in test games (at least 90% of the time), since now he is exploiting his learned policy.

However, you will find that training the same agent on the seemingly simple mediumGrid does not work well. In our implementation, Pacman’s average training rewards remain negative throughout training. At test time, he plays badly, probably losing all of his test games. Training will also take a long time, despite its ineffectiveness.

Pacman fails to win on larger layouts because each board configuration is a separate state with separate Q-values. He has no way to generalize that running into a ghost is bad for all positions. Obviously, this approach will not scale.

Implement an approximate Q-learning agent that learns weights for features of states, where many states might share the same features. Write your implementation in ApproximateQAgent class in qlearningAgents.py , which is a subclass of PacmanQAgent .

Note : Approximate Q-learning assumes the existence of a feature function \(f(s,a)\) over state and action pairs, which yields a vector \([f_1(s,a), \dots, f_i(s,a), \dots, f_n(s,a)]\) of feature values. We provide feature functions for you in featureExtractors.py . Feature vectors are util.Counter (like a dictionary) objects containing the non-zero pairs of features and values; all omitted features have value zero.

The approximate Q-function takes the following form:

where each weight \(w_i\) is associated with a particular feature \(f_i(s,a)\). In your code, you should implement the weight vector as a dictionary mapping features (which the feature extractors will return) to weight values. You will update your weight vectors similarly to how you updated Q-values:

Note that the \(\text{difference}\) term is the same as in normal Q-learning, and \(r\) is the experienced reward.

By default, ApproximateQAgent uses the IdentityExtractor , which assigns a single feature to every (state,action) pair. With this feature extractor, your approximate Q-learning agent should work identically to PacmanQAgent . You can test this with the following command:

Important : ApproximateQAgent is a subclass of QLearningAgent , and it therefore shares several methods like getAction . Make sure that your methods in QLearningAgent call getQValue instead of accessing Q-values directly, so that when you override getQValue in your approximate agent, the new approximate q-values are used to compute actions.

Once you’re confident that your approximate learner works correctly with the identity features, run your approximate Q-learning agent with our custom feature extractor, which can learn to win with ease:

Even much larger layouts should be no problem for your ApproximateQAgent ( warning : this may take a few minutes to train):

If you have no errors, your approximate Q-learning agent should win almost every time with these simple features, even with only 50 training games.

Grading : We will run your approximate Q-learning agent and check that it learns the same Q-values and feature weights as our reference implementation when each is presented with the same set of examples. To grade your implementation, run the autograder:

Congratulations! You have a learning Pacman agent!

In order to submit your project, run python submission_autograder.py and submit the generated token file reinforcement.token to the Project 3 assignment on Gradescope.

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Creating a Corporate Social Responsibility Program with Real Impact

• Emilio Marti,
• David Risi,
• Eva Schlindwein,
• Andromachi Athanasopoulou

Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

• EM Emilio Marti is an associate professor at the Rotterdam School of Management, Erasmus University. His research focuses on corporate sustainability with a specific focus on sustainable investing.
• DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
• ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
• AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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