AI-402-paul.wilmoth-2026-08-20-01-13-52
Below is a syllabus template that includes WSU's required syllabus elements. Please complete all items highlighted in yellow.
Title of Course [Multiagent Systems]
Prefix and Number [AI 402]
Semester and Year [tbd]
Number of Credit Hours [3]
Prerequisites [AI 240 Foundations of Artificial Intelligence ]
Course Details
Day and Time: [tbd]
Meeting Location: [tbd]
Instructor Contact Information
Instructor Name: [tbd]
Instructor Contact Information: [office location, phone, email] [tbd]
Instructor Office Hours: [click here for best practices] [tbd]
TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
TA Office Hours: [click here for best practices] [tbd]
Course Description
[The proposed B.S. in Artificial Intelligence program needs an upper division elective devoted to AI systems in which multiple autonomous agents interact, cooperate, compete, communicate, and make decisions in shared environments. This course develops multiagent foundations not treated in depth elsewhere in the proposed curriculum, including the challenges of moving from single agent to multiagent systems, formal frameworks for specifying multiagent problems, game theoretic foundations, coordination and communication, multiagent learning, and algorithmic methods for cooperative, competitive, and mixed motive settings. The course connects these foundations to applications in security, healthcare, conservation, resource allocation, and other sociotechnical systems (e.g., water management).
The course is distinct from courses that focus primarily on single agent AI, machine learning, or reinforcement learning. In contrast, this course emphasizes strategic interactions among multiple decision making agents and the resulting challenges of coordination, competition, incentives, decentralization, and emergent behavior. It also connects classical multiagent systems and game theoretic foundations to modern agentic AI, where multiple AI agents collaborate, communicate, negotiate, use tools, and coordinate to solve complex tasks. It supports the AI Foundations and AI Applications and Society pillars and prepares students for capstone projects, advanced studies, and professional work involving multiagent systems, agentic AI, and AI enabled sociotechnical systems.]
Course Materials
Books: [No required textbook is planned for this course.
Other Materials: Required course materials will include instructor-prepared lecture notes, slides, programming examples, case studies, research papers, tutorials, and selected open-access readings on multi-agent systems, game theory, agentic AI, coordination, negotiation, task allocation, and responsible deployment of multi-agent systems.
Fees: [None
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Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
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1. Explain and formulate multiagent AI problems, including the challenges of cooperation, competition, communication, decentralization, and strategic behavior, using appropriate formal frameworks (1a, 1b, 1c, 6a). |
Lectures, guided readings, formulation exercises, payoff matrices, and small environment models. |
Studio 1; Checkpoints 1 to 3 and 7; project proposal. |
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2. Apply game theoretic concepts and algorithmic methods to analyze and solve cooperative, competitive, and mixed motive multiagent problems (1d, 1e, 6a, 6c). |
Worked game theory examples, strategy calculations, programming studios, and simulation comparisons. |
Studios 1 and 2; Checkpoints 3 to 5 and 8; project analysis. |
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3. Design, implement, and evaluate multiagent systems for applications in security, healthcare, conservation, resource allocation, and other sociotechnical domains (22a, 2c, 2e, 2g, 6a, 6b, 6f). |
Application case studies, task allocation and learning studios, and project development. |
Studios 3 and 4; project baseline, progress report, and final implementation. |
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4. Design and evaluate agentic AI systems in which multiple AI agents collaborate, communicate, specialize, negotiate, coordinate, and use tools to accomplish complex tasks (2a, 2c, 2e, 6a, 6b, 6f). |
Agentic AI studio, message and tool design exercises, workflow comparisons, and failure tests. |
Studio 5; Checkpoint 10; workflow and safety evaluation. |
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5. Analyze and communicate the performance, robustness, reliability, safety, limitations, and societal implications of multiagent and agentic AI systems (3a, 3b, 3c, 3d, 3e, 4a, 4f, 6b, 6c, 6e). |
Evaluation exercises, responsible AI cases, experimental reports, demonstrations, and individual reflection. |
Studio evaluations; project report, presentation, and individual contribution statement. |
| Dates | Lesson Topic | Assignment | Assessment |
|---|---|---|---|
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Week 1 |
Agents, environments, utilities, and interaction. Cooperation, competition, and comparison with a single agent. |
Set up the Python notebook. Checkpoint 1 opens. |
Setup check and discussion; no graded deadline. |
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Week 2 |
Normal form games: actions, payoff matrices, best responses, dominance, and social welfare. |
Studio 1 and Checkpoint 2 open. Model a small resource sharing game. |
Checkpoint 1. |
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Week 3 |
Pure and mixed strategies, Nash equilibrium in small games, and minimax reasoning in zero sum games. |
Worked strategy calculations. Checkpoint 3 opens. |
Studio 1; Checkpoint 2. |
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Week 4 |
Extensive form games, backward induction, repeated interaction, and incentives for cooperation. |
Studio 2 and Checkpoint 4 open. Analyze and simulate simple strategies. |
Checkpoint 3. |
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Week 5 |
Negotiation, auctions, voting, and introductory mechanism design. Efficiency, fairness, and truthful reporting. |
Team formation and project ideas. Checkpoint 5 opens. |
Studio 2; Checkpoint 4. |
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Week 6 |
Coordination and task allocation. Centralized versus distributed decisions; simple distributed constraint examples. |
Studio 3 opens. Draft agent roles, objectives, and evaluation criteria. |
Checkpoint 5; project proposal. |
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Week 7 |
Communication protocols, local observations, shared information, and communication cost. |
Message protocol exercise. Checkpoint 6 opens. |
Formative studio and project feedback. |
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Week 8 |
Markov decision processes and basic reinforcement learning. Stochastic games and decentralized partially observable models. |
Guided state, action, reward, and observation exercises. Checkpoint 7 opens. |
Studio 3; Checkpoint 6. |
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Week 9 |
Learning in games and independent tabular Q learning. Exploration and changing behavior of other learners. |
Studio 4 and Checkpoint 8 open. Compare learning with a fixed policy. |
Checkpoint 7; project baseline. |
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Week 10 |
Cooperative learning, shared rewards, and credit assignment. Conceptual introduction to centralized training with decentralized execution. |
Compare independent and coordinated learners in a small environment. Checkpoint 9 opens. |
Checkpoint 8. |
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Week 11 |
Applications in security, conservation, healthcare resource allocation, and water management. Tradeoffs and modeling limits. |
Case study and experiment interpretation workshop using synthetic or public data. |
Studio 4; Checkpoint 9. |
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Week 12 |
Agentic AI: planners, workers, verifiers, role specialization, messages, negotiation, and approved tool use. |
Studio 5 and Checkpoint 10 open. Design a small collaborative workflow. |
Project progress report and prototype. |
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Week 13 |
Evaluation of agentic systems: a single agent baseline, coordination benefits, communication cost, verification, and failure cases. |
Test a workflow with one component removed. Review tool boundaries and human review. |
Checkpoint 10. |
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Week 14 |
Robustness, reliability, safety, fairness, and responsible deployment. Project testing and reproducibility. |
Project clinic, failure analysis, and presentation rehearsal. |
Studio 5. |
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Week 15 |
Project demonstrations, presentations, synthesis, and discussion of system limitations. |
Present the system and explain individual contributions. Prepare final artifacts. |
Project presentation and demonstration. |
Expectations for Student Effort
[Describe how much time students should expect to invest in the course each week. Graduate courses should state: "For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class." Note that Global campus courses will automatically include credit hour equivalents in the syllabus.] [Students should plan for approximately nine hours of work per teaching week: three hours of class activity and at least six hours outside class. Outside work includes reading, concept checks, programming, experiment analysis, and project meetings. Time spent on each activity will vary across the semester; teams should work steadily rather than postpone implementation until the final weeks.
Studios provide starter code and bounded tasks that can be completed on ordinary computers. Students are expected to attend prepared, ask questions when a concept is unclear, test their code, and explain the results they submit. No publication or original research result is expected.]
Grading [add more lines if necessary]
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
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Guided programming studios |
Five studios, 100 points each. The highest four scores count: 400 possible counted points. |
40% |
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Individual checkpoints |
Ten checkpoints, 10 points each. The highest eight scores count: 80 possible counted points. |
15% |
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Team project and individual reflection |
100 points across the milestones listed on the next page. All project milestones count. |
45% |
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Total |
Category scores are normalized before weighting. |
100% |
| Grade | Percent | Grade | Percent |
|---|---|---|---|
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A |
93 to 100 |
C |
73 to 76 |
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A- |
90 to 92 |
C- |
70 to 72 |
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B+ |
87 to 89 |
D+ |
67 to 69 |
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B |
83 to 86 |
D |
60 to 66 |
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B- |
80 to 82 |
F |
Below 60 |
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C+ |
77 to 79 |
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[Provide information about how grades will be rounded (eg, if 89% earns a B+ and 90% earns an A-, what grade is given to a student with an 89.5?]
Attendance and Make-Up Policy
[Provide details on how attendance affects final course grades. Indicate whether and how missed exams, laboratory sessions, etc. can be made up. Sample attendance statement: “Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.” ]
[Regular attendance and participation are expected, but attendance is not separately graded. After an absence, review the materials and contact the instructor promptly when missed work requires an arrangement.]
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-[Follow WSU academic integrity expectations and each assessment's rules on assistance. General discussion is encouraged, but individual submissions must reflect your own understanding. Project teams may share work within their group. Cite external code, ideas, and data; do not copy submissions or fabricate results.]
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.