Instructor: Faruk Polat

Catalog Data: Multigent Systems (3-0) 3

 Concurrency and distribution in AI. Agents: micro and macro views.   Agent communication languages. Rational agency: economic/game theoretic,   logical. BDI architecture. Multi-agent real-time search.   Reinforcement learning. Multi-agent learning. Opponent modeling.

Prerequisites: Consent of the instructor. 

1.  Textbook(s): None
   
   
2.  Reference(s):
• G.Weiss, Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 2000.
• Y.Shoham and K. Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic and Logical Foundations, Cambridge University Press, 2009.
• M.Wooldridge, An Int. to MultiAgent Systems, John Wiley & Sons, 2002.
• Selected classics in Autonomous Agents and Multiagents Systems Journal, Artificial Intelligence Journal, Journal of Artificial Intelligence Research and several conferences including AAMAS, IJCAI, AAAI..
3. Goals: To develop the theory and practice of multi-agent systems.
   
   
4.  Course Outline:

Topics

• Abstract architectures for intelligent agents
  

• Introduction to autonomous agents and multi-agent systems
• Purely reactive agents
• Perception
• Agents with state

• Concrete architectures for intelligent agents

• Logic based architectures
• Reactive architectures
• Belief, desire, intention architectures
• Layered architectures

• Agent Communication

• Speech Act Theory
• Agent Communication Languages: KQML and ACL

• Real-Time Search

• Real-time A* (RTA*),
• LRTA (Learning Real-Time A*),
• Moving Target Search (MTS)

• Multiagent Search

• Multi-agent Real-Time A* (MARTA*)
• Organizational strategies

• Rational Agents

• Agents as rational decision makers
• Observable worlds and Markov Property
• Stochastic transitions and utilities
• Game theory

• Agent Interaction

• Strategic games
• Iterated elimination of strictly dominated actions
• Nash equilibrium
• Mechanism design, negotiation

• Project Progress Presentation
  
• Logic-based agency

• Modal logic, dynamic logic, temporal logic
• BDI architectures

• Multiagent Learning 

• Reinforcement Learning
• TD(λ), Q-learning
• Learning in Markov Games
• NASH Q-Learning, Mini-Max Q-Learning
• Action Estimation Algorithm (ACE) and Action Group Estimation Algorithm (AGE)
• Research Paper Presentations
  

 Important Dates

• Project proposal report. 
• Project progress presentations 
• Project progress report. 
• Project Report and Demo : 

Evaluation:

• Course Project:  (progress report 15%, final report+demo 35%)
• Homeworks:  Programming Assignments. 25%
• Research Paper Presentations (15%): technical presentation.
• Active participation in the presentations (10%):

Homeworks: Individual programming tasks

Grades: