This course introduces fundamental principles and techniques of Distributed Artificial Intelligence (DAI), as well as the usage of such techniques for creating applications in distributed computing environments.Central to the course are the concepts of "Distributed decision making, control and optimization". The main goal of the course is to give students knowledge about basic methods and techniques which, in particular, can be applied to: Solving problems with decentralized control, Providing solutions to inherently distributed problems and Providing solutions to problems where expertise is distributed.

Here you will find administrative information for the Winter 1397.

Instructor: Dr. Mohsen Afsharchi, afsharchi at znu.ac.ir

Lectures: 8-10 Mon and 14-16 Wed

Prerequisites: Clear understanding of probability, basic game theory and reinforcement learning.

The main book for:

1. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations.  By Yoav Shoham and Kevin Leyhon-Brown, 2008.

Lecture materials will be drawn from textbooks, as well as from some of the recent online materials.

1. Game Theory Recap (Solution Concepts) (Extensive Form Games)
   Reading Materials:
   A Game-Theoretic Approach to Energy Trading in the Smart Grid
   Design of a Multi-unit Double Auction E-Market
2. Game Theory (Imperfect Information Games) (Behavioral vs Mixed Strategies) (Repeated Games)
   Reading Materials:
   Game Theory-Based Opponent Modeling in Large Imperfect-Information Games
3. Bayesian Games (PDF)
   Reading Materials:
   Bayesian Games: Games with Incomplete Information
   A Bayesian Game Approach for Intrusion Detection in Wireless Ad Hoc Networks
4. AllocationScarce Resources (PDF)
   Reading Materials:
   Multi-Robot Auctions for Allocation of Tasks with Temporal Constraints
   Multi-Agent Pathfinding as a Combinatorial Auction
5. Multi-agent Reinforcement Learning (Nash QLearning)
   Reading Materials:
   Markov Games as a Framework for Multi-agent Reinforcement Learning
   Multi-agent Reinforcement Learning Theoretical Framework and an Algorithm
   Multi-agent Learning Using a Variable Learning Rate
   Frequency Adjusted Multi-agent Q-learning
6. Distributed Constraint Satisfaction (PPT) (ABT Examle)
   Reading Materials:
   Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
   Asynchronous Forward-Checking for DisCSPs
   Distributed Constraint Satisfaction in a Wireless Sensor Tracking System
7. Distributed Constraint Optimization (PPT)
   Reading Materials:
   ADOPT: Asynchronous Distributed Constraint Optimization with Quality Guarantees
   Distributed constraint optimization for teams of mobile sensing agents
   USC Distributed Constraint Optimization Problem (DCOP) Repository

 

 

 

Students will be evaluated in this class through a series of exams, an in-class presentation and a semester project. The semester project will involve implementing a non-trivial  program that has been designed using knowledge and techniques covered in class. In-class students will work in teams to work on the project.