A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

Abstract

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.

Cooperation via sharing of probabilistic information

“…It will represent a sort of communication protocol studied and used in connection with closely related multi-agent systems (MAS, Vlassis, 2003).…”

“…The requirement 4 can be generally met only via a distributed DM. In spite of the numerous attempts, e.g., Tumer (1999, 2001), Vlassis (2003), Yiming et al (2003), Stirling (2004), etc., there is no systematic, widely accepted, solution.…”

Multi-Agent Reinforcement Learning: A Survey

“…-Coordination graphs simplify coordination when the global Q-function can be additively decomposed in local Q-functions which only depend on the actions of a subset of agents [18], [19]. -Communication is used to negotiate action choices, either alone or in combination with the above techniques, as in e.g., [20], [21].…”

Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy

“…A multi-agent system (MAS) is comprised of a collection of autonomous and intelligent agents that interact with each other in an environment to optimize a performance measure (Vlassis, 2007). Multi-agent systems are applied in a wide variety of domains including distributed control, resource management, robotic teams, collaborative decision support systems, data mining, and are useful in the modeling, analysis and design of systems where control is distributed among several autonomous decision makers (Goel, Kumar, & Srinivasan, 2008;Parunak, 2000;Vlassis, 2007).…”

“…Multi-agent systems are applied in a wide variety of domains including distributed control, resource management, robotic teams, collaborative decision support systems, data mining, and are useful in the modeling, analysis and design of systems where control is distributed among several autonomous decision makers (Goel, Kumar, & Srinivasan, 2008;Parunak, 2000;Vlassis, 2007).…”