The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the exploration-exploitation trade-off, but such methods typically assume a fully observable environments. The few Bayesian RL methods that are applicable in partially observable domains, such as the Bayes-Adaptive POMDP (BA-POMDP), scale poorly. To address this issue, we introduce the Factored BA-POMDP model (FBA-POMDP), a framework that is able to learn a compact model of the dynamics by exploiting the underlying structure of a POMDP. The FBA-POMDP framework casts the problem as a planning task, for which we adapt the Monte-Carlo Tree Search planning algorithm and develop a belief tracking method to approximate the joint posterior over the state and model variables. Our empirical results show that this method outperforms a number of BRL baselines and is able to learn efficiently when the factorization is known, as well as learn both the factorization and the model parameters simultaneously.

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Bayesian Reinforcement Learning in Factored POMDPs

Agent software is a rapidly developing area of research. However, the overuse of the word ‘agent’ has tended to mask the fact that, in reality, there is a truly heterogeneous body of research being carried out under this banner. This overview paper presents a typology of agents. Next, it places agents in context, defines them and then goes on, inter alia, to overview critically the rationales, hypotheses, goals, challenges and state-of-the-art demonstrators of the various agent types in our typology. Hence, it attempts to make explicit much of what is usually implicit in the agents literature. It also proceeds to overview some other general issues which pertain to all the types of agents in the typology. This paper largely reviews software agents, and it also contains some strong opinions that are not necessarily widely accepted by the agent community.

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Software agents: an overview

This second editionsystematically introduces the notion of ontologies to the non-expert reader and demonstrates in detail how to apply this conceptual framework for improved intranet retrieval of corporate information and knowledge and for enhanced Internet-based electronic commerce He also describes ontology languages (XML, RDF, and OWL) and ontology tools, and the application of ontologies In addition to structural improvements, the second edition covers recent developments relating to the Semantic Web, and emerging web-based standard languages

Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce

In this paper we introduce Challenger, a multiagent system that performs completely distributed resource allocation. Challenger consists of agents which individually manage local resources; these agents communicate with one another to share their resources (in this particular instance, CPU time) in an attempt to more e ciently utilize them. By endowing the agents with relatively simple behaviors which rely on only locally available information, desirable global system objectives can be obtained, such as minimization of mean job ow time. Challenger is similar to other market-based control systems in that the agents act as buyers and sellers in a marketplace, always trying to maximize their own utility. The results of several simulations of Challenger performing CPU load balancing in a network of computers are presented. The main contribution of this research is the addition of learning to the agents, which allows Challenger to perform better under a wider range of conditions than other systems for distributed processor allocation, such as Malone's Enterprise [Mal88].

Challenger: a multi-agent system for distributed resource allocation

Software agents help people with time-consuming activities. One relatively unexplored area of application is that of agents that buy and sell on behalf of users. We recently conducted a real-life experiment in creating an agent marketplace, using a slighly modified version of the Kasbah system. Approximately 200 participants intensively interacted with the system over a one-day (six-hour) period. This paper describes the set-up of the experiment, the architecture of the electronic market and the behaviours of the agents. We discuss the rationale behind the design decisions and analyze the results obtained. We conclude with a discussion of current experiments involving thousands of users interacting with the agent marketplace over a long period of time, and speculate on the long-range impact of this technology upon society and the economy.

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A Real-Life Experiment in Creating an Agent Marketplace

While there are many Web services which help users find things to buy, we know of none which actually try to automate the process of buying and selling. Kasbah is a system where users create autonomous agents to buy and sell goods on their behalf. In this paper, we describe how Kasbah works. We also discuss the implementation of a simple proof-of-concept prototype.

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Kasbah: An Agent Marketplace for Buying and Selling Goods

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Challenger: a multi-agent system for distributed resource allocation

A Real-Life Experiment in Creating an Agent Marketplace

Kasbah: An Agent Marketplace for Buying and Selling Goods