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.

In real-world planning problems, we must reason not only about our own goals, but about the goals of other agents with which we may interact. Often these agents' goals are neither completely aligned with our own nor directly opposed to them. Instead there are opportunities for cooperation: by joining forces, the agents can all achieve higher utility than they could separately. But, in order to cooperate, the agents must negotiate a mutually acceptable plan from among the many possible ones, and each agent must trust that the others will follow their parts of the deal. Research in multi-agent planning has often avoided the problem of making sure that all agents have an incentive to follow a proposed joint plan. On the other hand, while game theoretic algorithms handle incentives correctly, they often don't scale to large planning problems. In this paper we attempt to bridge the gap between these two lines of research: we present an efficient game-theoretic approximate planning algorithm, along with a negotiation protocol which encourages agents to compute and agree on joint plans that are fair and optimal in a sense defined below. We demonstrate our algorithm and protocol on two simple robotic planning problems.

In real-world planning problems, we must reason not only about our own goals, but about the goals of other agents with which we may interact. Often these agents' goals are neither completely aligned with our own nor directly opposed to them. Instead there are opportunities for cooperation: by joining forces, the agents can all achieve higher utility than they could separately. But, in order to cooperate, the agents must negotiate a mutually acceptable plan from among the many possible ones, and each agent must trust that the others will follow their parts of the deal. Research in multi-agent planning has often avoided the problem of making sure that all agents have an incentive to follow a proposed joint plan. On the other hand, while game theoretic algorithms handle incentives correctly, they often don't scale to large planning problems. In this paper we attempt to bridge the gap between these two lines of research: we present an efficient game-theoretic approximate planning algorithm, along with a negotiation protocol which encourages agents to compute and agree on joint plans that are fair and optimal in a sense defined below. We demonstrate our algorithm and protocol on two simple robotic planning problems.

In real-world planning problems, we must reason not only about our own goals, but about the goals of other agents with which we may interact. Often these agents' goals are neither completely aligned with our own nor directly opposed to them. Instead there are opportunities for cooperation: by joining forces, the agents can all achieve higher utility than they could separately. But, in order to cooperate, the agents must negotiate a mutually acceptableplan from among the many possible ones, and each agent must trust that the others will follow their parts of the deal. Research in multi-agent planning has often avoided the problem of making sure that all agents have an incentive to follow a proposed joint plan. On the other hand, while game theoretic algorithms handle incentives correctly, they often don'tscale to large planning problems. In this paper we attempt to bridge the gap between these two lines of research: we present an efficient game-theoretic approximate planning algorithm, along with a negotiation protocol which encourages agents to compute and agree on joint plans that are fair and optimal in a sense defined below. We demonstrate our algorithm andprotocol on two simple robotic planning problems.

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.

The newly emerging field of machine ethics (Anderson and Anderson 2006) is concerned with adding an ethical dimension to machines. Unlike computer ethics -- which has traditionally focused on ethical issues surrounding humans' use of machines -- machine ethics is concerned with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. In this article we discuss the importance of machine ethics, the need for machines that represent ethical principles explicitly, and the challenges facing those working on machine ethics. We also give an example of current research in the field that shows that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of correct ethical judgments and use that principle to guide its own behavior.

Horvitz noted two emerging trends at the conference and in the AI field. Second is the work in scaling AI to be more integrative. Instead of the ongoing great successes of AI researches on "wedges" of AI expertise and reasoning, there's increasing work on delivering more depth and breadth of capabilities such as sensing, learning, and reasoning. "This is very hard," notes Horvitz, "(but already) I see bits and pieces here and there." Game Playing Competition, the Poker AAI's Twenty-second Conference (AAAI-07) continued a longstanding the 1,025 attendees to choose Competition, and the Human Versus tradition of excellence.

Multiagent Systems" was delivered by Mark W. Spong (University of Illinois (ICINCO 2007) "Toward Human-Machine Cooperation" was delivered by Patrick Millot (Université de Valenciennes, France), who is a renowned professor in Europe. ICINCO received 435 paper submissions, not including workshops, from more than 50 countries, in all continents. Sciences et Techniques de l'Ingénieur are researchers in one of the ICIN-and Robotics (ICINCO 2007) was The conference was also held in cooperation selected for poster presentation. The volume tracks: "Intelligent Control Systems will be published by Springer-Verlag. Autònoma de Barcelona, as a major international forum The two satellite workshops were Spain) and Janan Zaytoon (Centre de to debate technical and scientific the Third International Workshop on Recherche en Sciences et Technologies advances presented by researchers and Multiagent Robotic Systems (MARS de l'Information et de la Communication, developers both from academe and 2007), chaired by Joaquim Filipe, and Universite de Reims Champagne-industry.

This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space.

Marvin Lee Minsky, a founder of the field of artificial intelligence and professor at MIT, celebrated his 80th birthday on August 9, 2007. This article seizes an opportune time to honor Marvin and his contributions and influence in artificial intelligence, science, and beyond. The article provides readers with some personal insights of Minsky from Danny Hillis, John McCarthy, Tom Mitchell, Erik Mueller, Doug Riecken, Aaron Sloman, and Patrick Henry Winston -- all members of the AI community that Minsky helped to found. The article continues with a brief resume of Minsky's research, which spans an enormous range of fields. It concludes with a short biographical account of Minsky's personal history.