Most artificial intelligence research investigates intelligent behavior for a single agent--solving problems heuristically, understanding natural language, and so on. Distributed Artificial Intelligence (DAI) is concerned with coordinated intelligent behavior: intelligent agents coordinating their knowledge, skills, and plans to act or solve problems, working toward a single goal, or toward separate, individual goals that interact. DAI provides intellectual insights about organization, interaction, and problem solving among intelligent agents. This comprehensive collection of articles shows the breadth and depth of DAI research. The selected information is relevant to emerging DAI technologies as well as to practical problems in artificial intelligence, distributed computing systems, and human-computer interaction. 

"Readings in Distributed Artificial Intelligence" proposes a framework for understanding the problems and possibilities of DAI. It divides the study into three realms: the natural systems approach (emulating strategies and representations people use to coordinate their activities), the engineering/science perspective (building automated, coordinated problem solvers for specific applications), and a third, hybrid approach that is useful in analyzing and developing mixed collections of machines and human agents working together. 

The editors introduce the volume with an important survey of the motivations, research, and results of work in DAI. This historical and conceptual overview combines with chapter introductions to guide the reader through this fascinating field. A unique and extensive bibliography is also provided.

Readings in Distributed Artificial Intelligence

Distributed Artificial Intelligence is concerned with problem solving in which groups solve tasks. In this paper we describe strategies of cooperation that groups require to solve shared tasks effectively. We discuss such strategies in the context of a specific group problem solving application: collision avoidance in air traffic control. Experimental findings with four distinct air-traffic control systems, each implementing a different cooperative strategy, are mentioned.

Strategies of cooperation in distributed problem solving

Distributed problem-solving is defined as a subfield of artificial intelligence that deals with the interaction of groups of intelligent agents attempting to cooperate to solve problems. A taxonomy of distributed artificial intelligence systems is presented, based on the communication and control methodologies used by their constituent agents, along with the theoretical foundations which underly them. Control in distributed problem-solvers is characterized by cooperation, organization, and dynamics. Communications are specified through paradigms, content, and protocols. Several prototypical systems in areas such as natural language processing and medical diagnosis are briefly discussed, along with more mature systems in applications such as air-traffic control, vehicle monitoring, and manufacturing systems.

Distributed problem-solving techniques: A survey

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This paper lays part of the groundwork for a domain theory of negotiation, that is, a way of classifying interactions so that it is clear, given a domain, which negotiation mechanisms and strategies are appropriate. We define State Oriented Domains, a general category of interaction. Necessary and sufficient conditions for cooperation are outlined. We use the notion of worth in an altered definition of utility, thus enabling agreements in a wider class of joint-goal reachable situations. An approach is offered for conflict resolution, and it is shown that even in a conflict situation, partial cooperative steps can be taken by interacting agents (that is, agents in fundamental conflict might still agree to cooperate up to a certain point).

A Unified Negotiation Protocol (UNP) is developed that can be used in all types of encounters. It is shown that in certain borderline cooperative situations, a partial cooperative agreement (i.e., one that does not achieve all agents' goals) might be preferred by all agents, even though there exists a rational agreement that would achieve all their goals.

Finally, we analyze cases where agents have incomplete information on the goals and worth of other agents. First we consider the case where agents' goals are private information, and we analyze what goal declaration strategies the agents might adopt to increase their utility. Then, we consider the situation where the agents' goals (and therefore stand-alone costs) are common knowledge, but the worth they attach to their goals is private information. We introduce two mechanisms, one "strict," the other "tolerant," and analyze their affects on the stability and efficiency of negotiation outcomes.

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Mechanisms for automated negotiation in state oriented domains

Situations in which several agents must interact to achieve goals present difficulties of coordination and cooperation not found in single-agent problem solving contexts. Techniques for coordination and cooperation required in group problem solving are not well understood because most AI models deal with cases in which problems are solved by a single agent. In this paper we present a framework for distributed problem solving that describes some of the expertise an agent working in an multi-agent environment must have. An application of the framework to the domain of air-traffic control is discussed. Here each aircraft is viewed as an agent that must cooperate with others to achieve a conflict-free plan.

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Randy Steeb

Papers

A framework for distributed problem solving