Trends in Cooperative Distributed Problem Solving

AI Magazine

Distributed problem solving is a subfield within multiagent systems, where agents are assumed to be part of a team and collaborate with each other to reach a common goal. In this article, we illustrate the motivations for distributed problem solving and provide an overview of two distributed problem solving models, namely distributed constraint satisfaction problems (DCSPs) and distributed constraint optimization problems (DCOPs), and some of their algorithms.

1992

Abstract Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today.

IEEE Transactions on Systems, Man, and Cybernetics, 1991

Distributed Artificial Intelligence, 1987

Distributed Artificial Intelligence, 2000

2008

Abstract. Distributed problem solving involves the collective effort of multiple problems solvers to combine their knowledge, information, and capabilities so as to develop solutions to problems that each could not have solved as well (if at all) alone. The challenge in distributed problem solving is thus in marshalling the distributed capabilities in the right ways so that the problem solving activities of each agent complement the activities of the others, so as to lead efficiently to effective solutions. Thus, while working together leads to distributed problem solving, there is also the distributed problem of how to work together that must be solved. We consider that problem to be a distributed planning problem, where each agent must formulate plans for what it will do that take into account (sufficiently well) the plans of other agents. In this paper, we characterize the variations of distributed problem solving and distributed planning, and summarize some of the basic techniqu...

1985

ABSTRACT Globally coherent behavior is the holy grail of distributed problem solving network research. Obtaining coherent network activity without sacrificing node autonomy and network flexibility places severe demands on the local control component of each node. We introduce new mechanisms that allow a node to compute an abstracted, high-level description of its local state which it then uses to formulate multi-step plans.