Artificial Intelligence Review 6, 35--66. 1992. © 1992 Kluwer Academic Publishers. Printed in the Netherlands. Trends in Distributed Artificial Intelligence B, CHAIB-DRAA and B. MOULIN D~partement d'Informatique, Facult~ des Sciences Universit~ Laval, Sainte-Foy, Quebec, Canada G1K 7P4. R. MANDIAU and P. MILLOT Laboratoire d'Automatique Industrielle et Humaine, URA CNRS No. 1118 Universit~ de Valenciennes. Mont Houy 59326 Valenciennes Cedex France. "... the whole (socie~) is prior to the part (the individual), not the part to the whole: and the part is explained in terms of the whole, not the whole in terms of the parts. "' (Mead, 1934) Abstract. Distributed artificial intelligence (DAI) is a subfield of artificial intel- ligence 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. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a dis- tributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed. Key Words: distributed artificial intelligence, reasoning about others, organization, cooperation, communication, cohesion, coordination, negotiation. 1. INTRODUCTION For many years, research in artificial intelligence has been mostly oriented towards single agent environments. In this approach, an agent evolves in a static environment and its main activities are: gathering information, planning, and ex- ecuting some plan to achieve its goals. This approach has been proven insufficient due to the inevitable presence of a number of agents in the real world. In fact, we must plan the activities of agent while keeping in mind the other agents' activities that can either help or hinder him. Hence, the scientific community is interested in DAI with the goal of studying such types of interac- tion. DAI, a relatively new but growing body of research in AI, is based on a different model than traditional artificial intelligence. Indeed, the latter stipu-