Showing papers on "Multi-agent system published in 1989"

Cooperating agents: a unified theory of communication and social structure

Abstract: We aim at providing a general theoretical framework for designing agents with a communicative and social competence. Thereby, we develop the foundations for the design of systems of agents that behave as a social unit or group. A unified theory of communication, cooperation, and social structure is presented. First, a theory of the cognitive states, the information and intentional states, of an agent is given. A theory of communication is developed that gives a formal account of how messages affect the intentions or plans of an agent. The theory of intentions is used to define the concepts of social role and social structure. The unified theory of communication, intention, and social structures is used to develop a theory of social cooperation for multiagent systems. This fulfills a necessary condition for the design of complex agents that cooperate as a group. We apply the theories with an analysis of two examples: the contract net protocol and a Wittgensteinian language game.

From vision to diagnosis a multi-agent system in biomedicine

Abstract: KISS (Knowledge-based Image Segmentation System) and KIDS (Knowledge-based Image Diagnosis System), which are multiagent systems devoted to the analysis and interpretation of biomedical images, are discussed They allow a high-level modeling of the expertise of the computer vision scientist and pathologist, using a distributed approach involving a networked set of agents dedicated to the handling of specific subtasks and communicating by message-passing The systems are currently under development on an Apollo workstation using the Flavor system and the MAPS (Multi-Agent Problem Solver) environment >

A knowledge-level analysis of informing

Abstract: Publisher Summary This chapter reviews how informing modifies the knowledge of an agent and the manifestation in terms of its problem solving performance Robust intelligent systems must work in many differing situations When the environment or computational resources change, a system must modify its behavior accordingly To make matters even worse, information available to an agent is often incomplete, especially in complex domains Designing intelligent agents that are able to adapt to their environment, to handle partial information, and to learn from experience has been a challenging task for AI researchers Then it becomes important to build informable agents that are able to accept declarative information at runtime and put that information to use without the intervention of a human programmer Intuitively, informing increases the amount of knowledge held by an agent Informing adds knowledge to an agent For an agent to be truly informable, it has to share the same conceptual primitives as the informing agent Control knowledge may appear as various forms of procedural hints to the agents, such as prescription of specific actions, elimination of specific actions, constraints on actions, and preference among actions etc Even though procedural hints are generally not in the deductive closure of the agent's body of domain knowledge, they do not necessarily enable the agents to solve more problems With more knowledge about the problem-solving process, which may help cut down combinatoric explosions, an agent is able to solve problems more efficiently Thus informing is a desirable feature for intelligent adaptive agents Furthermore, Declarative representations such as predicate calculus are expressive enough to cope with incomplete and incremental information