Various researchers in Distributed Artificial Intelligence (DAI) have suggested that it would be worthwhile to isolate "aspects of cooperative behavior," general rules that would cause agents to act in ways conducive to cooperation. The hypothesis is that when agents act in certain ways (e.g., share information, act in predictable ways, defer globally constraining choices), it will be easier for them to carry out effective joint action. Another kind of cooperative behavior, less explored in the literature, is when agents independently alter the environment to make it easier for everyone to function effectively. Cooperative behavior of this kind might be to put away a hammer that one finds lying on the floor, knowing that another agent will be able to find it more easily later on. In this paper we are concerned with cooperation of this latter type, state-changing behavior that improves the environment for everyone. We examine the effect that a specific "cooperativity rule" has on agents...

Because we think that plans or strategies are useful for coordinating multiple agents, and because we hypothesize that most of the plans we use are build partly by us and partly by our immediate environment (which includes other agents), this paper is devoted to the conditions in which strategies can be viewed as the result of interactions between simple agents, each of them having only local information about the state of the world. Our approach is based on the study of some examples of reactive agents applications. Their features arebriefly described and we underline, in each of them, what we call the emergent strategies obtained from the local interactions between the agents. Three examples are studied this way: the eco-problem-solving implementations of Pengi and the N-Puzzle, and the sociogenesis process occuring in the artificial ant colonies that compose the MANTA project. We then consider a typical strategical game (chess), and see how to decompose it through a distributed reac...

We present in this paper a distributed approach for solving the N-puzzle. This approach is based on the decomposition of a problem into the set of the smallest independent subgoals that can be described, be they serializable or not. These subgoals are at their turn decomposed into agents whose task is to satisfy the subgoal. We have chosen the Eco-Problem-Solving model for describing the internal functioning of these agents. Each of them is then characterized by a state, a goal, a satisfaction behavior and a flight behavior. Those kernel behaviors invoke domaindependent actions that must fit with the domain in which the agent is involved. The description of these actions is made by presenting their basic algorithm and the heuristics used in it, namely the MGB and VMGB distance computations. A simple example of solving is presented, followed by an example of what can be called an "emergent solution" to the problem of the corners. We prove, then, that the method is complete and decidabl...

This paper deals with one of the probably most challenging and, in our opinion, little addressed question that can be found in Distributed Artificial Intelligence today, that of the methodological design of a learning multi-agent system (MAS). In previous work, in order to solve the current software engineering problem of having the ingredients (MAS techniques) but not the recipes (the methodology) we have defined Cassiopeia, an agent-oriented, rolebased method for the design of MAS. It relies on three important notions: (1) independence from the implementation techniques; (2) definition of an agent as a set of three different levels of roles; (3) specification of a methodological process that reconciles both the bottom-up and the top-down approaches to the problem of organization. In this paper we show how this method enables Machine Learning (ML) techniques to be clearly classified and integrated at first hand in the design process of an MAS, by carefully considering the ...

A physically based system of interacting polyhedral objects is used to model self-assembly and spontaneous organization of complex structures. The surfaces of the polyhedra in the simulation are covered with bonding sites in states akin to those of cellular automata. The bonding sites interact with sites on neighbouring polyhedra to apply forces of attraction and repulsion between bodies and to trigger transitions in their states. Using only these mechanisms, the elements exhibit chaining, membrane and cluster formation, and differentiation / segregation. Examples of each of these phenomena are given along with explanations as to how they are formed.

gents, Interactions et Organisation). Avant de conclure, nous abordons la dynamique de notre Syst`eme Multi-Agents. A notre connaissance, ce travail constitue la premi`ere tentative d'application des syst`emes multiagents au domaine de l'information g'eographique. Nous avons `a ce moment fini la phase de mod'elisation, et nous nous trouvons actuellement en phase de programmation du syst`eme. Mots cl'es: SMA R'eactif, Syst`eme d'Information G'eographique, Application, Mod`ele, Interactions . Travail inscrit dans le cadre B.2. Multi-'echelle du PSIG, programme conjoint entre le CNRS et l'IGN sur les syst`emes d'information g'eographique y Recherche de doctorat financ'ee par la Commission Europ'eenne, DGXII, par le moyen de bourse CHM no. ERBCHBICT930672 z Charg'e de Recherche au CNRS x En d'etachement de UFRGS, Porto Alegre, Br'esil, avec une bourse CNPq 2 1 Introduction No

Abstract not found