Using The Active Object Model To Implement Multi-agent Systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)This paper discusses the implementation and runtime support for Multi-Agent Systems (MAS). We start presenting MAS in the context of Open Distributed Processing (ODP). Next, a model of a cognitive agent being currently developed at LIFIA is detailed. Taking this model as reference, we examine alternatives for supporting cognitive agents in distributed and heterogeneous environments. Finally, a distributed processing tool developed by the authors is presented. This tool follows the active object model and we show that active object and agent are strongly related concepts.70772010/ 17804-7; FAPESP; São Paulo Research Foundation; 2010/02098-0; FAPESP; São Paulo Research Foundation; 2010/18268-1; FAPESP; São Paulo Research FoundationFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Unifying preference and judgment aggregation

The paper proposes a unification of the two main frameworks commonly used for the analysis of collective decision-making: the framework of preference aggregation, developed from the seminal work of K. Arrow on social choice theory; and the more recent framework of judgment aggregation. Such unification provides several original insights on collective decision-making problems. The methods used are based on logic and, in particular, on formal semantics

A fair payoff distribution for myopic rational agents

We consider the case of self-interested agents that are willing to form coalitions for increasing their individual rewards. We assume that each agent knows its individual payoff when a coalition structure (CS) is formed. We consider a CS to be stable if no individual agent has an incentive to change coalition from this CS. When no stable CSs exist, rational agents will be changing coalitions forever. When stable CSs exist, they may not be unique, and choosing one over the other may give an unfair advantage to some agents. In addition, it may not be possible to reach a stable CS from any CS using a sequence of myopic rational actions. We propose a payoff distribution scheme that is based on the expected utility of a rational myopic agent (an agent that changes coalitions to maximize immediate reward) given a probability distribution over the space of coalition structures for selecting the initial CS. To compute this expected utility, we model the coalition formation process by a Markov chain. We recommend that agents share the utility from a social welfare maximizing CS proportionally to the expected utility they would receive if all agents acted in a myopic rational fashion. This scheme guarantees that agents receive at least as much as their expected utility from myopic behavior, which ensures sufficient incentives for the agents to use our proposed payoff distribution

Optimal strategies in sequential bidding

We are interested in mechanisms that maximize the final social welfare. In [1] this problem was studied for multiunit auctions with unit demand bidders and for the public project problem, and in each case social welfare undominated mechanisms in the class of feasible and incentive compatible mechanisms were identified. One way to improve upon these optimality results is by relaxing the assumption of simultaneity and allowing the players to move sequentially. With this in mind, we study here sequential versions of two feasible Groves mechanisms used for single item auctions: the Vickrey auction and the Bailey-Cavallo mechanism. Because of the absence of dominant strategies in this sequential setting, we focus on a weaker concept of an optimal strategy. For each mechanism, we introduce natural optimal strategies and observe that in each mechanism these strategies exhibit different behaviour. However, we then show that among all optimal strategies, the one we introduce for each mechanism maximizes the social welfare when each player follows it. The resulting social welfare can be larger than the one obtained in the simultaneous setting

Towards a framework for agent coordination and reorganization, AgentCoRe

Research in the area of Multi-Agent System (MAS) organization has shown that the ability for a MAS to adapt its organizational structure can be beneficial when coping with dynamics and uncertainty in the MASs environment. Different types of reorganization exist, such as changing relations and interaction patterns between agents, changing agent roles and changing the coordination style in the MAS. In this paper we propose a framework for agent Coordination and Reorganization (AgentCoRe) that incorporates each of these aspects of reorganization. We describe both declarative and procedural knowledge an agent uses to decompose and assign tasks, and to reorganize. The RoboCupRescue simulation environment is used to demonstrate how AgentCoRe is used to build a MAS that is capable of reorganizing itself by changing relations, interaction patterns and agent roles