The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling

Adaptive multiagent system for seismic emergency management

Presently, most multiagent frameworks are typically programmed in Java. Since the JADE platform has been recently ported to .NET, we used it to create an adaptive multiagent system where the knowledge base of the agents is managed using the CLIPS language, also called from .NET. The multiagent system is applied to create seismic risk scenarios, simulations of emergency situations, in which different parties, modeled as adaptive agents, interact and cooperate.adaptive systems, risk management, seisms.

Control of Networked Multiagent Systems with Uncertain Graph Topologies

Multiagent systems consist of agents that locally exchange information through a physical network subject to a graph topology. Current control methods for networked multiagent systems assume the knowledge of graph topologies in order to design distributed control laws for achieving desired global system behaviors. However, this assumption may not be valid for situations where graph topologies are subject to uncertainties either due to changes in the physical network or the presence of modeling errors especially for multiagent systems involving a large number of interacting agents. Motivating from this standpoint, this paper studies distributed control of networked multiagent systems with uncertain graph topologies. The proposed framework involves a controller architecture that has an ability to adapt its feed- back gains in response to system variations. Specifically, we analytically show that the proposed controller drives the trajectories of a networked multiagent system subject to a graph topology with time-varying uncertainties to a close neighborhood of the trajectories of a given reference model having a desired graph topology. As a special case, we also show that a networked multi-agent system subject to a graph topology with constant uncertainties asymptotically converges to the trajectories of a given reference model. Although the main result of this paper is presented in the context of average consensus problem, the proposed framework can be used for many other problems related to networked multiagent systems with uncertain graph topologies.Comment: 14 pages, 2 figure

Aspects of cooperating agents

An overview on aspects about cooperating agents is presented. As multiagent systems are various, we start with a classification of multiagent systems which is particularly influenced by an article from Decker, Durfee, and Lesser [Decker& 89]. In the following, the aspects of communication, planning, and negotiation are examined. On the occasion of communication, the discussion is split into: no communication - simple protocols - artificial languages. The planning aspect is broken into sections: from classical to multiagent planning - a general multiagent planning theory - intention - intention-directed multiagent planning. Finally, a summary of Brigitte and Hassan Lâasri and Victor Lesser\u27s negotiation theory will be presented

Organizational Structure-Satisfactory Social Law Determination in Multiagent Workflow Systems

The multiagent workflow systems can be formalized from an organizational structure viewpoint, which includes three parts: the interaction structure among agents, the temporal flow of activities, and the critical resource sharing relations among activities. While agents execute activities, they should decide their strategies to satisfy the constraints brought by the organizational structure of multiagent workflow system. To avoid collisions in the multiagent workflow system, this paper presents a method to determine social laws in the system to restrict the strategies of agents and activities; the determined social laws can satisfy the characteristics of organization structures so as to minimize the conflicts among agents and activities. Moreover, we also deal with the social law adjustment mechanism for the alternations of interaction relations, temporal flows, and critical resource sharing relations. It is proved that our model can produce useful social laws for organizational structure of multiagent workflow systems, i.e., the conflicts brought by the constraints of organization structure can be minimized