In this paper we propose an argumentation-based framework for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of cases that they have to exchange (using argumentation) in ...

In the supply chain modeling context, the agent-based model aims to represent not only each node, but also the information sharing process among these nodes. Despite the complexity of the configuration, the agent-based model can be applied straightforwardly to support the collaborative planning process. This allows the parties to achieve common goals effectively. Thus by sharing accurate, actio...

We are exploring the enhancement of models of agent behaviour with more “human-like” decision making strategies than are presently available. Our motivation is to build multi-agent based simulations of human societies that would exhibit more realistic simulation behaviours. This in turn will allow researchers to study more complex issues regarding individual and organisational performance than ...

In this paper, we examine the effects of probabilistic response on a task allocation problem for a decentralized multi-agent system (MAS) and how such a mechanism may be used to tune the level of redundancy in an MAS. Redundancy refers to a back up pool of agents, beyond the necessary number required to act on a task, that have experience on that task. We present a formal analysis of a response...

Norms or conventions can be used as external correlating signals to promote coordination between rational agents and hence have merited in-depth study of the evolution and economics of norms both in the social sciences and in multiagent systems. While agent simulations can be used to gain a cursory idea of when and what norms can evolve, the estimations obtained by running simulations can be co...

The paper presents an agent based surveillance system for use in monitoring scenes involving both pedestrians and vehicles. The surveillance system supplies textual descriptions for the dynamic activity occurring in the 3D world. These are derived by means of dynamic and probabilistic inference based on geometric information provided by a vision system that tracks vehicles and pedestrians. The ...

In this paper a multi-agent architecture for intelligent Websites is presented and applied in insurance. The architecture has been designed and implemented using the compositional development method for multi-agent systems DESIRE. The agents within this architecture are based on a generic broker agent model. It is shown how it can be exploited to design an intelligent Website for insurance, dev...

In this paper a multiagent Tourism Recommender System is presented. This system has a multiagent architecture and one of its main agents, the Travel Assistant Agent (T-Agent), is modelled as a graded BDI agent. The graded BDI agent model allows us to specify an agent architecture able to deal with the environment uncertainty and with graded mental attitudes. We focus on the implementational asp...

In this paper, we propose an approach using meta-agents for monitoring multi-agents and controlling their behaviour while moving between states in a network. The meta-agents are built on the agents in the system and then used to inspect the agents’ behaviour when reaching a result as well as perceiving the reason for that result. Each meta-agent can comprise everything from one agent to dozen o...

We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs. We show how congregations can be used to explain and predict the behavior of self-interested agents that are searching for other agents to interact with. This framework is integrated with Vidal and Durfee’s CLRI framework [11] for evaluating learning wi...