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We present a utility-driven rationality and a complementary-driven rationality based model, relative to multiple partner coalitions, motivated by relations of dependence and instrumental goal adoption. For this purpose, we analyze social dependency patterns and its corresponding dependency networks. The networks are used as a source of quantitative and(More)
This paper proposes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The proposed frame-work facilitates the integration of two MABS constructs: i) the design of the environmental physical market properties, and ii) the simulation models of the decision-making and reactive(More)
Looking to the operation of an agent architecture, ie. its goal generation and maintenance processing, is relevant to understand fully how a moral based agent takes appropriate and diverse decisions within social situations of serious games. How decision does happen is a complex issue and the major motivation of this paper, and our answer, the proposal of a(More)
Autonomous agents are being used in an increasing number of applications. The agents operate in complex environments and, over time, conflicts inevitably occur among them. Negotiation is the predominant process for resolving conflicts. This paper presents a generic negotiation model for autonomous agents that handles multi-party, multi-issue and single or(More)
Autonomous agents operate in complex environments and, over time, conflicts inevitably occur among them. Negotiation is the predominant process for resolving conflicts. This paper presents a generic negotiation model for autonomous agents that handles multi-party, multi-issue and repeated rounds. The model is based on com-putationally tractable assumptions,(More)
AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian(More)