Building collaborative intelligent agents: revealing main pillars

Abstract

Software agents are persistent, knowledgeable, autonomous, collaborative, and learnable entities. One important feature of software agents is the ability to interact and communicate as a team to achieve more than they could individually. Although the Belief-DesireIntention (BDI) agent model is possibly the best known and used model of practical reasoning agent; nevertheless, this model does not address the key concept of how individual agent learn from the environment and manipulate itself to collaborate with others. In this paper we present a novel approach that customize the BDI model to define a so-called "RBDIA: RapportBelief-Desire-Intention-Adaptation" as a generic method to support progress from individual autonomous agent concept towards a collaborative multiple agents. Rapport here refers to the component that connects an agent to its environment, whereas Adaptation module incorporates mechanisms of learning. We believe that the five proposed tiers for multiagent systems modeling serves for mastering the complexity and the difficulty of setting up effective autonomous collaborative MAS.

Parallel agent-based simulation with Repast for High Performance Computing

“…The search for increased computational capacity has led various research groups to employ existing standards such as the High Level Architecture (HLA) 3 and various kinds of Web services. 4 These widely used standards can be effective for their purposes but they are not designed to work efficiently on extremely large computing platforms. This ultimately limits their ability to address the need for computationally complex model runs.…”