Dispute Resolution Using Argumentation-Based Mediation

Mediation is a process, in which both parties agree to resolve their dispute by negotiating over alternative solutions presented by a mediator. In order to construct such solutions, mediation brings more information and knowledge, and, if possible, resources to the negotiation table. The contribution of this paper is the automated mediation machinery which does that. It presents an argumentation-based mediation approach that extends the logic-based approach to argumentation-based negotiation involving BDI agents. The paper describes the mediation algorithm. For comparison it illustrates the method with a case study used in an earlier work. It demonstrates how the computational mediator can deal with realistic situations in which the negotiating agents would otherwise fail due to lack of knowledge and/or resources.Comment: 6 page

Intentions and Information in Discourse

This paper is about the flow of inference between communicative intentions, discourse structure and the domain during discourse processing. We augment a theory of discourse interpretation with a theory of distinct mental attitudes and reasoning about them, in order to provide an account of how the attitudes interact with reasoning about discourse structure

Joint Commitment and Collective Belief

According to Margaret Gilbert, two or more people collectively believe that p if and only if they are jointly committed to believe that p as a body. But the way she construes joint commitment in her account – as a commitment of and by the several parties to “doing something as a body” – encourages the thought that the phenomenon accounted for is not that of genuine belief. I explain why this concern arises and explore a different way of construing joint commitment, in order to avoid the concern. This leads me to propose a revised Gilbertian account of collective belief, according to which two or more people collectively believe that p if and only if they are jointly committed to p as true

Towards a goal-oriented agent-based simulation framework for high-performance computing

Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

Unifying control in a layered agent architecture

In this paper, we set up a unifying perspective of the individual control layers of the architecture InteRRaP for autonomous interacting agents. InteRRaP is a pragmatic approach to designing complex dynamic agent societies, e.g. for robotics Müller & Pischel and cooperative scheduling applications Fischer et al.94. It is based on three general functions describing how the actions an agent commits to are derived from its perception and from its mental model: belief revision and abstraction, situation recognition and goal activation, and planning and scheduling. It is argued that each InteRRaP control layer - the behaviour-based layer, the local planning layer, and the cooperative planning layer - can be described by a combination of different instantiations of these control functions. The basic structure of a control layer is defined. The individual functions and their implementation in the different layers are outlined. We demonstrate various options for the design of interacting agents within this framework by means of an interacting robots application. The performance of different agent types in a multiagent environment is empirically evaluated by a series of experiments