Combining Rational and Biological Factors in Virtual Agent Decision Making

To enhance believability of virtual agents, this paper presents an agent-based modelling approach for decision making, which integrates rational reasoning based on means-end analysis with personal psychological and biological aspects. The agent model developed is a combination of a BDI-model and a utility-based decision model in the context of specific desires and beliefs. The approach is illustrated by addressing the behaviour of violent criminals, thereby creating a model for virtual criminals. Within a number of simulation experiments, the model has been tested in the context of a street robbery scenario. In addition, a user study has been performed, which confirms the fact that the model enhances believability of virtual agents. © 2009 The Author(s)

The Theory and Practice of Intention Reconsideration

Abstract. One of the key problems in the design of belief-desire-intention (BDI) agents is that of finding an appropriate policy for intention reconsideration. Crudely, the idea is that at any given time, an agent will have a number of intentions, relating to states of affairs that the agent has committed to bring about. An agent chooses plans that are appropriate for bringing about these intentions; if a particular plan for a given intention fails, then the agent will typically replan, to find an alternative course of action for this intention. However, a rational agent’s intentions will not be static. From time-to-time, it makes sense for such an agent to reconsider its intentions, for example when the intention is doomed never to be realized, or else when the agent would simply profit from adopting another, more fruitful goal. This paper presents a detailed investigation of the properties of intention reconsideration. The work builds on the foundational work of Kinny and Georgeff, who investigated the properties of various intention reconsideration strategies in environments that were to varying degrees dynamic, i.e. subject to unanticipated change. The present paper broadly falls into two distinct parts. In the first part, the authors extend wor

Crowd behavior modeling in emergency evacuation scenarios using belief-desire-intention model

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Slicing agent programs for more efficient verification

This is data related to the paper `Slicing Agent Programs for more Efficient Verification' published in the post-proceedings for the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018). Specifically it contains the Prolog program used to slice a transformed Gwendolen Program and the logs of the verification runs used to evaluate the effectiveness of the proposed slicing method in reducing the state space for verification