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Integrating Life-Like Action Selection into Cycle-Based Agent Simulation Environments

Standardised simulation platforms such as RePast, Swarm, MASON and NetLogo are making Agent-Based Modelling accessible to ever-widening audiences. Some proportion of these modellers have good reason to want their agents to express relatively complex behaviour, or they may wish to describe their agents’ actions in terms of real time. Agents of increasing complexity may often be better (more simply) described using hierarchical constructs which express the priorities and goals of their actions, and the contexts in which sets of actions may be applicable (Bryson, 2003a). Describing an agent’s behaviour clearly and succinctly in this way might seem at odds with the iterative, cycle-based nature of most simulation platforms. Because each agent is known to act in lock-step synchrony with the others, describing the individual’s behaviour in terms of fluid, coherent long-term plans may seem difficult. In this paper we describe how an action-selection system designed for more conventionallyhumanoid AI such as robotics and virtual reality can be incorporated into a cycle-based ABM simulation platform. We integrate a Python-language version of the action selection for Bryson’s Behavior Oriented Design (BOD) into a fairly standard cycle-based simulation platform, MASON (Luke et al., 2003). The resulting system is currently being used as a research platform in our group, and has been used for laboratories in the European Agent Systems Summer School.