Importance

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Abstract

An RTA or similar rule-based agent (102) is enabled to construct a virtual simulation of its environment by the provision of an internal prediction module (100). The prediction module comprises a rule population run in tandem with the population (106) defining the agent behaviors. The prediction module rules represent predictions of agent behavior state changes and their accuracy is periodically checked (110), with more accurate rules being assigned a higher fitness rating. A genetic algorithm (108) defines mutation operators by which further rules are evolved from those having the highest fitness. When a predetermined level of fitness (prediction accuracy) is achieved, the prediction module rules provide (116) a virtual simulation of the environment to the agent behaviors by direct control in place of external (real world) stimuli.

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