AI modelling of animal movements in a heterogeneous habitat

Abstract We demonstrate use of object-oriented programming, dynamic linkages, rule-based decission procedures, and several other concepts from the field of artificial intelligence (AI) for modelling animal movements in a heterogeneous habitat. An object-oriented model of a deer that learns about habitat structure, plans movements, and accommodates to changes in a patchy brushland habitat is described and used to simulate effects of patch size on deer movements. Innovative features of this model include: (a) representation of habitat as a network of heterogeneous patches, (b) representation of an individual's knowledge of the environment (memory network) as different from but related to the habitat (habitat network), (c) individual's use of knowledge of the environment to plan paths to goals, and (d) ability for an individual to change its knowledge base when it encounters changes in the environment. Decision rules in the model are hypothetical, but the current application suggests that object-oriented modelling provides a concise yet detailed technology for modelling animal movements.

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