AN ARTIFICIAL-INTELLIGENCE MODELING APPROACH TO SIMULATING ANIMAL HABITAT INTERACTIONS
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abstract
Ecological modellers have begun to recognize the potential of object-oriented programming techniques in structuring models. However, little has been done to take advantage of artificial intelligence's (AI) symbolic representations to model the decision-making processes of animals. Here, a generic model of animal-habitat interaction and a specific model of moose-, Alces alces L., forest interactions in Finland are described that are event-driven and behavior-based. Individual level simulation is accomplished through an object-oriented knowledge representation scheme and AI techniques to implement a hierarchical decision-making model of behavior. The habitat is likewise represented in an object-oriented scheme, allowing the simulation of a heterogeneous environment. Other AI techniques for modelling behavior, memory, and actions are discussed including LISP methods, rule-based reasoning, and several search algorithms. Simulations of the moose-forest system show the power of this approach but are not intended to advance the theory of large-herbivore behavior and foraging. AI techniques are found to be most beneficial in (a) studying population processes based on individual level models of behavior, (b) modelling spatial heterogeneity, (c) building event-driven models, (d) providing a conceptual clarity to model construction, and (e) providing a structure equally well suited to simulating resource management. 1988.
