Intelligent Behavior as an Adaptation to the Task Environment

As research in artificial intelligence focuses on increasingly complex task domains, a key question to be resolved is how to design a system that can efficiently acquire knowledge and gracefully adapt its behavior in an uncertain environment. This dissertation argues that examining more closely the way animate systems cope with real-world environments can provide valuable insights about the structural requirements for intelligent behavior. Accordingly, a class of simulated environments is designed that embodies many of the important functional properties characteristic of natural environments. A new type of adaptive system is then defined that uses pattern-directed, rule-based processing to cope with uncertain information. As a rule-based system, the system presented here is notable in that several rules can be active at once and there are no fixed priorities determining the order in which rules can be activated. Moreover, the syntax of each rule is simple enough to make a powerful learning heuristic applicable--one that is provably more efficient than the techniques used in most other adaptive rule-based systems. A simple version of the adaptive system is implemented as a hypothetical organism having to locate resources and avoid noxious stimuli by generating temporal sequences of actions in a simulated environment. Simulation results show that the naive organism quickly acquires the knowledge required to function effectively. Further experiments show that the system is capable of discriminating a large class of schematic patterns; and, that prior learning experiences transfer to novel situations. The results presented here demonstrate that activity in a collection of simple computational elements--operating in parallel and activated stochastically--can be orchestrated to produce reliable behavior in a challenging environment. The system touches on several issues related to cognitive functioning such as the generic representation of objects and the management of limited processing resources. These issues have been addressed in a way that is computationally feasible and that allows for rigorous testing.