Knowledge Representation and Intelligent Systems: from Semantic Networks to Cognitive Maps (Artificial Intelligence).

Al systems have long relied on propositional semantic network knowledge representation. Although many Al projects produce impressive results, they tend to be difficult to generalize and have yielded only meagre progress towards a theory of intelligence. The field lacks coherence in definitions, assumptions, and methods. A systematic treatment of the underlying knowledge representation issues appears essential for the development of a more unified theory. This dissertation considers knowledge networks in the context of an attempt to model and produce intelligent, adaptive behavior. Natural intelligent systems build perceptual and predictive capacity on the basis of ordinary experience, and function routinely in ill-defined, context sensitive situations. Propositional Al systems have not demonstrated these capabilities; this may be inevitable given their underlying knowledge representations. To facilitate an analysis of these issues a set of parameters characterizing the space of such networks is developed. The networks so defined range from Quillian's semantic network to the more theoretically based cognitive map A network activity passing simulation (NAPS) was developed to investigate the effects of these various parameters on performance. NAPS searches for subgoals between two given locations in a familiar environment. This wayfinding task is a simplification of the more general problem solving method of searching for subgoals between a percieved state and a desired state. NAPS inputs a set of knowledge representation parameters and constructs the sort of network specified for use in testing. Then given a start and a goal location NAPS propagates activity in the net to select a subgoal. Experiments were performed in several environments; networks were compared in terms of speed, reliability, flexibility and robustness. The results were somewhat surprizing; marker passing semantic networks are more reliable than activity passing cognitive maps in simple environments but prove to be less reliable in complex situations. The semantic networks are shown to be rigid and inflexible, and so unsuitable for use in unpredictable, or difficult environments without the inclusion of additional mechanisms. The cognitive maps by contrast, are able to h and le unexpected environmental vagaries without the intervention of an intelligent executive.Ph.D.Computer scienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/160552/1/8512454.pd