Search

An underlying problem of current interface agent research is the failure to adequately address effective and efficient knowledge representations and associated methodologies suitable for modeling the users' interactions with the system. These user models lack the representational complexity to manage the uncertainty and dynamics involved in predicting user intent and modeling user behavior. A utility theory-based approach is presented for effective user intent prediction by incorporating the ability to explicitly model users' goals, the uncertainty in the users' intent in pursuing these goals, and the dynamics of users' behavior. We present an interface agent architecture, CIaA, that incorporates our approach and discuss the integration of CIaA with three disparate domains — a probabilistic expert system shell, a natural language input database query system, and a virtual space plane —that are being used as test beds for our interface agent research.

This paper provides insight into combining stochastic and deterministic search methods using evolutionary algorithms (EAs) such as evolutionary programming, evolutionary strategies, and genetic algorithms integrated with depth-first search with backtracking, branch and bound, and best-first search algorithms such as A*. An important view of such an integration focuses on both theoretical analysis and experimental evaluation. Included in the discussion is the constraining impact of the “No Free Lunch Theorem” on combined search performance. Also, a variety of combinations of EAs and deterministic combined search methods are proposed along with expert system components. Discussion of anticipated results of such architectures speculates about existence of high performance integrated search environments. A particular successful specific NPC problem environment is presented that employs search metrics for short-term performance evaluation and combined search algorithm selection.