<title>Knowledge-based goal-driven approach for information extraction and decision making for target recognition</title>

Abstract

This paper presents a novel goal-driven approach for designing a knowledge-based system for information extraction and decision-making for target recognition. The underlying goal-driven model uses a goal frame tree schema for target organization, a hybrid rule-based pattern- directed formalism for target structural encoding, and a goal-driven inferential control strategy. The knowledge-base consists of three basic structures for the organization and control of target information: goals, target parameters, and an object-rulebase. Goal frames represent target recognition tasks as goals and subgoals in the knowledge base. Target parameters represent characteristic attributes of targets that are encoded as information atoms. Information atoms may have one or more assigned values and are used for information extraction. The object-rulebase consists of pattern/action assertional implications that describe the logical relationships existing between target parameter values. A goal realization process formulates symbolic patten expressions whose atomic values map to target parameters contained a priori in a hierarchical database of target state information. Symbolic pattern expression creation is accomplished via the application of a novel goal-driven inference strategy that logically prunes an AND/OR tree constructed object-rulebase. Similarity analysis is performed via pattern matching of query symbolic patterns and a priori instantiated target parameters.