A Goal - Based Approach to Intelligent Information

Intelligent information retrieval (IIR) requires inference. The number of inferences that can be drawn by even a simple reasoner is very large, and the inferential resources available to any practical computer system are limited. This problem is one long faced by AI researchers. In this paper, we present a method used by two recent machine learning programs for control of inference that is relevant to the design of IIR systems. The key feature of the approach is the use of explicit representations of desired knowledge, which we call knowledge goals. Our theory addresses the representation of knowledge goals, methods for generating and transforming these goals, and heuristics for selecting among potential inferences in order to feasibly satisfy such goals. In this view, IIR becomes a kind of planning: decisions about what to infer, how to infer and when to infer are based on representations of desired knowledge, as well as internal representations of the system's inferential abilities and current state. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel newspaper stories, and a diierential diagnosis program that improves its accuracy with experience. We conclude by making several suggestions on how this machine learning framework can be integrated with existing information retrieval methods. An important step in the evolution of intelligent information retrieval (IIR) systems involves the loosening of the relationship between a query and information retrieved to address it. A database system nds all of the stored structures that precisely match a query. An IIR system should be able to nd and process relevant stored information into an appropriate response to a query. Such a system should be guided by the meaning of its queries, not only by their syntax, and should be able to reason about information implicit in its stored structures. IIR, therefore, involves inference. Systems capable of generalized inference face a problem ubiquitous in AI: a combinatorial explosion of possible inferences. Although the conclusions that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially innnite), the inferential resources available to any reasoning system are limited. In general, reasoning systems simply cannot draw all justiied inferences. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Several methods of controlling inference have been proposed. Perhaps the simplest is constrained forward chaining: making as many inferences as …