Principles of artificial intelligence
Principles of artificial intelligence
Transition network grammars for natural language analysis
Communications of the ACM
Learning and Memory
Understanding Natural Language
Understanding Natural Language
Artificial Intelligence Programming
Artificial Intelligence Programming
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
Learning Structural Descriptions From Examples
Learning Structural Descriptions From Examples
Toward A Model Of Children''s Story Comprehension
Toward A Model Of Children''s Story Comprehension
Truth Maintenance Systems for Problem Solving
Truth Maintenance Systems for Problem Solving
Retrieval and organizational strategies in conceptual memory: a computer model
Retrieval and organizational strategies in conceptual memory: a computer model
Generalization and memory in an integrated understanding system
Generalization and memory in an integrated understanding system
User-specified domain knowledge for document retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
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Overall, the field of information retrieval is already more aware than many other fields of the relevance of artificial intelligence (AI) [1-6]. Nonetheless there remain exciting applications of artificial intelligence that have been so far overlooked. In this paper we will point out some of the ways artificial intelligence might influence the field of information retrieval. We will then examine one application in more detail to discover the kind of technical problems involved in its fruitful exploitation.Before proceeding, it is important to interject a note of caution. While the promise of artificial intelligence is indeed bright, the time of complete fulfillment of its promise is a long way off. Of course, some of the expected contributions are shorter term than others. However, the more difficult problems will fall only after a good deal of basic research is accomplished. Artificial intelligence researchers have, in the past, been culpable of what can most charitably be described as over-optimism [7,8]. This naivete on the part of even the most respected of researchers stemmed from the profound subtleties underlying intelligent behavior. The problem is compounded by the fact that some of the most difficult of intelligent behavior (i.e. common sense) seems intuitively easy.