Information Technology
Meta-rules: Reasoning about control
How can we insure that knowledge embedded in a program is applied effectively? Traditionally the answer to this question has been sought in different problem solving paradigms and in different approaches to encoding and indexing knowledge. Each of these is useful with a certain variety of problem, but they all share a common problem: they become ineffective in the face of a sufficiently large knowledge base. How then can we make it possible for a system to continue to function in the face of a very large number of plausibly useful chunks of knowledge? In response to this question we propose a framework for viewing issues of knowledge indexing and retrieval, a framework that includes what appears to be a useful perspective on the concept of a strategy. We view strategies as a means of controlling invocation in situations where traditional selection mechanisms become ineffective. We examine ways to effect such control, and describe meta-rules, a means of specifying strategies which offers a number of advantages. We consider at some length how and when it is useful to reason about control, and explore the advantages meta-rules offer for doing this. Artificial Intellligence 15:179-222.
Generating relevant explanation: Natural language responses to questions about database structure
Proceedings of the AAAI, 306-309.
The SDC speech understanding system
Barnett, J. | Bernstein, M. | Gillman, R. | Kameny, I.
In Lea, Trends, 272-293
Non-monotonic logic I
'Non-monotonic' logical systems are logics in which the introduction of new axioms can invalidate old theorems. Such logics are very important in modeling the benefits of active processes which, acting in the presence of incomplete information, must make and subsequently revise assumptions in light of new observations. We present the motivation and history of such logics. We develop model and proof theories, a proof procedure, and applications for one non-monotonic logic. In particular, we prove the completeness of the non-monotoic predicate calculus and the decidability of the non-monotonic sentential calculus. We also discuss characteristic properties of this logic and its relationship to stronger logics, logics of incomplete information, and truth maintenance systems. Artificial Intelligence 13:41-72.
Analyzing intention in utterances
Allen, J. F. | Perrault, C. R.
Artificial Intelligence 15 (3):143-178
Repair theory: A generative theory of bugs in procedural skills
Cognitive Science 4:379-426
Increasing tree search efficiency for constraint satisfaction problems
Haralick, R. M. | Elliot, G. L.
Artificial Intelligence, 14 (3), 263-313.