In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.

In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley, 151-168.

In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.

AI Memo 389, AI Laboratory, Massachusetts Instituteof Technology.

As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery. Many critics (Dreyfus, 1972), (Lighthill, 1973) have urged that we are over the border. We have been very defensive toward this charge, drawing ourselves up with dignity when it is made and folding the cloak of Science about us. On the other hand, in private, we have been justifiably proud of our willingness to explore weird ideas, because pursuing them is the only way to make progress.

In the spring of 1971, I attended a course on statistical inference taught by Arthur Dempster at Harvard. In the fall of that same year Geoffrey Watson suggested I give a talk expositing Dempster's work on upper and lower probabilities to the Department of Statistics at Princeton. This essay is one of the results of the ensuing effort. It offers a reinterpretation of Dempster's work, a reinterpretation that identifies his "lower probabilities" as epistemic probabilities or degrees of belief, takes the rule for combining such degrees of belief as fundamental, and abandons the idea that they arise as lower bounds over classes of Bayesian probabilities.

This is an informal description of my ideas about using formal logic as a tool for reasoning systems using computers. The theoretical ideas are illustrated by the features of fol. All of the examples presented have actually run using the fol system.

This paper describes a computer system for understanding English. The system answers questions, executes commands, and accepts information in an interactive English dialog. It is based on the belief that in modeling language understanding, we must deal in an integrated way with all of the aspects of language—syntax, semantics, and inference. The system contains a parser, a recognition grammar of English, programs for semantic analysis, and a general problem solving system. We assume that a computer cannot deal reasonably with language unless it can understand the subject it is discussing. Therefore, the program is given a detailed model of a particular domain.

A generalization of AND/OR graphs is introduced as a problem solving model, in which subproblem interdependence in problem reduction can be explicitly accounted for. An ordered-search algorithm is given to find a solution. The algorithm is proven to be admissible and optimal. Examples are given which show the application of the formalism to problems which cannot be modelled by AND/OR graphs. Generalized AND/OR graphs are finally shown to be equivalent to type O grammars.

Lexical ambiguity can be syntactic if it involves more than one grammatical category for a single word, or semantic if more than one meaning can be associated with a word. In this article we discuss the application of a Bayesian-network model in the resolution of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatically for any context-free grammar, and a subnetwork for semantic analysis, which, in the spirit of Fillmore's (1968) case grammars, seeks to fulfill the required cases of all candidates for verb of the sentence. Solving for the highest joint probability of the variables conditioned upon the evidences to the network yields the most likely candidate with its meaning, along with its cases and respective meanings. This is achieved by fixing the values of all evidence nodes concurrently, and then performing a stochastic simulation in which the remaining nodes are updated probabilistically with a high degree of parallelism.