Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glass Hamiltonians. Certain configurations of the spin system, chosen at random, which serve as memories, are stored in the quenched random couplings. The present analysis is restricted to the case of a finite number p of memorized spin configurations, in the thermodynamic limit. We show that the long-time behavior of the two models is identical, for all temperatures below a transition temperature Tc.

This paper reports several properties of heuristic best-first search strategies whose scoring functions ƒ depend on all the information available from each candidate path, not merely on the current cost g and the estimated completion cost h. It is shown that several known properties of A* retain their form (with the minmax of f playing the role of the optimal cost), which helps establish general tests of admissibility and general conditions for node expansion for these strategies. On the basis of this framework the computational optimality of A*, in the sense of never expanding a node that can be skipped by some other algorithm having access to the same heuristic information that A* uses, is examined. A hierarchy of four optimality types is defined and three classes of algorithms and four domains of problem instances are considered. Computational performances relative to these algorithms and domains are appraised.

Given that the knowledge-based system relies on these inferences in the midst of its operation (i.e., its diagnosis, planning, or whatever), their computational tractability is an important concern. Here we present evidence as to how the cost of computing one kind of inference is directly related to the expressiveness of the representation language.

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T1. Let S1 be the set of equivalence classes of S0 with respect to N, and

Deciding where the truth lies between these two extremes is the main purpose of John Haugeland's marvelously lucid and witty book on what artificial intelligence is all about. Although presented entirely in non-technical terms, it neither oversimplifies the science nor evades the fundamental philosophical issues. Far from ducking the really hard questions, it takes them on, one by one. Artificial intelligence, Haugeland notes, is based on a very good idea, which might well be right, and just as well might not. That idea, the idea that human thinking and machine computing are "radically the same," provides the central theme for his illuminating and provocative book about this exciting new field.

In Ronald Brachman and Hector Levesque, editors, Readings in Knowledge.’ Representation, pages 467-486. Morgan Kaufmann,

Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. Direct applications include economics, medicine, remote sensing, sedimentology, and signal detection (pattern recognition). Also describes indirect applications--in outlier models, density estimation, Bayesian and empirical Bayes analysis, and robustness studies. Goes on to cover mathematical concepts such as identifiability and information, and the inferential problems associated with data from a mixture. Approximate sequential methods are developed here, in order to deal with estimation difficulties and engineering applications.

Although the concept ofknowledge plays a central role in artificial intelligence, the theoretical foundations of knowledge representation currently rest on a very limited conception of what it means for a machine to know a proposition. In the current view, the machine is regarded as knowing a fact if its state either explicitly encodes the fact as a sentence of an interpreted formal language or if such a sentence can be derived from other encoded sentences according to the rules of an appropriate logical system.

Criterion l: The notation should be as Close to English as possible.