A new system for the analysis of aerial photographs of suburban areas is presented. In this system, regions are characterized by various spectral and spatial features such as brightness, color, size, shape, texture, and spatial relationships with other regions. The analysis proceeds from a global survey of the picture to the detailed analysis of specific regions. The former process extracts several kinds of characteristic regions which are supposed to include specific objects by using knowledge-free picture processing programs. In the detailed analysis, several object-detection programs perform parallel analysis of extracted characteristic regions in detail to recognize objects such as crop fields, woods, roads, and houses.

It can be seen that a great deal of worthwhile material has now been generated about computer chess. There is also quite a bit of nonsense by persons who have never built a program. Several groups with excellent programs have done little publishing, although I can hardly blame them since their work requires much time and is usually unsupported by any funding agencies. Certain staples have given rise to duplication: All but one of the books published explain the depth-first alpha-beta procedure. We expect that by far the largest portion of our readers will be scientists interested in updating their knowledge of the subject.

A model of a text processing package is described which is based on elementary set theory. This is used to describe procedures for computing indexes, concordances, and collocations. Aspects of the implementation of the model are given.

As scientists interested in studying the phenomenon of “intelligence”, we first choose a view of Man, develop a theory of how intelligent behavior is managed, and construct some models which can test and refine that theory. The view we choose is that Man is a processor of symbolic information. The theory is that sophisticated cognitive tasks can be cast as searches or explorations, and that each human possesses (and efficiently accesses) a large body of informal rules of thumb (or heuristics) which constrain his search. The source of what we colloquially call “intelligence” is seen to be very efficient searching of an a priori immense space. Some computational models which incorporate this theory are described.

An analysis of the alpha-beta pruning algorithm is presented which takes into account both shallow and deep cut-offs. A formula is first developed to measure the average number of terminal nodes examined by the algorithm in a uniform tree of degree n and depth d when ties are allowed among the bottom positions: specifically, all bottom values are assumed to be independent identically distributed random variables drawn from a discrete probability distribution. A worst case analysis over all possible probability distributions is then presented by considering the limiting case when the discrete probability distribution tends to a continuous probability distribution. The branching factor of the alpha-beta pruning algorithm is shown to grow with n as Θ(n/lnn), therefore confirming a claim by Knuth and Moore that deep cut-offs only have a second order effect on the behavior of the algorithm.

This paper presents a body of program synthesis knowledge dealing with array operations, space reutilization, the divide-and-conquer paradigm, conversion from recursive paradigms to iterative paradigms, and ordered set enumerations. Such knowledge can be used for the synthesis of efficient and in-place sorts including quicksort, mergesort, sinking sort, and bubble sort, as well as other ordered set operations such as set union, element removal, and element addition. The knowledge is explicated to a level of detail such that it is possible to codify this knowledge as a set of program synthesis rules for use by a computer-based synthesis system. The use and content of this set of programming rules is illustrated by the methodical synthesis of bubble sort, sinking sort, quicksort, and mergesort.

The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable. More specifically, if F is a fuzzy subset of a universe of discourse U={u} which is characterized by its membership function μF, then a proposition of the form “X is F,” where X is a variable taking values in U, induces a possibility distribution ∏X which equates the possibility of X taking the value u to μF(u)—the compatibility of u with F. In this way, X becomes a fuzzy variable which is associated with the possibility distribution ∏x in much the same way as a random variable is associated with a probability distribution. In general, a variable may be associated both with a possibility distribution and a probability distribution, with the weak connection between the two expressed as the possibility/probability consistency principle. A thesis advanced in this paper is that the imprecision that is intrinsic in natural languages is, in the main, possibilistic rather than probabilistic in nature. Thus, by employing the concept of a possibility distribution, a proposition, p, in a natural language may be translated into a procedure which computes the probability distribution of a set of attributes which are implied by p. Several types of conditional translation rules are discussed and, in particular, a translation rule for propositions of the form “X is F is α-possible,” where α is a number in the interval [0, 1], is formulated and illustrated by examples.

The word-by-word time-course of spoken language understanding was investigated in two experiments, focussing simultaneously on word-recognition (local) processes and on structural and interpretative (global) processes. Both experiments used three word-monitoring tasks, which varied the description under which the word-target was monitored for (phonetic, semantic, or both) and three different prose contexts (normal, semantically anomalous, and scrambled), as well as distributing word-targets across nine word-positions in the test-sentences. The presence or absence of a context sentence, varied across the two experiments, allowed an estimate of between-sentence effects on local and global processes. The combined results, presenting a detailed picture of the temporal structuring of these various processes, provided evidence for an on-line interactive language processing theory, in which lexical, structural (syntactic), and interpretative knowledge sources communicate and interact during processing in an optimally efficient and accurate manner.

Production systems designed to function and grow in environments that make large numbers of different, sometimes competing, and sometimes unexpected demands require support from their interpreters that is qualitatively different from the support required by systems that can be carefully hand crafted to function in constrained environments. In this paper we explore the role of conflict resolution in providing such support. Using criteria developed in the paper, we evaluate both individual conflict resolution rules and strategies that make use of several rules.

"Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We describe a rule-based system that uses a partitioned semantic network representation for the premises and conclusions." In D. A. Waterman and Frederick Hayes-Roth. 1978. Pattern-Directed Inference Systems. Academic Press, Inc., Orlando, FL, USA. pp. 203-221.