In Gallaire, H. and Minker, J. (Eds.), Logic and Data Bases, pp. 293-322. Plenum.

Computer J. 21:149-153

Xerox PARC, Palo Alto, Calif.

"The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. The research has been conducted within many different scientific communities, however, and these terms have come to have a variety of meanings. It is therefore often difficult to recognize that problems which are described differently may in fact be identical. Learning system models as well are often tuned to the require- ments of a particular discipline and are not suitable for application in related disciplines."In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker

Semantic networks constitute one of the many attempts to capture human knowledge in an abstraction suitable for processing by computer program. While semantic nets enjoy widespread popularity, they seem never to live up to their authors' expectations of expressive power and ease of construction. This paper examines the fundamentals of network notation, in order to understand why the “formalism” has not been the panacea it was once hoped to be. We focus here on “concepts”—what net-authors think they are, and how network nodes might represent them. The simplistic view of concept nodes as representing extensional sets is examined, and found wanting in several respects.

A wide range of segmentation techniques continues to evolve in the literature on scene analysis. Many of these approaches have been constrained to limited applications or goals. This survey analyzes the complexities encountered in applying these techniques to color images of natural scenes involving complex textured objects. It also explores new ways of using the techniques to overcome some of the problems which are described. An outline of considerations in the development of a general image segmentation system which can provide input to a semantic interpretation process is distributed throughout the paper.

This paper describes a problem-solving framework in which aspects of mathematical decision theory are incorporated into symbolic problem-solving techniques currently predominant in artificial intelligence. The utility function of decision theory is used to reveal tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility function on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed. These techniques are illustrated by application to the classic monkey and bananas problem.

GUS is the first of a series of experimental computer systems that we intend to construct as part of a program of research on language understanding. In large measure, these systems will fill the role of periodic progress reports, summarizing what we have learned, assessing the mutual coherence of the various lines of investigation we have been following, and suggesting where more emphasis is needed in future work. GUS (Genial Understander System) is intended to engage a sympathetic and highly cooperative human in an English dialog, directed towards a specific goal within a very restricted domain of discourse. As a starting point, GUS was restricted to the role of a travel agent in a conversation with a client who wants to make a simple return trip to a single city in California. There is good reason for restricting the domain of discourse for a computer system which is to engage in an English dialog.

By placing various restrictions on the heuristic estimator it is possible to constrain the heuristic search process to fit specific needs. This paper introduces a new restriction upon the heuristic, called the “bandwidth” condition, that enables the ordered search to better cope with time and space difficulties. In particular, the effect of error within the heuristic is considered in detail. Beyond this, the bandwidth condition quite naturally allows for the extension of the heuristic search to MIN/MAX trees. The resulting game playing algorithm affords many desirable practical features not found in minimax based techniques, as well as maintaining the theoretical framework of ordered searches.

We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints.