A full book, available for free in PDF form.From the preface:A major problem in artificial intelligence is to endow computers with commonsense knowledge of the world and with the ability to use that knowledge sensibly. A large body of research has studied this problem through careful analysis of typical examples of reasoning in a variety of commonsense domains. The immediate aim of this research is to develop a rich language for expressing commonsense knowledge, and inference techniques for carrying out commonsense reasoning. This book provides an introduction and a survey of this body of research. It is, to the best of my knowledge, the first book to attempt this.The book is designed to be used as a textbook for a one-semester graduate course on knowledge representation.Morgan Kaufmann

Databases are at the heart of most real-world knowledge base systems. The management and effective use of these databases will be the limiting factors in our ability to build ever more complex AI systems. This article reports on a workshop that explored how databases and their associated technologies can best be used in the development of large AI applications.

Text planning is one of the most rapidly growing subfields of language generation. Until the 1988 AAAI conference, no workshop has concentrated on text planning and its relationship to realiza-tion. This report is a summary of that workshop.

Largely from this Traditionally, systems that automatically and realization--was widely experience, we came to understand generate natural language have deemed more convenient than accurate: the sorts of tasks that a text planner been conceived as consisting of two The components of a generator has to perform: determining which principal components: a text planner should be able to communicate at elements to say, coherently structuring and a realization grammar. Recent any level where their information is the input elements, building advances in the art, especially in the applicable.

The A.T. Nonmonotonic Workshop multisubmission paper policy by Anderson Memorial Scholarship Program The third international workshop on IJCAI was deferred until the of the American Indian Science August meeting.

This article records the results of an experiment in which a survey of AI and expert systems (ES) literature was attempted using Science Citation Indexes. The survey identified a sample of authors and institutions that have had a significant impact on the historical development of AI and ES. However, it also identified several glaring problems with using Science Citation Indexes as a method of comprehensively studying a body of scientific research. Accordingly, the reader is cautioned against using the results presented here to conclude that author A is a better or worse AI researcher than author B.

Knowledge Acquisition, 1[#1]:3-37

ABSTRACT Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of compet- ing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems. Artificial Intelligence, 40 (1-3), 235-82.

A novel approach is presented for the development of expert systems for structural design problems. This approach differs from the conventional expert systems in two fundamental respects. First, mathematical optimization is introduced into the design process. Second, a computer is used to obtain parts of the knowledge necessary in the expert systems in addition to heuristics and experiential knowledge obtained from documented materials and human experts. As an example of this approach, a prototype coupled expert system, the bridge truss expert (BTExpert), is presented for optimum design of bridge trusses subjected to moving loads. BTExpert was developed by interfacing an interactive optimization program developed in Fortran 77 to an expert system shell developed in Pascal. This new generation of expert systems-embracing various advanced technologies such as AI (machine intelligence), the numeric optimization technique, and interactive computer graphics -- should find enormous practical implications.

Within the academic and professional auditing communities, there has been growing concern about how to accurately assess the various risks associated with performing an audit. These risks are difficult to conceptualize in terms of numeric estimates. This article discusses the development of a prototype computational model (computer program) that assesses one of the major audit risks -- inherent risk. This program bases most of its inferencing activities on a qualitative model of a typical business enterprise.