Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations. To comprehensively investigate the faithfulness and reliability of entity typing methods, we first systematically define distinct kinds of model biases that are reflected mainly from spurious correlations. Particularly, we identify six types of existing model biases, including mention-context bias, lexical overlapping bias, named entity bias, pronoun bias, dependency bias, and overgeneralization bias. To mitigate model biases, we then introduce a counterfactual data augmentation method. By augmenting the original training set with their debiased counterparts, models are forced to fully comprehend sentences and discover the fundamental cues for entity typing, rather than relying on spurious correlations for shortcuts. Experimental results on the UFET dataset show our counterfactual data augmentation approach helps improve generalization of different entity typing models with consistently better performance on both the original and debiased test sets.

R B. Abhyankar Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, Computing with Logic Logic Programming with Prolog (The Benjamin Cummings Publishing Company, Menlo Park, Calif., 1988, 535 pp., $27 95) by David Maier and David S. Warren, respected researchers in logic programming, is a superb book Offering an in-depth treatment of advanced topics, the book also includes the necessary background material on logic and automatic theorem proving, making it self-contained. The only real prerequisite is a first course in data structures, although it would be helpful if the reader has also had a first course in program translation. The book has a wealth of exercises and would make an excellent textbook for advanced undergraduate or graduate students in computer science; it is also appropriate for programmers interested in the implementation of Prolog The book presents the concepts of logic programming using theory presentation, implementation, and application of Proplog, Datalog, and Prolog, three logic programming languages of increasing complexity that are based on horn clause subsets of propositional, predicate, and functional logic, respectively This incremental approach, unique to this book, is effective in conveying a thorough understanding of the subject The book consists of 12 chapters grouped into three parts (Part 1 chapters 1 to 3, Part 2. chapters 4 to 6, and Part 3 chapters 7 to 12), an appendix, and an index The three parts, each dealing with one of these logic programming languages, are organized the same First, the authors informally present the language using examples; an interpreter is also presented. Then the formal syntax and semantics for the language and logic are presented, along with soundness and completeness results for the logic and the effects of various search strategies Next, they give optimization techniques for the interpreter Each chapter ends with exercises, brief comments regarding the material in the chapter, and a bibliography Chapter I presents top-down and bottom-up interpreters for Proplog Chapter 2 offers a good discussion of the related notions: negation as failure, closed-world assumption, minimal models, and stratified programs Chapter 3 considers clause indexing and lazy concatenation as optimization techniques for the Proplog interpreter in chapter 1 Chapter 4 explains the connection between Datalog and relational algebra. Chapter 5 contains a proof of Herbrand's theorem for predicate logic.

Artificial Intelligence for Microcomputers If you would like to develop an expert system or knowledgebased system on a microcomputer, you might want to read Artijcial Intelligence for Microcomputers by Mickey Williamson, This nontechnical book is easy to understand, written for the unsophisticated microcomputer user. The first chapters provide a brief history of artificial intelligence (AI) and an introduction to natural language query systems. They explain what knowledge-based systems and expert systems are and how they work. Discussions are also provided of the two major AI programming languages, Lisp and Prolog, including their strengths and weaknesses. The remainder of the book is devoted to a review of some of the existing AI software products for microcomputers, such as natural language query systems, decision support systems, expert system development shells, and AI programming languages.

His proofs are ingenious, cleverly argued, quite convincing to many of his contemporaries, and utterly wrong. The Simon Newcomb Award is given annually for the silliest published argument attacking AI. Our subject may be unique in the virulence and frequency with which it is attacked, both in the popular media and among the cultured intelligentsia. Recent articles have argued that the very idea of AI reflects a cancer in the heart of our culture and have proven (yet again) that it is impossible. While many of these attacks are cited widely, most of them are ridiculous to anyone with an appropriate technical education.

However, recently, there seems to be a new wave of interest, as indicated by many papers, monographs, edited books, and doctoral theses, in exploring aspects of similarity and analogical reasoning from various perspectives. Amid these numerous publications, Similarity and Analogical Reasoning surely stands out as the most valuable reference work on the topic, covering especially well the recent advances in the understanding of this topic, with many chapters written by leading researchers. Although it is based on a collection of papers initially presented at the Workshop on Similarity and Analogy, unlike the typical workshop proceedings, this volume is well edited and coherent in both its content and format, with a great deal of cross-references and detailed summary-comment chapters for every part of the book. Let us look at the book in detail. Because each of these chapters has a different perspective, approach, and organization, I first discuss a number of chapters one by one.

How do we form categories? What is the role of similarity in categorization? Can we formalize the answers to these questions to derive further insights and develop useful software systems? These were the questions addressed at an interdisciplinary meeting attended by psychologists, computer scientists, anthropologists, statisticians, and philosophers held at the University of Edinburgh. The edited volume Similarity and Categorization arises from this meeting. The publication of Similarity and Categorization is timely because the study of categorization is at a theoretical crossroads.

This book is a collection of many of the seminal papers from the first decade of research in artificial intelligence in medicine (AIM). The editors state that the need for such a collection became evident when a two-day AIM tutorial was held at Stanford in 1980, following the annual national AIM research workshop. The 19 papers included in the book are each introduced by a short section written by the editors. Typically one page in length, these introductory sections are designed to place the paper into context in the field. In addition, the editors have included introductory and concluding chapters of their own.

You are cordially invited to become a member of the AI Community's principal scientific society: Both these facts run counter to other connectionist models but easily fit SDM. Sparse Distributed Memory will be of interest to anyone doing research in neural models or brain physiology. As the theory is refined, the book will also be of interest to those trying to find applications for neural models. Finally, it will be fascinating to anyone who is even slightly curious about human intelligence and how it might arise from the brain. Terry Rooker is a graduate student at the Oregon Graduate Institute.

The two-volume set entitled Knowledge-Based Systems (Volume 1, Knowledge Acquisition for Knowledge-Based Systems, 355 pp., and Volume 2, Knowledge Acquisition Tools for Expert Systems, 343 pp., Academic Press, San Diego, California, 1988), edited by B. R. Gaines and J. H. Boose, is an excellent collection of papers useful to both commercial practitioners of knowledge-based-systems development and research-oriented scientists at specialized centers or academic institutions. The set is the result of a call for papers to support the first American Association for Artificial Intelligence Knowledge Acquisition for Knowledge-Based Systems Workshop, held 3-7 November 1986 in Banff, Canada. Although the conference was held three years ago, these volumes are still timely and sorely needed. Few books dedicated to knowledge acquisition exist. The first volume, Knowledge Acquisition for Knowledge-Based Systems, begins with a paper whose title sounds appropriate: "An Overview of Knowledge Acquisition and Transfer" by the editor B. R. Gaines.

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.