Genetic Epistemology Editor: In his recent article in AI Magazine, "AI prepares for 2001," Nils Nilsson put forward a paradigm of AI based on a declarative representation of knowledge with semantic attachments to problem-specific procedures and data structures. The author discussed various research strategies for AI and specifically a computer-individual project was introduced as an efficient way of stimulating research and advances in the basic science of AI The undertaking of such a project immediately raises some classical psychological questions. Besides the deductive versus inductive or declarative versus procedural controversials, problems related to knowledge representation and evolution in an interactive environment must be considered. I would like to present some ideas and concepts stemming from current research in Genetic Epistemology (GE), initiated by Jean Piaget, as possible contributions to AI research fields. Knowledge is a common preoccupation for GE and AI.

A five-year project, it is aimed at promoting cooperation among research centers, developing state-of-the art training and teaching programs, and demonstrating KBCS solutions to selected socioeconomic problems. The Department of Electronics, Government of India, with the assistance of the United Nations Development Program (UNDP) decided in 1986 to support a five-year project on knowledge-based computer systems (KBCSs). Seven major research and teaching centers and a number of associated institutions are involved in the project. The nodal centers in this project are the Center for the Development of Advanced Computing (Pune), the Department of Electronics (New Delhi), The Indian Institute of Science (Bangalore), The Indian Institute of Technology (Madras), the Indian Statistical Institute (Calcutta), the National Center for Software Technology (Bombay), and the Tata Institute of Fundamental Research (Bombay). The objectives are multiple but among them are to build an institutional infrastructure and promote cooperation among research centers in India; develop state-of-the-art training and teaching programs; and demonstrate specific KBCS solutions to selected socioeconomic problems encountered, in particular, in India.

Department of Computer Science Rutgers Universaty New Brunswick, New Jersey 08903 Abstract In this article we discuss a method for learning useful conditions on the application of operators during heuristic search Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving We conclude that the basic approach of learning from solution paths can be applied t,o any situation in which problems can be solved by sequential search Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them. PEOPLE LEARN FROM EXPERIENCE, and for the past 25 years, Artificial Intelligence researchers have been attempting to replicate this process. In t,his article we focus on learning in domains where search is involved. Furthermore, we will restrict our attention t,o cases in which the legal operators for a task are known, and the learning task is to determine the conditions under which those operators can be usefully applied. Once such a set of heuristically useful conditions has been discovered, search will be directed down profitable We would like to thank Jaime Carbonell and Hans Berliner for helpful comments on an earlier version of this article.

Editor's Note: AI workers have claimed for some time A partial evaluator is an interpreter that, with only partial information about a program's inputs, produces a specialized version of the program which exploits the partial information. A similar example is described in more detail in Kahn (1982b). Programming methodology in AI shares much with general programming methodology but differs in significant ways. An AI researcher does not typically understand the problem being programmed very well. An essential aspect of a very common style of doing AI research is to write programs in order to understand something better.

We report on the spring 1992 symposium on diagrammatic representations in reasoning and problem solving sponsored by the American Association for Artificial Intelligence. The symposium brought together psychologists, computer scientists, and philosophers to discuss a range of issues covering both externally represented diagrams and mental images and both psychologyand AIrelated issues. In this article, we develop a framework for thinking about the issues that were the focus of the symposium as well as report on the discussions that took place. We anticipate that traditional symbolic representations will increasingly be combined with iconic representations in future AI research and technology and that this symposium is simply the first of many that will be devoted to this topic. The emphasis of this symposium was diagrammatic (or pictorial) representations in problem solving and reasoning.

Semantic integration has been a longstanding challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration and discuss the difficulties underlying the integration process. We then describe recent progress and identify open research issues. We focus in particular on schema matching, a topic that has received much attention in the database community, but also discuss data matching (for example, tuple deduplication) and open issues beyond the match discovery context (for example, reasoning with matches, match verification and repair, and reconciling inconsistent data values).

The Fifth International Conference on Genetic Algorithms was held at the University of Illinois at Urbana-Champaign from 17-21 July 1993. Approximately 350 participants attended the multitrack conference, which covered a wide range of topics, including genetic operators, mathematical analysis of genetic algorithms, parallel genetic algorithms, classifier systems, and genetic programming. This article highlights the major themes of the conference by discussing a few papers in detail. The conference was organized by Stephanie Forrest (University of New Mexico, conference cochair and editor of the proceedings), David Goldberg (University of Illinois at Urbana-Champaign, conference cochair and local arrangements chair), and J. David Schaffer (Philips Laboratories, New York, conference cochair). Of the 240 papers submitted to the conference, 82 were accepted for oral presentation, and 37 were accepted for poster presentation.

The 1993 International Logic Programming Symposium was held in Vancouver, British Columbia, on 26-29 October. It presented the state of the art in logic programming, emphasizing the deliberate interaction with other fields, in particular, humanistic fields. Topics covered at the symposium included algorithmic analysis, programming methodologies, semantic analysis, deductive databases, and programming language design. The years of unrelenting development by these pioneers and other wonderful people have brought the field to a stage of maturity that makes more deliberate interactions with other fields possible and desirable and that gives us enough perspective to consider our field from wider viewpoints, such as the philosophical. The 1993 International Logic Programming Symposium, held in Vancouver, British Columbia, on 26-29 October, presented the state of the art in logic programming and also emphasized these other viewpoints.

Regtech startups are saving firms billions in regulatory fines and displacing manual risk and compliance with cutting-edge technology. Over 143 million Americans will be at risk of financial fraud for years following the Equifax cybersecurity breach, while an estimated 3 million Wells Fargo customers unknowingly had their digital identity stolen to open fraudulent trading accounts. It's been nearly a decade since the financial crisis exposed how a weak risk management framework and lack of governance can almost permanently debilitate even one of the strongest capital markets. Yet despite a massive regulatory overhaul following the crisis, recent incidents show just how vulnerable the industry still is when it comes to hackers, fraud, and mismanagement. Looking at the top breaches since the financial crisis highlights some of the impacts that major gaps in regulation have on consumers.

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multiattribute) structure. We give an introduction to preference handling in combinatorial do - mains in the context of collective decision making and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, which is known as computational social choice and which brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.