Abstract — Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS’s) (e.g., syntax versus acoustics in the case of a speech-understanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered. I. DISTRIBUTED PROBLEM SOLVING

Ideally, definitions induced from examples should consist of all, and only, disjuncts that are meaningful (e.g., as measured by a statistical significance test) and have a low error rate. Existing inductive systems create definitions that are ideal with regard to large disjuncts, but far from ideal with regard to small disjuncts, where a small (large) disjunct is one that correctly classifies few (many) training examples. The problem with small disjuncts is that many of them have high rates of misclassification, and it is difficult to eliminate the error-prone small disjuncts from a definition without adversely affecting other disjuncts in the definition. Various approaches to this problem are evaluated, including the novel approach of using a bias different than the "maximum generality" bias. This approach, and some others, prove partly successful, but the problem of small disjuncts remains open.

. One of the defining challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, and compares existing work on scaling up inductive algorithms. We concentrate on algorithms that build decision trees and rule sets, in order to provide focus and specific details; the issues and techniques generalize to other types of data mining. We begin with a discussion of important issues related to scaling up. We highlight similarities among scaling techniques by categorizing them into three main approaches. For each approach, we then describe, compare, and contrast the different constituent techniques, drawing on specific examples from published papers. Finally, we use the preceding analysis to suggest how to proceed when dealing with a large problem, and where to focus future research. Keywords: scaling up, inductive learning, decision trees, rule learning 1. Introduction The knowledge discovery and data...

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In this paper, we review AI research on computational discovery and its recent application to the discovery of new scientific knowledge. We characterize five historical stages of the scientific discovery process, which we use as an organizational framework in describing applications. We also identify five distinct steps during which developers or users can influence the behavior of a computational discovery system. Rather than criticizing such intervention, as done in the past, we recommend it as the preferred approach to using discovery software. As evidence for the advantages of such human-computer cooperation, we report seven examples of novel, computer-aided discoveries that have appeared in the scientific literature. We consider briefly the role that humans played in each case, then examine one such interaction in more detail. We close by recommending that future systems provide more explicit support for human intervention in the discovery process. Running head: Computational Sci...

: Each year, one of the explicit challenges for the KDD research community is to develop methods that facilitate the use of inductive learning algorithms for mining very large databases. By collecting, categorizing, and summarizing past work on scaling up inductive learning algorithms, this paper serves to establish a common ground for researchers addressing the challenge. We begin with a discussion of important, but often tacit, issues related to scaling up learning algorithms. We highlight similarities among methods by categorizing them into three main approaches. For each approach, we then describe, compare, and contrast the different constituent methods, drawing on specific examples from the published literature. Finally, we use the preceding analysis to suggest how one should proceed when dealing with a large problem, and where future research efforts should be focused. Primary contact: Foster Provost NYNEX Science and Technology, 400 Westchester Avenue, White Plains, NY 10604 em...

When the terms “intelligence ” or “intelligent ” are used by scientists, they are referring to a large collection of human cognitive behaviors—people thinking. When life scientists speak of the intelligence of animals, they are asking us to call to mind a set of human behaviors that they are asserting the animals are (or are not) capable

An ability to reason at a meta-level is widely regarded as an important aspect of human creativity which is often missing from creative computer programs. We discuss recent experiments with the HR theory formation program where it formed meta-theories about previously formed theories. We report how HR re-invented aspects of how it forms theories and reflected on the nature of the theories it produces. Additionally, the meta-theories contains higher level concepts than those produced using HR normally. We discuss how HR's meta-level abilities were enabled by changing domains, rather than writing new programs, which was the model previously employed in the Meta-DENDRAL and Eurisko programs. These experiments suggest an improved model of theory formation where meta-theories are produced alongside theories, with information from the meta-theory being used to improve the search in the original theory. 1

This article is an essay on directions and methodology in computer-science oriented research on scientific discovery. The essay starts by reviewing briefly some of the history of computing in scientific reasoning, and some of the results and impact that have been achieved. The remainder analyzes some of the goals of this field, its relations with sister fields, and the practical applications of this analysis to evaluating research quality, reviewing, and methodology. An earlier review in this journal [13] analyzed scientific discovery programs in terms of their designs, achievements, and shortcomings; the focus here is research directions, evaluation, and methodology, all from the viewpoint of computer science. 2. History of Research on Scientific Discovery The early days of artificial intelligence saw various attempts to automate creative tasks of scientific and mathematical inference. Perhaps the earliest examples (on electronic computers) of symbolic mathematical or scientific inference were master's theses at MIT (J.F. Nolan) and at Temple (H.G. Kahrimanian) in 1953 on analytical differentiation in the calculus [9]. Soon after came the Logic Theorist, whose designers (A. Newell & H.A. Simon) submitted in 1958 an improved proof discovered by the program to the Journal of Symbolic Logic [5]. At around the same time, Gelernter created the Geometry Theorem Prover [7]. Starting in the 1960's, Lederberg invented an algorithm for generating molecular structures efficiently, which led to the Stanford Dendral project whose goal was to elucidate molecular structure on the basis of mass spectrograms and other experimental evidence [21]. These are some of the early events in the application of computers to problems of creative scientific and mathematical inference. A milestone ...

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