Massachusetts Institute Technology Press

In IJCAI-81, p. 1064.

IEEE Transactions on Systems, Man and Cybernetics, 11

Summary of PhD thesis, Computer Science Department, Stanford University, January, 1980,Stanford Rep. Nos. HPP-80-2, STAN-CS-80-784

"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." IEEE Transactions on Systems, Man, and Cybernetics, SMCll(l):61-70. PDF: http://www.reidgsmith.com/Frameworks_for_Cooperation_in_Distributed_Problem_Solving_Jan-1981.pdf.

We have introduced the notion of the closed world assumption for deductive question-answering. This says, in effect, "Every positive statement that you don't know to be true may be assumed false". We have then shown how query evaluation under the closed world assumption reduces to the usual first order proof theoretic approach to query evaluation as applied to atomic queries. Finally, we have shown that consistent Horn data bases remain consistent under the closed world assumption and that definite data bases are consistent with the closed world assumption. ACKNOWLEDGMENT This paper was written with the financial support of the National Research Council of Canada under grant A7642. Much of this research was done while the author was visiting at Bolt, Beranek and Newman, Inc., Cambridge, Mass. I wish to thank Craig Bishop for his careful criticism of an earlier draft of this paper.

Technical report CMU-CS-81-135, Computer Science Department, Carnegie-Mellon University. "This is a combination introductory and reference manual for OPS5, a programming language for production systems. OPS5 is used primarily for applications in the areas of artificial intelligence, cognitive psychology, and expert systems. OPS5 interpreters have been implemented in LISP and BLISS."

"Practice, and the performance improvement that it engenders, has long been a major topic in psychology. In this paper, both experimental and theoretical approaches are employed in an investigation of the mechanisms underlying this improvement On the experimental side, it is argued that a single law, the power law of practice, adequately describes all of the practice data. On the theoretical side, a model of practice rooted in modern cognitive psychology, the chunking theory of learning, is formulated. The paper consists of (1) the presentation of a set of empirical practice curves; (2) mathematical investigations into the nature of power law functions; (3) evaluations of the ability of three different classes of functions to adequately model the empirical curves; (4) a discussion of the existing models of practice; (5) a presentation of the chunking theory of learning." In J. R. Anderson (Ed.). Cognitive Skills and their Acquisition (pp. 1-55). Hillsdale, NJ: Erlbaum.

A four-volume collection of articles on all the major topics of AI at that time, with an extensive bibliography. Vol I (Avron Barr and Edward A. Feigenbaum, 1981) (https://books.google.com/books?isbn=1483214370). Vol II (Avron Barr, Edward A. Feigenbaum, Paul R. Cohen, 1982) (https://books.google.com/books?isbn=1483214389). Vol III (Paul R. Cohen and Edward A. Feigenbaum, 1982) (https://books.google.com/books?isbn=1483214397). Vol IV (Avron Barr and Paul R. Cohen, 1989) (https://books.google.com/books?isbn=1483214370). Reading, Mass.: Addison-Wesley.

Presented at the NATO Symposium Human and Artificial Intelligence, Lyon, France, October, 1981. CMU Technical Report CMU-CS-82-104. We examine computer games in order to develop concepts of the relative roles of knowledge and search. The paper concentrates on the relation between knowledge applied at leaf nodes of a search and the depth of the search that is being conducted. Each knowledge of an advantage has a projection ability (time to convert to a more permanent advantage) associated with it. The best programs appear to have the longest projection ability knowledge in them. If the application of knowledge forces a single view of a terminal situation, this may at times be very wrong. We consider the advantages of knowledge delivering a range as its output, a method for which some theory exists, but which is as yet unproven.