It creates some plans and tries to execute them. It analyses the situations deeper in the tree only if the plan fails. In that case it generates new plans correcting what is wrong in the old one. So, the program considers only natural branches of the tree. It can find combinations for which it is necessary to look more than twenty ply ahead. The paper describes the methods used for analyzing a situation and for modifying unsuccessful plans. Then we examine some results found by the program.

Rote learning.We can keep all the situations already found. With each situation we store an indication on its interest or the move which has to be played. Samuell gives an example of such an application. This can be done if there are not too many possible situations. Even in games where there are many possible situations, this method can be useful for the beginning or the end of the games. We can improve this method: if the rulesare the same for all the players, we can standardize the situations: we assume that it is always the same player who has to play; for instance, at chess, white. We just keep half of the possible situations. At Go Moku where there are two axes of symetry, we just keep a quarter of the situations. But even with these improvements, there are many cases where this method is not useful because there are too many situations. It is doubtful that we can have good results in the middle game 398 at chess with such methods. We can try to generalize what has been done in a situation to another similar situation. For example in the second situation we play the same move than in the first one. Waterman2 has written an interesting progrpi playing poker. Let us describe it roughly. A situation is described by the value of seven variables: value of the program's hand, amount of money in the pot, measure of conservative style by the opponent... The program defines a partition of the set of possible values of these variables. For instance: If our hand is excellent, bet low if the opponent tends to be a conservative player and has just bet low. The problem is to define wisely these subsets. This can be done by the program which improves progressively the quality of the partition. This method is good for poker and it obtained very good results. But it is difficult to see how we can use it in a game like chess. How could we evaluate the similarity between situations, such as in similar situations we have to play the same move? A different position of a pawn can destroy a combination.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE

References are to text of chapters, not to the bodies of bibliographies.

Donald Michie Volumes 1 --7 are published by Edinburgh University Press and in the United States by Halsted Press (a subsidiary of John Wiley & Sons, Inc.) Volumes 8 -- 9 are published by Ellis Horwood Ltd., Publishers, Chichester and in the United States by Halsted Press (a subsidiary of John Wiley & Sons, Inc.) MACHINE INTELLIGENCE 9 New York - Chichester - Brisbane - Toronto First published in 1979 by ELLIS HORWOOD LIMITED Market Cross House, Cooper Street, Chichester, West Sussex, P019 lEB, England The publisher's colophon is reproduced from James Gillison's drawing of the ancient Market Cross, Chichester No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form of by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission. One intelligent approach to prefaces -- is to have the empty preface. The well prepared reader will form a good idea of the technical programme just from looking at the table of contents; together with the names of the authors, this gives him a good idea of what happened at the symposium. I could try to assess the tallcs and direct the reader's attention to the more interesting communications. But I fear this would be too subjective and unfair to the remaining authors -- all of them equally represented in this book. However, recalling that Spring week in Repino, a resort 20 kilometres from Leningrad on the Bay of Finland and unpopulated at that time of year, I have come to the definite conclusion that the scientific meeting was in its own way unique.

A new predicate calculus deduction system based on production rules is proposed. The system combines several developments in Artificial Intelligence and Automatic Theorem Proving research including the use of domain-specific inference rules and separate mechanisms for forward and backward reasoning. It has a clean separation between the data base, the production rules, and the control system. Goals and subgoals are maintained in an AND/OR tree structure. We introduce here a structure that is the dual of the AND/OR tree to represent assertions. The production rules modify these structures until they "connect" in a fashion that proves the goal theorem. Unlike some previous systems that used production rules, ours is not limited to rules in Horn Clause form. Unlike previous PLANNER-like systems, ours can handle the full range of predicate calculus expressions including those with quantified variables, disjunctions, and negations.

For the past ten years we have been working on the problem of getting a computer to understand natural language.

Distributed systems are multi-processor information processing systems which do not rely on the central shared memory for communication. The importance of distributed systems has been growing with the advent of "computer networks" of a wide spectrum: networks of geographically distributed computers at one end, and tightly coupled systems built with a large number of inexpensive physical processors at the other end. Both kinds of distributed system are made available by the rapid progress in the technology of large-scale integrated circuits. Yet little has been done in the research on semantics and programming methodologies for distributed information processing systems. Our main research goal is to understand and describe the behaviour of such distributed systems in seeking the maximum benefit of employing multi-processor computation schemata.

Success of a knowledge-based program depends on both competence and acceptability. It must perform well for it to be worth using, but is must be acceptable to users for it to be used. There are many dimensions to developing competent and acceptable knowledge based systems which can serve as "intelligent assistants" for problem solvers in science (see Shortliffe and Davis, 1975). One of these is the old AI problem of representation of knowledge. Since most previous work on representation has stressed its importance for problem-solving (e.g.

A model for adaptive problem solving applied to natural language acquisition.