Results


Using Patterns and Plans in Chess

Classics (Collection 2)

The purpose of this research is to investigate the extent to which knowledge can replace and support search in selecting a chess move and to delineate the issues involved. This has been carried out by constructing a program. PARADISE (PArtern Recognition Applied to Directing SEarch), which finds the best move in tactically sharp middle game positions from the games of chess masters. The actions of the rules post concepts in the data base while the conditions match patterns in the chess position and data base. The program uses the knowledge base to discover plans during static analysis and to guide a small tree search which confirms that a particular plan is best.


The B* Tree Search Algorithm: A Best-First Proof Proceduret

Classics (Collection 2)

ABSTRACT In this paper we present a new algorithm for searching trees. It does this by attempting to find both the best arc at the root and the simplest proof, in best-first fashion. This strategy determines the order of node expansion. Any node that is expanded is assigned two values: an upper (or optimistic) bound and a lower (or pessimistic) bound. During the course of a search, these bounds at a node tend to converge, producing natural termination of the search.


A Reprint from

Classics (Collection 2)

THIRD LONDON SYMPOSIUM Papers read at a Symposium on'Information Theory' held at the Royal Institution, London, September 12th to 16th 1955 Published by BUTTERWORTHS SCIENTIFIC PUBLICATIONS 88 KINGSWAY, LONDON, W.C.2 33 PATTERN RECOGNITION AND LEARNING* Massachusetts Institute of Technology, U.S.A. MANY psychologists studying learning have assumed that the subject--rat, dog, or graduate student--invariably knows what the stimulus is. They have not concerned themselves with how a dog knows that it is the bell ringing which is the stimulus to jump over a fence. A bell ringing never gives the same set of nervous impulses into the brain twice (of course the argument would still apply even if it did); why then should the dog classify all cases of bell ringing into one category--'stimulus'? There is then the further question of how this category is more or less quickly'associated' with a response: the point is that the stimulus is not a priori considered a significant entity by the subject. In designing programmes for computers to imitate conditioned reflexes, for instance, we have found that the real problem was to identify the stimulus.


Offprint from Artificial Intelligence and Heuristic Programming Edinburgh University Press 1971

Classics (Collection 2)

A general game-playing program must know the rules of the particular playing game. These rules are: (1) an algorithm indicating the winning state; (2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation. There are two means of giving these rules: (1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or'you do not win', or the list of legal moves.


CC Al

Classics (Collection 2)

First we examine the vocabulary and the syntax. Then we go through the various objects appearing in this context: pieces, areas, sides, moves, situations, knowledge about chess, people, time, events, judgements, changes, explanations, goals, consequences and reasons. We also consider how the information about an object is put together. Finally we survey the consequences of this analysis for the realization of chess programs. I] presented a systematic account of the facts of thought and of the methods to express them.


A Chess Combination Program Which Uses Plans

Classics (Collection 2)

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.


Computer Oriented Learning Processes

Classics (Collection 2)

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.


A Bibliography of Computer Chess

Classics (Collection 2)

The following is a fairly comprehensive list of English language articles on computer chess. Although works about related games like checkers and GO have been excluded, it would be wrong not to refer here to A. L. Samuel's early masterpiece "Some studies in machine learning using the game of checkers" In McDonell, whose general assistance was much appreciated. Many people reviewed and commented upon early drafts, the comments and observations by Max Bramer and Hartmut Tanke being especially valuable. Readers may also be interested in the excellent annotated bibliography by Harald Relcsten [259], whose reviews include not only quotations and paraphrased abstracts, but interesting observations. For computer chess works in other languages, especially German and Russian, a revised version of the bibliography by Egbert Meissenberg "Schach liche leistungen von computer", Deutsche Schachblaetter (1968), 1-4, is reputed to be the most correct.



8 A Theory of Advice

Classics (Collection 2)

Machine intelligence problems are sometimes defined as those problems which (i) computers can't yet do, and (ii) humans can. We shall further consider how much "knowledge" about a finite mathematical function can, on certain assumptions, be credited to a computer program. Although our approach is quite general, we are really only interested in programs which evaluate "semihard" functions, believing that the evaluation of such functions constitutes the defining aspiration of machine intelligence work. If a function is less hard than "semihard," then we can evaluate it by pure algorithm (trading space for time) or by pure lookup (making the opposite trade), with no need to talk of knowledge, advice, machine intelligence, or any of those things. We call such problems "standard."