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.

In Clarke, M. R. B. (Ed.), Advances in Computer Chess 2, pp. 103-109. Edinburgh University Press

Chess Life and Review (February):84-85.

It can be seen that a great deal of worthwhile material has now been generated about computer chess. There is also quite a bit of nonsense by persons who have never built a program. Several groups with excellent programs have done little publishing, although I can hardly blame them since their work requires much time and is usually unsupported by any funding agencies. Certain staples have given rise to duplication: All but one of the books published explain the depth-first alpha-beta procedure. We expect that by far the largest portion of our readers will be scientists interested in updating their knowledge of the subject.

An analysis of the alpha-beta pruning algorithm is presented which takes into account both shallow and deep cut-offs. A formula is first developed to measure the average number of terminal nodes examined by the algorithm in a uniform tree of degree n and depth d when ties are allowed among the bottom positions: specifically, all bottom values are assumed to be independent identically distributed random variables drawn from a discrete probability distribution. A worst case analysis over all possible probability distributions is then presented by considering the limiting case when the discrete probability distribution tends to a continuous probability distribution. The branching factor of the alpha-beta pruning algorithm is shown to grow with n as Θ(n/lnn), therefore confirming a claim by Knuth and Moore that deep cut-offs only have a second order effect on the behavior of the algorithm.

Master skill--operational in the sense-t'hat it can be run on Another form of the'Master skill' aspiration aims at correct'strong mastery' in this sense is attainable for the complete None of the above listed endgames contains anything problematical from a Master's point of view and computer programs Using a vocabulary which is defined in Kmoch's (1959) 'An enemy pawn ahead on the same file is a counterpawn, Some of these relations may be very useful if developed further. For expmple, if a pawn is'overloaded', in that it is pefforming Defence Diagram, see Figure 1). A rule is applied'to a position (in a manner familiar to'forcing tree' that guarantees the achievement of better-goals The'and-or' tree search, carried out by module 1 of the AU Figure 1 The ADD corresponding to the position shown in Figure 1. The Computer Journal ' HOW DIFFICULT IS THE KNKR PROBLEM? Longest variation in Fine before capture of the Knight: 24 moves; longest known variation 27 moves.

We here discuss insights gained about the structure of evaluation functions for a large domain such as backgammon. Evaluation began as a single linear polynomial of backgammon features. Later, we introduced Mate-classes, each with its own evaluation function. This improved the play, but caused problems with odge-effects between state-classes. Our latest effort uses models of position potential to select across the set of best members of each represented state-class. "This has produced a significant jump in performance of BKG. Because of the localization of knowledge, state-classes permit relatively easy modification of knowledge used in evaluation. They also permit the building of opponent models based upon what evidence shows the opponent knows in each state-class.

In Frey, 1-33.

Chess programs can differ in depth of search or in the evaluation function applied to leaf nodes or both. Over the past 10 years, the notion that the principal way to strengthen a chess program is to improve its depth of search has held sway. Improving depth of search undoubtedly does improve a program's strength. However, projections of potential gain have time and again been found to overestimate the actual gain. We examine the notion that it is possible to project the playing strength of chess programs by having different versions of the same program (differing only in depth of search) play each other.

Philosophers and - "pseudognosticians" (the artificial intelligentsial) are coming more and more to recognize that they share common ground and that each can learn from the other. This has been generally recognized for many years as far as symbolic logic is concerned, but less so in relation to the foundations of probability. In this essay I hope to convince the pseudognostician that the philosophy of probability is relevant to his work. Formal systems, such as those used in mathematics, logic, and computer programming, can lead to deductions outside the system only when there is an input of assumptions. For example, no probability can be numerically inferred from the axioms of probability unless some probabilities are assumed without using the axioms: ex nihilo nihil fit.2