Abstract not found

The efficiency of the ##-algorithm as a minimax search procedure can be attributed to its effective pruning at so-called cut-nodes; ideally only one move is examined there to establish the minimax value. This paper explores the benefits of investing additional search effort at cut-nodes by also expanding some of the remaining moves. Our results show a strong correlation between the number of promising move alternatives at cut-nodes and a new principal variation emerging. Furthermore, a new forward-pruning method is introduced that uses this additional information to ignore potentially futile subtrees. We also provide experimental results with the new pruning method in the domain of chess.

With the continuing price-performance improvement of small computers there is growing interest in looking again at some of the heuristic techniques developed for problemsolving and planning programs, to see if they can be enhanced or replaced by more algorithmic methods. The application of raw computing power, while and anathema to some, often provides better answers than is possible by reasoning or analogy. Thus brute force techniques form a good basis against which to compare more sophisticated methods designed to mirror the human deductive process. One source of extra computing power comes through the use of parallel processing on a multicomputer, an so this aspect is also covered here. Here we review the development of heuristic algorithms for application in singleagent and adversary games. We provide a detailed study of iterative deepening A* and its many variants, and show how effective various enhancements, including the use of refutation lines and a transposition table, can be....

In this article we review standard null-move pruning and introduce our extended version of it, which we call verified null-move pruning. In verified null-move pruning, whenever the shallow null-move search indicates a fail-high, instead of cutting off the search from the current node, the search is continued with reduced depth. Our experiments with verified null-move pruning show that on average, it constructs a smaller search tree with greater tactical strength in comparison to standard null-move pruning. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, verified null-move pruning manages to detect most zugzwangs and in such cases conducts a re-search to obtain the correct result. In addition, verified null-move pruning is very easy to implement, and any standard null-move pruning program can use verified null-move pruning by modifying only a few lines of code.

Many decades ago, Japanese professional Go-Moku players stated that Go-Moku (Five-in-a-row on a horizontally placed 15×15 board) is a won game for the player to move first. So far, this claim has never been substantiated by (a tree of) variations or by a computer program. Meanwhile many variants of Go-Moku with slightly different rules have been developed. This paper shows that for two common variants the game-theoretical value has been established. Moreover, the Go-Moku program Victoria is described. It uses two new search techniques, threat-space search and proof-number search. One of the results is that Victoria is bound to win against any (optimal) counterplay if it moves first. Furthermore, it achieves good results as a defender against non-optimally playing opponents. In this contribution we focus on threat-space search and its advantages compared to conventional search algorithms. 1.

. In this paper we will describe some of the basic techniques that allow computers to play chess like human grandmasters. In the first part we will give an overview about the sequential algorithms used. In the second part we will describe the parallelization that has been developed by us. The resulting parallel search algorithm has been used successfully in the chess program Zugzwang even on massively parallel hardware. In 1992 Zugzwang became Vize World Champion at the Computer Chess Championships in Madrid, Spain, running on 1024 processors. Moreover, the parallelization proves to be flexible enough to be applied successfully to the new Zugzwang program, although the new program uses a different sequential search algorithm and runs on a completely different hardware. 1 Introduction The game of chess is one of the most fascinating two-person zero-sum games with complete information. Besides of being one of the oldest games of this kind it is still played by millions of people all ove...

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for computer chess. Our results show that using an appropriate mentor, we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program with a smaller number of parameters in its evaluation function to mimic the behavior of a superior mentor which uses a more extensive evaluation function. In principle, our mentor-assisted approach could be used in a wide range of problems for which appropriate mentors are available.

Abstract. In this paper we review the conventional versions of nullmove pruning, and present our enhancements which allow for a deeper search with greater accuracy. While the conventional versions of nullmove pruning use reduction values of R ≤ 3, we use an aggressive reduction value of R = 4 within a verified adaptive configuration which maximizes the benefit from the more aggressive pruning, while limiting its tactical liabilities. Our experimental results using our grandmasterlevel chess program, Falcon, show that our null-move reductions (NMR) outperform the conventional methods, with the tactical benefits of the deeper search dominating the deficiencies. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, NMR is impervious to zugzwangs. Finally, the implementation of NMR in any program already using null-move pruning requires a modification of only a few lines of code. 1

This paper overviews the Cilk language, illustrating how Cilk supports the programming of parallel game-tree search and other chess mechanisms

In this paper we present a new conspiracy number search algorithm (CNS), called Controlled Conspiracy Number Search (CCNS). The basic steps of any CNS algorithm, the selection, the expansion, and the backup of results have been modified compared to other CNS algorithms. The selection is done by assigning demands, so called CN targets, to the nodes of the tree in a top-down fashion. By this, a set of leaves is selected in a single selection phase. The expansion is used to check, whether or not a leaf node can fulfill its demand. The backup uses heuristic information gained from the expansion step to prepare the tree for the next selection phase. As a result, our algorithm is stronger than the ff-fi -algorithm in tactical positions. This is shown by comparing them on a set of test positions. It is able to play even in nontactical positions, as shown on the 4:IPC 3 ; where Ulysses CCN, a program based on the CCNS algorithm, played a complete tournament. In addition, since in every selec...