A computer model of chess memory

Chess research provides rich data for testing computational models of human memory. This paper presents a model which shares several common concepts with an earlier attempt (Simon & Gilmartin, 1973), but features several new attributes: dynamic short-term memory, recursive chunking, more sophisticated perceptual mechanisms and use of a retrieval structure (Chase & Ericsson, 1982). Simulations of data from three experiments are presented: 1) differential recall of random and game positions; 2) recall of several boards presented in short succession; 3) recall of positions modified by mirror image reflection about various axes. The model fits the data reasonably well, although some empirical phenomena are not captured by it. At a theoretical level, the conceptualization of the internal representation and its relation with the retrieval structure needs further refinement

A pattern-recognition theory of search in expert problem solving

Understanding how look-ahead search and pattern recognition interact is one of the important research questions in the study of expert problem-solving. This paper examines the implications of the template theory (Gobet & Simon, 1996a), a recent theory of expert memory, on the theory of problem solving in chess. Templates are "chunks" (Chase & Simon, 1973) that have evolved into more complex data structures and that possess slots allowing values to be encoded rapidly. Templates may facilitate search in three ways: (a) by allowing information to be stored into LTM rapidly; (b) by allowing a search in the template space in addition to a search in the move space; and (c) by compensating loss in the "mind's eye" due to interference and decay. A computer model implementing the main ideas of the theory is presented, and simulations of its search behaviour are discussed. The template theory accounts for the slight skill difference in average depth of search found in chess players, as well as for other empirical data

Expertise in chess

This chapter provides an overview of research into chess expertise. After an historical background and a brief description of the game and the rating system, it discusses the information processes enabling players to choose good moves, and in particular the trade-offs between knowledge and search. Other topics include blindfold chess, talent, and the role of deliberate practice and tournament experience

Data Structure for the Use of Patterns in the Perceptual Ordering of the Game of Chess

It is well known that the way one perceives a problem can influence the difficulty of solving the problem in a profound way. In the case of computer chess playing programs, one finds that most programs perceive the game in much the same way. They are all based on Shannon\u27s original proposal for chess playing programs. His approach was to generate all of the possible combinations of moves up to a certain number of plays and then a subset of all combinations to a deeper level thereafter. Each of these moves would then be evaluated as to its relative worth. This paper lays the foundation for research in an alternate method of approaching the game based on how human experts perceive the board initially. A suitable data structure for this is then proposed and discussed

A data structure for the use of patterns in the perceptual ordering of the game of chess

It is well known that the way one perceives a problem can influence the difficulty of solving the problem in a profound way. In the case of computer chess playing programs, one finds that most programs perceive the game in much the same way. They are all based on Shannon\u27s original proposal for chess playing programs. His approach was to generate all of the possible combinations of moves up to a certain number of plays and then a subset of all combinations to a deeper level thereafter. Each of these moves would then be evaluated as to its relative worth. This paper lays the foundation for research in an alternate method of approaching the game based on how human experts perceive the board initially. A suitable data structure for this is then proposed and discussed --Abstract, page ii

Recherche et reconnaissance de patterns chez les experts

Ce papier va d'abord donner un bref aperçu des recherches de Simon en psychologie, et va ensuite se concentrer sur ses travaux concernant la psychologie des experts. Les rôles respectifs de la recherche et de la reconnaissance de patterns, ainsi que leur interaction, seront investigués, en particulier avec ses travaux sur les joueurs d'échecs, et des implications pour l'intelligence artificielle seront tirées

Team diversity and categorization salience : capturing diversity-blind, intergroup biased, and multicultural perceptions

It is increasingly recognized that team diversity with respect to various social categories (e.g., gender, race) does not automatically result in the cognitive activation of these categories (i.e., categorization salience), and that factors influencing this relationship are important for the effects of diversity. Thus, it is a methodological problem that no measurement technique is available to measure categorization salience in a way that efficiently applies to multiple dimensions of diversity in multiple combinations. Based on insights from artificial intelligence research, we propose a technique to capture the salience of different social categorizations in teams that does not prime the salience of these categories. We illustrate the importance of such measurement by showing how it may be used to distinguish among diversity-blind responses (low categorization salience), multicultural responses (positive responses to categorization salience), and intergroup biased responses (negative responses to categorization salience) in a study of gender and race diversity and the gender by race faultline in 38 manufacturing teams comprising 239 members

Problem-solving skills utilized by graduating engineers from a baccalaureate program to solve problems

This study extends the knowledge of the problem-solving frameworks and skills used by graduating engineers (GE). The frameworks are comprehensive systematic processes that a GE uses to frame the problem. The problem-solving skills are mental and physical mechanisms, such as heuristics and flow charts. An in-depth qualitative study of 31 randomly selected graduating engineers from a state university was employed to obtain firsthand data on how GEs solved a specific problem scenario. The methodology of this study was an open running dialog between the researcher and the GE while the GE solved the standardized problem scenario. A one-sentence problem scenario was provided. The GE asked questions of the researcher throughout the process. The researcher responded with an established set of answers. The researcher timed and coded the GE\u27s responses. The running dialogue was coded utilizing the problem-solving frameworks and skills for each of the 31 GEs. Time ranged from 1 minute and 10 seconds to 9 minutes and 48 seconds. Of the GEs, 90% utilized 2 or 3 of the 4 types of frameworks in different combinations. However, 1 student applied only 1 framework and 2 students utilized all 4 frameworks. The 5 GEs who most rapidly solved the problem used different combinations of the frameworks. The evaluation step in any of the 4 frameworks was not implemented by 48% of the GEs. The choice of frameworks did not appear to be related to gender, age, or experience. The analysis indicated that the GEs also used different arrangements of problem-solving skills. The GEs employed all 5 categories of problem-solving skills: tools, defining, goal-identification, heuristics, and reasoning. Of the GEs, 100% utilized the skills termed tools, defining, and goal-identification. The heuristic problem-solving skills were used by 97% and the reasoning skills were used by 26% of the GEs. All of the GEs required a wide range of problem-solving frameworks and skills in order to solve effectively the problem scenario. Thus, engineering educators should provide student engineers with a wide range of frameworks and skills of problem-solving in order to provide a strong basis for their future work

Solving Mechanics Problems Using Meta-Level Inference

In this paper we shall describe a program (MECHO), written in Prolog[14], which solves a wide range of mechanics problems from statements in both predicate calculus and English. Mecho uses the technique of meta-level inference to control search in natural language understanding, common sense inference, model formation and algebraic manipulation. We argue that this is a powerful technique for controlling search while retaining the modularity of declarative knowledge representations