Towards the unification of intuitive and formal game concepts with applications to computer chess

In computer game development, an interesting point which has been little or no studied at all is the formalization of intuition such as game playing concepts, including playing style. This work is devoted to bridge the gap between human reasoning in game playing and heuristic game playing algorithms. The idea is motivated as follows. In most chess-like games there exist many intuition-oriented concepts such as capture, attack, defence, threaten, blocked position, sacrifice, zugzwang position and different playing styles such as aggressive, conservative, tactical and positional. Most human players use to manage these concepts, pergaps in an intuitive way, as they were not well formalized in a precise manner. A good formalization of these concepts would be an important step towards the automation of human reasoning in chess (and other strategy games) for better understanding of the game, thus leading to better playing. The goal of this research is to take a first step towards the unification of both "paradigms", namely human reasoning in game play and more formal heuristic concepts. We focus on computer chess as an example but the result could be also applied to most two-player zero-sum perfect information games. The applications of such a formulation are practical, such as better game understanding and opponent modeling, as well as educational: it would be nice to have these concepts somehow formalized. Then we suggest a way of transfering these intuitions into formal definitions. We propose an interpretation technique for describing chess positions and evaluation functions. The technique consists of interpreting and mapping part of the algorithmic scenario into quantities such as integer numbers. With such a mapping a given concept is likely to be described in a very precise way. As an application we look for candidate definitions of the following concepts: attack, defence, threat, sacrifice, zugzwang, aggressive play and defensive play. For each one of them we use the previous technique and propose a formal definition. Thus we give the first formulation of game playing styles -at least to the author\u27s knowledge- and we show how this definition goes through for the game of chess. We describe different possibilities when moving from intuition to the formal setting, varying from a simple formulation through a connectionist approach. Then we show as an application how an evaluation function can be modified in order to include a given concept. This new evaluation function should take into account the degree of presence of the given concept (eg. how defensive is a given position) and thus it can be incorporated into a computer chess program. An advantage of allowing one to modify in such a manner an evaluation function is that one can combine different evaluation functions and -perhaps- get the better of each one of them. Although this is a first step in the given direction, some more difficult tasks will remain, such as the formalization of the so called positional, strategic and tactical play. References B. Abramson. Learning expected-outcome evaluators in chess. In H. Berliner, editor, Proceedings of the AAAI Spring Symposium on Computer Game Playing, pages 26-28, Stanford University, 1988. B. Abramson. On learning and testing evaluation functions. Journal of Experimental and Theoretical Artificial Intelligence, 2(3):182-193, 1990. T. S. Anantharaman. Evaluation tuning for computer chess: Linear discriminant methods. International Computer Chess Association Journal, 20(4):224-242, 1997. E. B. Baum, Warren D. Smith. Best Play for Imperfect Players and Game Tree Search. 1993 J. Fürnkranz. Machine Learning in Computer Chess: The Next Generation Austrian Research Institute for Artificial Intelligence, Vienna, TR-96-11, 1996. A. Plaat, J. Schaeffer, W. Pijls and A. De Bruin. Best-First Fixed-Depth Game-Tree Search in Practice. IJCAI\u2795, Montreal. J. Schaeffer, P. Lu, D. Szafron and R. Lake. A Re-examination of Brute-Force Search Games: Planning and Learning, Chapel Hill, N.C., pp. 51-58, 1993. AAAI Report FS9302

Concepts and analogies in cybernetics: Mathematical investigations of the role of analogy in concept formation and problem solving; with emphasis for conflict resolution via object and morphism eliminations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.We address two problematic areas of cybernetics; nam. Analogical Problem Solving (APS) and Analogical Learning (AL). Both these human faculties do unquestionably require Intelligence. In addition, we point out that shifting of representations is the main unified theme underlying these two intellectual tasks. We focus our attention on the formulation and clarification of the notion of analogy, which has been loosely treated and used in the literature; and also on its role in shifting of representations. We describe analogizing situations in a new representational scheme, borrowed from mathematics and modified and extended to cater for our targets. We call it k-structure, closely resembling semantic networks and directed graphs; the main components of it are the so-called objects and morphisms. We argue and substantiate the need for such a representation scheme, by analysing what its constituents stand for and by cataloguing its virtues, the main one being its visual appeal and its mathematical clarity, and by listing its disadvantages when it is compared to other representation systems. Emphasis is also given to its descriptive power and usefulness by implementing it in a number of APS and AL situations. Besides representation issues, attention is paid to intelligence mechanisms which are involved in APS and AL. A cornerstone in APS and a fundamental theme in AL is the 'skeletization of k-structures'. APS is conceived as 'harmonization of skeletons'. The methodology we develop involves techniques which are computer implemented and extensively studied in theoretic terms via a proposed theory for extended k-structures. To name but a few: 1. 'the separation of the context of a concept from the concept itself', based on the ideas of k-opens and k-spaces; 2, 'object and morphism elimination' of a controversial nature; and 3. 'conflict or deadlock or dilemma resolution' which naturally arises in a k-structure interaction. The overall system, is then applied to capture the essence of EVANS' (1963) analogy-type problems and WINSTOM (1970) learning-type situations. In our attempt not to be too informal, we use basic notions and terminology from abstract Algebra, Topology and Category theory. We rather tend to be "non-logical" (analogical) in EVANS' and WINSTON's sense; "non-numeric", in MESAROVIC (1970) terms (we rather deal with abstract conceptual entities); "non-linguistic" (we do not touch natural language); and "non-resolution" oriented, in the sense of BLEDSOE (1977). However, we give hints sometimes about logical deductive axiomatic systems, employing First Order Predicate Calculus (FOPC); and about semiotics, by which we denote syntactic-semantic-pragmatic features of our system and issues of the problem domains it is acting upon. We believe in what we call: shift from the traditional 'Heuristic search paradigm' era to the 'Analogy-paradigm' era underlying Artificial Intelligence and Cybernetics. We justify this merely by listing a number of A. I. works, which employ, in some way or another, the concept of analogy, over the last fifteen years or so, where a noticeable peak is obvious during the last years and especially in 1977. Finally, we hope that if the proposed conceptual framework and techniques developed do not straightforwardly constitute some kind of platform for Artificial Intelligence, at least it would give some insights into and illuminate our understanding of the two most fundamental faculties the human brain is occupied with; namely problem solving and learning

A Philosophical Treatise of Universal Induction

Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results and is based on solid philosophical foundations, the requisite technical knowledge necessary for understanding this framework has caused it to remain largely unknown and unappreciated in the wider scientific community. The main contribution of this article is to convey Solomonoff induction and its related concepts in a generally accessible form with the aim of bridging this current technical gap. In the process we examine the major historical contributions that have led to the formulation of Solomonoff Induction as well as criticisms of Solomonoff and induction in general. In particular we examine how Solomonoff induction addresses many issues that have plagued other inductive systems, such as the black ravens paradox and the confirmation problem, and compare this approach with other recent approaches.Comment: 72 pages, 2 figures, 1 table, LaTe

Recent Advances in General Game Playing

The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new games. In this review, we present a survey of recent advances (2011 to 2014) in GGP for both traditional games and video games. It is notable that research on GGP has been expanding into modern video games. Monte-Carlo Tree Search and its enhancements have been the most influential techniques in GGP for both research domains. Additionally, international competitions have become important events that promote and increase GGP research. Recently, a video GGP competition was launched. In this survey, we review recent progress in the most challenging research areas of Artificial Intelligence (AI) related to universal game playing

Logic Programming: Context, Character and Development

Logic programming has been attracting increasing interest in recent years. Its first realisation in the form of PROLOG demonstrated concretely that Kowalski's view of computation as controlled deduction could be implemented with tolerable efficiency, even on existing computer architectures. Since that time logic programming research has intensified. The majority of computing professionals have remained unaware of the developments, however, and for some the announcement that PROLOG had been selected as the core language for the Japanese 'Fifth Generation' project came as a total surprise. This thesis aims to describe the context, character and development of logic programming. It explains why a radical departure from existing software practices needs to be seriously discussed; it identifies the characteristic features of logic programming, and the practical realisation of these features in current logic programming systems; and it outlines the programming methodology which is proposed for logic programming. The problems and limitations of existing logic programming systems are described and some proposals for development are discussed. The thesis is in three parts. Part One traces the development of programming since the early days of computing. It shows how the problems of software complexity which were addressed by the 'structured programming' school have not been overcome: the software crisis remains severe and seems to require fundamental changes in software practice for its solution. Part Two describes the foundations of logic programming in the procedural interpretation of Horn clauses. Fundamental to logic programming is shown to be the separation of the logic of an algorithm from its control. At present, however, both the logic and the control aspects of logic programming present problems; the first in terms of the extent of the language which is used, and the second in terms of the control strategy which should be applied in order to produce solutions. These problems are described and various proposals, including some which have been incorporated into implemented systems, are described. Part Three discusses the software development methodology which is proposed for logic programming. Some of the experience of practical applications is related. Logic programming is considered in the aspects of its potential for parallel execution and in its relationship to functional programming, and some possible criticisms of the problem-solving potential of logic are described. The conclusion is that although logic programming inevitably has some problems which are yet to be solved, it seems to offer answers to several issues which are at the heart of the software crisis. The potential contribution of logic programming towards the development of software should be substantial

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence