Comparing Typical Opening Move Choices Made by Humans and Chess Engines

The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books. For chess, which has a very rich opening theory, large databases of high-quality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected. In order to find out whether the opening books used by modern chess engines in machine versus machine competitions are ``comparable'' to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans' games and the other from machines' games. Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans' and machines' choices of opening moves when using a book to guide their choices.Comment: 12 pages, 1 figure, 6 table

Enhancing Artificial Intelligence on a Real Mobile Game

Mobile games represent a killer application that is attracting millions of subscribers worldwide. One of the aspects crucial to the commercial success of a game is ensuring an appropriately challenging artificial intelligence (AI) algorithm against which to play. However, creating this component is particularly complex as classic search AI algorithms cannot be employed by limited devices such as mobile phones or, even on more powerful computers, when considering imperfect information games (i.e., games in which participants do not a complete knowledge of the game state at any moment). In this paper, we propose to solve this issue by resorting to a machine learning algorithm which uses profiling functionalities in order to infer the missing information, thus making the AI able to efficiently adapt its strategies to the human opponent. We studied a simple and computationally light machine learning method that can be employed with success, enabling AI improvements for imperfect information games even on mobile phones. We created a mobile phone-based version of a game calledGhostsand present results which clearly show the ability of our algorithm to quickly improve its own predictive performance as far as the number of games against the same human opponent increases

Classifier System Learning of Good Database Schema

This thesis presents an implementation of a learning classifier system which learns good database schema. The system is implemented in Java using the NetBeans development environment, which provides a good control for the GUI components. The system contains four components: a user interface, a rule and message system, an apportionment of credit system, and genetic algorithms. The input of the system is a set of simple database schemas and the objective for the classifier system is to keep the good database schemas which are represented by classifiers. The learning classifier system is given some basic knowledge about database concepts or rules. The result showed that the system could decrease the bad schemas and keep the good ones

Classifier System Learning of Good Database Schema

This thesis presents an implementation of a learning classifier system which learns good database schema. The system is implemented in Java using the NetBeans development environment, which provides a good control for the GUI components. The system contains four components: a user interface, a rule and message system, an apportionment of credit system, and genetic algorithms. The input of the system is a set of simple database schemas and the objective for the classifier system is to keep the good database schemas which are represented by classifiers. The learning classifier system is given some basic knowledge about database concepts or rules. The result showed that the system could decrease the bad schemas and keep the good ones

On Advanced Template-based Interpretation As Applied To Intention Recognition In A Strategic Environment

An area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection of tactics that it could not detect without somehow manipulating the environment. Therefore, the aim of this thesis is to address the problem of recognizing an opponent\u27s intent in a strategic environment where the system can think ahead in time to see the agent\u27s plan. To approach the problem, a structured form of knowledge called Template-Based Interpretation is borrowed from the work of others and enhanced to reason in a temporally dynamic simulation

From Deep Learning to Rational Machines -- What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence -- Sample Chapter 1 -- "Moderate Empiricism and Machine Learning"

This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks (“deep learning”) to achieve artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning’s current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the extraction of abstract knowledge from perceptual experience involves the active engagement of general psychological faculties—such as perception, memory, imagination, attention, and empathy. This book explains how recent headline-grabbing deep learning achievements were enabled by adding functionality to these networks that model forms of processing attributed to these faculties by philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to building more robustly rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can now be realized in specific computational systems

Deeply Rational Machines -- What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence -- Sample Chapter 1 -- "Moderate Empiricism and Machine Learning"

This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks (“deep learning”) to achieve artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning’s current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the extraction of abstract knowledge from perceptual experience involves the active engagement of general psychological faculties—such as perception, memory, imagination, attention, and empathy. This book explains how recent headline-grabbing deep learning achievements were enabled by adding functionality to these networks that model forms of processing attributed to these faculties by philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to building more robustly rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can now be realized in specific computational systems

Примена виртуелних светова у истраживању теорије агената и инжењерском образовању

The focus of this doctoral dissertation is on exploring the potentials of virtual worlds, for applications in research and education. Regarding this, there are two central aspects that are explored in the dissertation. The first one considers the concept of autonomous agents, and agent theory in general, in the context of virtual worlds. The second aspect is related to the educational applications of virtual worlds, while especially focusing on the concept of virtual laboratories. An introduction to basic terminology related to the subject is given at the start of the dissertation. After that, a thorough analysis of the role of agents in virtual worlds is presented. This, among others, includes the analysis of the techniques that shape the agent’s behavior. The development of the virtual gamified educational system, specially dedicated to agents is then presented in the dissertation, along with a thorough description. While, in the end, analysis of the concept of virtual laboratories in STE (Science, Technology, and Engineering) disciplines is performed, and existing solutions are evaluated according to the criteria defined in the dissertation.Фокус ове докторске дисертације је на истраживању потенцијала виртуелних светова за примене у истраживањима и образовању. У вези са тим, постоје два главна аспекта која су обрађена у дисертацији. Први аспект се тиче концепта аутономних агената, као и теорије агената у целини, а у контексту виртуелних светова. Други аспект је везан за примену виртуелних светова у образовању, при чему је посебан акценат стављен на виртуелне лабораторије. На почетку дисертације је дат кратак увод који се тиче терминологије и појединих појмова везаних за област којом се ова дисертција бави. Након тога је представљена систематична и темељна анализа улоге агената у виртуелним световима. Између осталог, ово укључује и анализу техника потребних за обликовање понашања агената. Потом је у дисертацији детаљно представљен развој оригиналног виртуелног образовног система посвећеног агентима. На крају, анализиран је концепт виртуелних лабораторија у НТИ (наука, технологија, инжењерство) дисциплинама и извршена је евалуација постојећих решења у складу са критеријумима који су дефинисани у дисертацији