Showing papers on "Applications of artificial intelligence published in 2009"

Artificial Intelligence for Games

Abstract: AI and Games Introduction What Is AI? Model of Game AI Algorithms, Data Structures, and Representations On the Website Layout of the Book Game AI The Complexity Fallacy The Kind of AI in Games Speed and Memory The AI Engine Techniques Movement The Basics of Movement Algorithms Kinematic Movement Algorithms Steering Behaviors Combining Steering Behaviors Predicting Physics Jumping Coordinated Movement Motor Control Movement in the Third Dimension Pathfinding The Pathfinding Graph Dijkstra A* World Representations Improving on A* Hierarchical Pathfinding Other Ideas in Pathfinding Continuous Time Pathfinding Movement Planning Decision Making Overview of Decision Making Decision Trees State Machines Behavior Trees Fuzzy Logic Markov Systems Goal-Oriented Behavior Rule-Based Systems Blackboard Architectures Scripting Action Execution Tactical and Strategic AI Waypoint Tactics Tactical Analyses Tactical Pathfinding Coordinated Action Learning Learning Basics Parameter Modification Action Prediction Decision Learning Naive Bayes Classifiers Decision Tree Learning Reinforcement Learning Artificial Neural Networks Board Games Game Theory Minimaxing Transposition Tables and Memory Memory-Enhanced Test Algorithms Opening Books and Other Set Plays Further Optimizations Turn-Based Strategy Games Supporting Technologies Execution Management Scheduling Anytime Algorithms Level of Detail World Interfacing Communication Getting Knowledge Efficiently Event Managers Polling Stations Sense Management Tools and Content Creation Knowledge for Pathfinding and Waypoint Tactics Knowledge for Movement Knowledge for Decision Making The Toolchain Designing Game AI Designing Game AI The Design Shooters Driving Real-Time Strategy Sports Turn-Based Strategy Games AI-Based Game Genres Teaching Characters Flocking and Herding Games Appendix Books, Periodicals, and Papers Games

The Quest for Artificial Intelligence

Abstract: Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.

Artificial Intelligence for Games, Second Edition

Abstract: Creating robust artificial intelligence is one of the greatest challenges for game developers, yet the commercial success of a game is often dependent upon the quality of the AI. In this book, Ian Millington brings extensive professional experience to the problem of improving the quality of AI in games. He describes numerous examples from real games and explores the underlying ideas through detailed case studies. He goes further to introduce many techniques little used by developers today. The book's associated web site contains a library of C++ source code and demonstration programs, and a complete commercial source code library of AI algorithms and techniques."Artificial Intelligence for Games - 2nd edition" will be highly useful to academics teaching courses on game AI, in that it includes exercises with each chapter. It will also include new and expanded coverage of the following: AI-oriented gameplay; Behavior driven AI; Casual games (puzzle games). * The first comprehensive, professional tutorial and reference to implement true AI in games written by an engineer with extensive industry experience.* Walks through the entire development process from beginning to end.* Includes examples from over 100 real games, 10 in-depth case studies, and web site with sample code.

Applications of artificial intelligence and data mining techniques in soil modeling

Abstract: In recent years, several computer-aided pattern recognition and data mining techniques have been developed for modeling of soil behavior. The main idea behind a pattern recognition system is that it learns adaptively from experience and is able to provide predictions for new cases. Artificial neural networks are the most widely used pattern recognition methods that have been utilized to model soil behavior. Recently, the authors have pioneered the application of genetic programming (GP) and evolutionary polynomial regression (EPR) techniques for modeling of soils and a number of other geotechnical applications. The paper reviews applications of pattern recognition and data mining systems in geotechnical engineering with particular reference to constitutive modeling of soils. It covers applications of artificial neural network, genetic programming and evolutionary programming approaches for soil modeling. It is suggested that these systems could be developed as efficient tools for modeling of soils and analysis of geotechnical engineering problems, especially for cases where the behavior is too complex and conventional models are unable to effectively describe various aspects of the behavior. It is also recognized that these techniques are complementary to conventional soil models rather than a substitute to them.

Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects

Abstract: Despite decades of research, developing software that is fit for purpose, developed on time, and within budget remains a challenge Many researchers have advocated the use of artificial intelligence techniques such as knowledge-based systems, neural networks, and data mining as a way of addressing these difficulties Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects provides an overview of useful techniques in artificial intelligence for future software development along with critical assessment for further advancement A compendium of latest industry findings, this Premier Reference Source offers researchers, academicians, and practitioners developmental ideas within the field

Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

Abstract: Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro – fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student’s answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

Advances of Soft Computing Methods in Edge Detection

Abstract: Artificial Intelligence (AI) techniques are now commonly used to solve complex and ill-defined problems. AI a broad field and will bring different meanings for different people. John McCarthy would probably use AI as “computational intelligence”, while Zadeh claimed that computational intelligence is actually Soft Computing (SC) techniques. Regardless of its definition, AI concerns with tasks that require human intelligence which require complex and advanced reasoning processes and knowledge. Due to its ability to learn, handle incomplete or incomprehensible data, deal with nonlinear problems, and perform reasonable tasks very fast, AI has been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social sciences. However, in this paper, we will focus on Soft Computing (SC), one of the AI influences that sprang from the concept of cybernetics. The main objective of this paper is to illustrate how some of these SC techniques generally work on detecting the edges. The paper also outlines practical differences among these techniques when they are applied to solving the problem of edge detection.

Computational Intelligence and AI in Games: A New IEEE Transactions

Abstract: The author first provides an overview of computational intelligence and AI in games. Then he describes the new IEEE Transactions, which will publish archival quality original papers in all aspects of computational intelligence and AI related to all types of games. To name some examples, these include computer and video games, board games, card games, mathematical games, games that model economies or societies, serious games with educational and training applications, and games involving physical objects such as robot football and robotic car racing. Emphasis will also be placed on the use of these methods to improve performance in, and understanding of, the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It will also include using games as a platform for building intelligent embedded agents for real-world applications. The journal builds on a scientific community that has already been active in recent years with the development of new conference series such as the IEEE Symposium on Computational Intelligence in Games (CIG) and Artificial Intelligence and Interactive Digital Entertainment (AIIDE), as well as special issues on games in journals such as the IEEE Transactions on Evolutionary Computation. When setting up the journal, a decision was made to include both artificial intelligence (AI) and computational intelligence (CI) in the title. AI seeks to simulate intelligent behavior in any way that can be programmed effectively. Some see the field of AI as being all-inclusive, while others argue that there is nothing artificial about real intelligence as exhibited by higher mammals.

JGOMAS: New Approach to AI Teaching

Abstract: This paper presents a new environment for teaching practical work in AI subjects. The main purpose of this environment is to make AI techniques more appealing to students and to facilitate the use of the toolkits which are currently widely used in research and development. This new environment has a toolkit for developing and executing agents, called JADE-based game-oriented multiagent system (JGOMAS). The environment also has a dedicated website where students can access different documentation and information and interact with teachers. An actual case study of this environment applied to the practical work component of an advanced AI course is presented.

Designing and Modeling Solar Energy Systems

Abstract: Chapter 11 presents methods used for the design and modeling of solar systems. These include the f -chart method and program and comprise sections on the design of liquid-based solar heating systems, various corrections required for the storage capacity, collector flow rate and load heat exchanger size; the design of air-based solar heating systems, various corrections required for the pebble-bed storage size and air flow-rate correction; the design of solar service-water systems, and the use of the f -chart for the design of thermosiphon solar water-heating systems. The section concludes with some general remarks and a brief overview of the f -chart program. This is followed by the utilizability method and examines the hourly utilizability, daily utilizability, and the design of active systems using both the hourly and the daily utilizability methods. Subsequently the Φ ¯ , , f -chart method is presented and include the corrections for the storage tank losses and heat exchanger correction, followed by the unutilizability method, which include direct gain systems, collector storage walls, and active collection with passive storage systems. The chapter includes also a description of the various programs that can be used for the modeling and simulation of solar systems and include the TRNSYS, WATSUN, and Polysun simulation programs. This is followed by a short description of the artificial intelligence techniques used in renewable energy systems modeling, performance prediction, and control. It includes a description of the artificial neural networks and comprises sections on biological and artificial neurons, artificial neural network principles, network parameters selection, and a description of the basic neural network architectures, like the back-propagation, the general regression neural network, and the group method data handling neural network. Then genetic algorithms are described as well as their applications in solar energy systems, followed by a short description of GenOpt and TRNopt programs. Subsequently, fuzzy logic is examined and includes membership functions, logical operations, if-then rules, fuzzy inference systems, and fuzzy systems applications in solar energy systems. Finally, hybrid systems are described, which employ more than one artificial intelligence applications. The chapter concludes with an analysis of the limitations of simulations.

The Role of Associative Processing in Cognitive Computing

Abstract: The traditional approaches—of symbolic artificial intelligence (AI) and of sub-symbolic neural networks—towards artificial cognition have not been very successful The rule-based symbolic AI approach has proven to be brittle and unable to provide any real intelligence (Mckenna, Artificial intelligence and neural networks: steps toward principled integration, Academic Press, USA, 1994) On the other hand, traditional artificial neural networks have not been able to advance very much beyond pattern recognition and classification This shortcoming has been credited to the inability of conventional artificial neural networks to handle syntax and symbols Hybrid approaches that combine symbolic AI and sub-symbolic neural networks have been tried with results that fall short of the ultimate goal It has been argued that traditional AI programs do not operate with meanings and consequently do not understand anything (Searle, Minds, brains & science, Penguin Books Ltd, London, 1984; Searle, The mystery of consciousness, Granta Books, London, 1997) It seems that in this way some essential ingredient is missing, but there may be a remedy available Associative information processing principles may enable the utilization of meaning and the combined sub-symbolic/symbolic operation of neural networks

Opportunities and Challenges for Next-Generation Applied Intelligence

Abstract: The term "Artificial Intelligence" has been used since 1956 and has become a very popular research field. AI Techniques have been used in almost any domain. The term "Applied Intelligence" was created to represent its practicality. It emphasizes applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation, robotics, business, finance, medicine, biomedicine, bioinformatics, cyberspace, man-machine interactions, etc.The International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) seeks for quality papers on applied intelligence that incorporate all kinds of real life problems. The objective of the conference was to bring together scientists, engineers and practitioners, who work on designing or developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains. The book is comprises of 12 parts including 52 chapters providing an up-to-date and state-of-the research on the applications of artificial Intelligence techniques.

Judicial Applications of Artificial Intelligence

Abstract: The judiciary is in the early stages of a transformation in which AI (Artificial Intelligence) technology will help to make the judicial process faster, cheaper, and more predictable without compromising the integrity of judges' discretionary reasoning. Judicial decision-making is an area of daunting complexity, where highly sophisticated legal expertise merges with cognitive and emotional competence. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has had two more practical goals: producing tools to support judicial activities, including programs for intelligent document assembly, case retrieval, and support for discretionary decision-making; and developing new analytical tools for understanding and modeling the judicial process, such as case-based reasoning and formal models of dialectics, argumentation, and negotiation. Judges, squeezed between tightening budgets and increasing demands for justice, are desperately trying to maintain the quality of their decision-making process while coping with time and resource limitations. Flexible AI tools for decision support may promote uniformity and efficiency in judicial practice, while supporting rational judicial discretion. Similarly, AI may promote flexibility, efficiency and accuracy in other judicial tasks, such as drafting various judicial documents. The contributions in this volume exemplify some of the directions that the AI transformation of the judiciary will take.

A Survey of Applications of Artificial Intelligence Algorithms in Eco-environmental Modelling

Abstract: Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

Enhancing artificial intelligence on a real mobile game

Abstract: 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 (ie, 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 called Ghosts and 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

Beyond Adversarial: The Case for Game AI as Storytelling

Abstract: As a field, artificial intelligence (AI) has been applied to games for more than 50 years, beginning with traditional two-player adversarial games like tic-tac-toe and chess and extending to modern strategy games, first-person shooters, and social simulations. AI practitioners have become adept at designing algorithms that enable computers to play games at or beyond human levels in many cases. In this paper, we argue that the traditional goal of AI in games—to win the game—is not the only, nor the most interesting goal. An alternative goal for game AI is to make the human player’s play experience “better.” AI systems in games should reason about how to deliver the best possible experience within the context of the game. The key insight of this paper is that approaching AI reasoning for games as storytelling reasoning makes this goal much more attainable. We present an overview of traditional game AI techniques as well as a few more recent AI storytelling techniques. We also provide a foundation for describing and reasoning about games as stories, citing a number of examples. We conclude by discussing the implications for future directions. Author Keywords Artificial intelligence, machine learning, story telling, narrative, drama management

From artificial to collective intelligence: Perspectives and implications

Abstract: Artificial Intelligence (AI), in its long journey since inception in 1956, has seen many cycles of successes and failures. It has undergone major focal transformations, both in terms of philosophical directions and the areas attracting generous funding. The journey of AI from Turing's test to current state of the art techniques, and their applications, can be seen as the A′B′C′D′ of Artificial Intelligence; with A to mean ‘Artificial’, B denoting ‘Builtin’, C standing for ‘Collective’ and D for ‘Derived’ Intelligence. In this paper, we have tried to track important defining paradigms of AI and demonstrate how Collective Intelligence, the new AI perspective, is enriching computational intelligence techniques, the World Wide Web (Web) and research in social sciences.

Rehearsing Naval Tactical Situations Using Simulated Teammates and an Automated Tutor

Abstract: This paper describes a deployed simulation-based intelligent tutoring system (ITS) for training of tactical action officers (TAOs). The TAO on board a Navy ship is responsible for the operation of the entire watch team manning the ship's command center. The ITS goal is to train the TAO in ldquocommand by negation,rdquo in which watchstanders perform their duties autonomously, while the TAO supervises, intervening in order to correct mistakes and rectify omissions. The ITS uses artificial intelligence (AI) techniques to provide Automated Role Players (ARPs) representing the watchstanders in the ship, and to provide a natural language interface to communicate with these automated teammates. An adaptive coaching strategy is used to provide coaching and feedback during an exercise. The paper presents a discussion of the ITS instructional design, its architecture, and the AI techniques it employs.

Towards ethical aspects on artificial intelligence

Abstract: This paper presents the role of ethics in developing artificial intelligence, and how the artificial intelligence could change our perspective, because artificial intelligence in fact is all around us. Artificial intelligence is an important part of our life, but we are sure that the possibility of acquiring the domination of AI over the humanity is only a myth. During the time, the progress helps society but also brings a number of ethical problems. In the academic society, like in real life, the process of using different kind of power are complex, and even if it is about the robots, the computer or other artificial intelligence tools, the ethical problems are not only theoretical but also practical, it is not only a concept, but it is also a practical support for our life.

Artificial Intelligence Application in Mobile Phone Serious Game

Abstract: This paper is to discuss the importance of artificial intelligence (AI) in entertainment and in serious game, the methods of AI applications beyond entertainment, and the technology features of AI in and beyond entertainment. In addition, it analyses the weakness when AI is applied in mobile phone serious game, and provides solutions.

Artificial Intelligence Applications and Innovations: Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications and Innovations

Abstract: This book constitutes the refereed proceedings ofthe 5th IFIP Artificial Intelligence Innovations and Applications Conference held in Thessaloniki, Greece. The IFIP series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; computers and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Proceedings and post-proceedings of refereed international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the IFIP series is to encourage education and the dissemination and exchange of information about all aspects of computing.

Medical applications of artificial intelligence

Abstract: The development of modern medicine has made ever-increasing demands for the automation and intelligence of medical equipments. After briefly introducing to the basic concept of Artificial Intelligence, this paper mainly described the medical applications and trend of Expert System, Artificial Neural Network and Data mining, the three important branches of Artificial Intelligence, and put forward four problems which was worthy of further research.

Tools and Applications with Artificial Intelligence

Abstract: In recent years, the use of Artificial Intelligence (AI) techniques has been greatly increased. The term intelligence seems to be a must in a large number of European and International project calls. AI Techniques have been used in almost any domain. Application-oriented systems usually incorporate some kind of intelligence by using techniques stemming from intelligent search, knowledge representation, machine learning, knowledge discovery, intelligent agents, computational intelligence etc. The Workshop on Applications with Artificial Intelligence seeks for quality papers on computer applications that incorporate some kind of AI technique. The objective of the workshop was to bring together scientists, engineers and practitioners, who work on designing or developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains (like medicine, biology, education etc), to present and discuss their research works and exchange ideas in this book.

A Frame Manipulation Algebra for ER Logical Stage Modelling

Abstract: The ER model is arguably today's most widely accepted basis for the conceptual specification of information systems. A further common practice is to use the Relational Model at an intermediate logical stage, in order to adequately prepare for physical implementation. Although the Relational Model still works well in contexts relying on standard databases, it imposes certain restrictions, not inherent in ER specifications, which make it less suitable in Web environments. This paper proposes frames as an alternative to move from ER specifications to logical stage modelling, and treats frames as an abstract data type equipped with a Frame Manipulation Algebra (FMA). It is argued that frames, with a long tradition in AI applications, are able to accommodate the irregularities of semi-structured data, and that frame-sets generalize relational tables, allowing to drop the strict homogeneity requirement. A prototype logic-programming tool has been developed to experiment with FMA. Examples are included to help describe the use of the operators.

A review of the artificial intelligence applications in construction dispute resolution

Abstract: It is generally acknowledged that construction disputes are inevitable, highly complicated and may become destructive in construction projects. Artificial Intelligence (AI) applications have been developed recently with the aim of facilitating dispute resolution processes in construction as AI have become more specialized. In this paper, contemporary AI applications in construction dispute resolution field are analyzed and categorized into three groups as settlement oriented systems, method selection oriented systems and dispute evaluation oriented systems, reviewing the tools used in each category so far. This analysis is expected to contribute to the further development of the subject, by providing a holistic perspective and determining the trends and neglected areas in the field.

Inteligência artificial aplicada à Zootecnia

Abstract: Biological systems are surprising flexible in processing information in the real world. Some biological organisms have a central unit processing named brain. The human's brain, consisting of 1011 neurons, realizes intelligent information processing based on exact and commonsense reasoning. Artificial intelligence (AI) has been trying to implement biological intelligence in computers in various ways, but is still far from real one. Therefore, there are approaches like Symbolic AI, Artificial Neural Network and Fuzzy system that partially successful in implementing heuristic from biological intelligence. Many recent applications of these approaches show an increased interest in animal science research. The main goal of this article is to explain the principles of heuristic problem-solving approach and to demonstrate how they can be applied to building knowledge-based systems for animal science problem solving.

Artificial Intelligence: New Research

Abstract: The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximise its chances of success. AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimisation and logic. AI research also overlaps such fields as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others. This book presents recent and important research in the field.

Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII

Abstract: The papers in this volume are the refereed papers presented at AI-2009, the Twenty-ninth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2009 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Reasoning, Data Mining and Machine Learning, Optimisation and Planning, and Knowledge Acquisition and Evolutionary Computation, followed by application stream sections on AI and Design, Commercial Applications of AI and Further AI Applications. The volume also includes the text of short papers presented as posters at the conference.