Showing papers on "Applications of artificial intelligence published in 2008"

Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift

Abstract: Officially, AI was born in 1956. Since then, very impressive progress has been made in many areas - but not in the realm of human level machine intelligence. During much of its early history, AI "was rife "with exaggerated expectations. A headline in an article published in the late forties of last century was headlined, "Electric brain capable of translating foreign languages is being built". Today, more than half a century later, we do have translation software, but nothing that can approach the quality of human translation. Clearly, achievement of human level machine intelligence is a challenge that is hard to meet. A prerequisite to achievement of human level machine intelligence is mechanization of these capabilities and, in particular, mechanization of natural language understanding. To make significant progress toward achievement of human level machine intelligence, a paradigm shift is needed. More specifically, what is needed is an addition to the armamentarium of AI of two methodologies: (a) a nontraditional methodology of computing with words (CW) or more generally, NL-Computation; and (b) a countertraditional methodology "which involves a progression from computing with numbers to computing with words. The centerpiece of these methodologies is the concept of precisiation of meaning. Addition of these methodologies to AI would be an important step toward the achievement of human level machine intelligence and its applications in decision-making, pattern recognition, analysis of evidence, diagnosis, and assessment of causality. Such applications have a position of centrality in our infocentric society.

Artificial Intelligence Methods in the Environmental Sciences

Abstract: How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a red thread ties the book together, weaving a tapestry that pictures the natural data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Artificial Dreams: The Quest for Non-Biological Intelligence

Abstract: This book is a critique of Artificial Intelligence (AI) from the perspective of cognitive science - it seeks to examine what we have learned about human cognition from AI successes and failures. The book's goal is to separate those "AI dreams" that either have been or could be realized from those that are constructed through discourse and are unrealizable. AI research has advanced many areas that are intellectually compelling and holds great promise for advances in science, engineering, and practical systems. After the 1980s, however, the field has often struggled to deliver widely on these promises. This book breaks new ground by analyzing how some of the driving dreams of people practicing AI research become valued contributions, while others devolve into unrealized and unrealizable projects.

Toward human level machine intelligence - is it achievable?

Abstract: Achievement of human level machine intelligence has long been one of the basic objectives of AI. Officially, AI was born in 1956. Since then, very impressive progress has been made in many areas - but not in the realm of human level machine intelligence. Anyone who has been forced to use a dumb automated customer service system will readily agree. The Turing test lies far beyond. Today, no machine can pass the Turing test and none is likely to do so in the foreseeable future.To make progress toward achievement of human level machine intelligence, AI must add to its armamentarium concepts and techniques drawn from other methodologies, especially evolutionary computing, neurocomputing and fuzzy logic. A key contribution of fuzzy logic is the machinery of Computing with Words (CW) and, more generally, NL-Computation. This machinery opens the door to mechanization of natural language understanding and computation with information described in natural language. Addition of this machinery to the armamentarium of AI would be an important step toward the achievement of human level machine intelligence and its applications in decision making, pattern recognition, analysis of evidence, diagnosis and assessment of causality. Such applications have a position of centrality in our info-centric society.

WikiTaxonomy: A Large Scale Knowledge Resource

Abstract: We present a taxonomy automatically generated from the system of categories in Wikipedia. Categories in the resource are identified as either classes or instances and included in a large subsumption, i.e. isa, hierarchy. The taxonomy is made available in RDFS format to the research community, e.g. for direct use within AI applications or to bootstrap the process of manual ontology creation.

Interactionist AI and the promise of ubicomp, or, how to put your box in the world without putting the world in your box

Abstract: In many ways, the central problem of ubiquitous computing -- how computational systems can make sense of and respond sensibly to a complex, dynamic environment laden with human meaning -- is identical to that of Artificial Intelligence (AI). Indeed, some of the central challenges that ubicomp currently faces in moving from prototypes that work in restricted environments to the complexity of real-world environments -- e.g. difficulties in scalability, integration, and fully formalizing context -- echo some of the major issues that have challenged AI researchers over the history of their field. In this paper, we explore a key moment in AI's history where researchers grappled directly with these issues, resulting in a variety of novel technical solutions within AI. We critically reflect on six strategies from this history to suggest technical solutions for how to approach the challenge of building real-world, usable solutions in ubicomp today.

Why PBD systems fail: Lessons learned for usable AI

Abstract: Programming by demonstration systems have long attempted to make it possible for people to program computers without writing code. These systems typically employ artificial intelligence techniques to learn from user behavior in order to predict their future behavior. However, while these systems have resulted in many publications in AI venues, none of the technologies have yet achieved widespread adoption. Usability remains a critical barrier to their success. Based on lessons learned from three different programming by demonstration systems, we present a a set of guidelines to consider when designing usable AI-based systems.

Rapid adaptation of video game AI

Abstract: Current approaches to adaptive game AI require either a high quality of utilised domain knowledge, or a large number of adaptation trials These requirements hamper the goal of rapidly adapting game AI to changing circumstances In an alternative, novel approach, domain knowledge is gathered automatically by the game AI, and is immediately (ie, without trials and without resource-intensive learning) utilised to evoke effective behaviour In this paper we discuss this approach, called dasiarapidly adaptive game AIpsila We perform experiments that apply the approach in an actual video game From our results we may conclude that rapidly adaptive game AI provides a strong basis for effectively adapting game AI in actual video games

Incorporating mental simulation for a more effective robotic teammate

Abstract: How can we facilitate human-robot teamwork? The teamwork literature has identified the need to know the capabilities of teammates. How can we integrate the knowledge of another agent’s capabilities for a justifiably intelligent teammate? This paper describes extensions to the cognitive architecture, ACT-R, and the use of artificial intelligence (AI) and cognitive science approaches to produce a more cognitively-plausible, autonomous robotic system that “mentally” simulates the decision-making of its teammate. The extensions to ACT-R added capabilities to interact with the real world through the robot’s sensors and effectors and simulate the decision-making of its teammate. The AI applications provided visual sensor capabilities by methods clearly different than those used by humans. The integration of these approaches into intelligent team-based behavior is demonstrated on a mobile robot. Our “TeamBot” matches the descriptive work and theories on human teamwork. We illustrate our approach in a spatial, team-oriented task of a guard force responding appropriately to an alarm condition that requires the human and robot team to “man” two guard stations as soon as possible after the alarm.

Artificial Intelligence: A Systems Approach with CD

Abstract: This book offers students and AI programmers a new perspective on the study of artificial intelligence concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input & reduction as well as data output (i.e., algorithm usage). Because traditional AI concepts such as pattern recognition, numerical optimization and data mining are now simply types of algorithms, a different approach is needed. This sensor / algorithm / effecter approach grounds the algorithms with an environment, helps students and AI practitioners to better understand them, and subsequently, how to apply them. The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more. A CD-ROM with simulations, code, and figures accompanies the book.

Artificial General Intelligence 2008:Proceedings of the First AGI Conference

Abstract: The field of Artificial Intelligence (AI) was initially directly aimed at the construction of 'thinking machines' - that is, computer systems with human-like general intelligence. But this task proved more difficult than expected. As the years passed, AI researchers gradually shifted focus to producing AI systems that intelligently approached specific tasks in relatively narrow domains. In recent years, however, more and more AI researchers have recognized the necessity - and the feasibility - of returning to the original goal of the field. Increasingly, there is a call to focus less on highly specialized 'narrow AI' problem solving systems, and more on confronting the difficult issues involved in creating 'human-level intelligence', and ultimately general intelligence that goes beyond the human level in various ways. Artificial General Intelligence (AGI), as this renewed focus has come to be called, attempts to study and reproduce intelligence as a whole in a domain independent way. Encouraged by the recent success of several smaller-scale AGI-related meetings and special tracks at conferences, the initiative to organize the very first international conference on AGI was taken, with the goal to give researchers in the field an opportunity to present relevant research results and to exchange ideas on topics of common interest. In this collection you will find the conference papers: full-length papers, short position statements and also the papers presented in the post conference workshop on the sociocultural, ethical and futurological implications of AGI.IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences

Bots Get Smart

Abstract: The main challenge in making video games is to make computer-generated characters-dubbed bots-act realistically. They must, of course, look good and move naturally. But, ideally, they should also be able to engage in believable conversations, plan their actions, find their way around virtual worlds, and learn from their mistakes. That is, they need to be smart. Today many video games create only an illusion of intelligence, using a few programming tricks. But in the not-so -distant future, game bots will routinely use sophisticated AI techniques to shape their behavior. We and our colleagues in the University of Alberta GAMES (game-playing, analytical methods, minimax search and empirical studies) research group, in Edmonton, Canada, have been working to help bring about such a revolution.

An AI Framework for the Automatic Assessment of e-Government Forms

Abstract: This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, follow-up action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed. An AI framework has been used to implement an AI module for one of the busiest immigration agencies in the world.

AI and Artificial Life in Video Games

Abstract: Learn how to create more challenging and dynamic games with AI and Artificial Life in Video Games. AI, or artificial intelligence, builds better games by directing behaviors inside the games that make them more difficult, while artificial life, or A-Life, adds unpredictability of play and a more lifelike environment to games. This book examines easy and inexpensive methods for implementing AI and A-Life in any video game to not only model behavior in the game but also create tools, generate code, and test the game during development. After introducing the basics of AI and A-Life to use as building blocks, the book delves into more advanced methods and examines possible future uses and techniques. Youll learn how AI can be built up in a game by layering behavioral models on static data to produce behavior that is both intelligent and unpredictable. Examples of several A-Life enhancements in games are presented, and youll investigate the potential pitfalls of using AI and how to troubleshoot, apply A-Life to your own games, test A-Life itself and test virtually using A-Life, implement AI and A-Life in a multiplayer environment, and more. Written for the current and next-generation game developer, AI and Artificial Life in Video Games is a great reference for both game programmers and game designers.

Optimizing Building's Environments Performance Using Intelligent Systems

Abstract: This article describes an intelligent system architecture that based on neural networks, expert systems and negotiating agents technologies is designed to optimize intelligent building's performance. By understanding a building as a dynamic entity capable of adapting itself not only to changing environmental conditions but also to occupant's living habits, high standards of comfort and user satisfaction can be achieved. Results are promising and encourage further research in the field of artificial intelligence applications in building automation systems.

Ways and Means of Employing AI Technology in E-Learning Systems

Abstract: Employing the state-of-the-art artificial intelligence (AI) technology in current e-learning systems can bring personalized, adaptive, and intelligent services to both students and educators. Although we have seen more and more successful applications of AI in e-learning, most of them have not yet been expanded to or adopted in widely used e-learning systems, especially open-source learning management systems (LMS) such as Moodle, Sakai and so on. This observation takes us to the analysis and discussion of the current work in both LMS and applied AI. The findings include that current intelligent LMS systems are still in their early stage, while AI applications need to handle some problems or to be modified before applying them into the LMS systems, and AI technology also needs to be brought to open source communities.

Usable artificial intelligence

Abstract: "The AI and HCI communities have often been characterized as having opposing views of how humans and computers should interact" observes Winograd in "Shifting Viewpoints". Reconciling these views requires a thoughtful balancing of assistance and control, of mental and system representations, and of abstract process and contextualized workflow. This workshop examines the gap between HCI and artificial intelligence, with the goal of improving usability of AI systems.

Teaching Distributed Artificial Intelligence with RoboRally

Abstract: Teaching Artificial Intelligence (AI) or multi-agent systems is a challenging task as algorithms are in question which are advantageous in highly complex and dynamic environments Explaining multi-agent systems (MAS) in lectures requires interactive approaches accompanied by exercises The key challenge in using practical exercises within lectures on MAS is to establish an environment for testing which is extremely time consuming It is not reasonable that students do this work as they do have not enough time focussing on the important aspects In this paper, we introduce a system which supports experimenting with AI and Distributed Artificial Intelligence (DAI) algorithms concurrently to the lecture Our system is based on a board game called RoboRally Different issues from the field of AI and DAI can be implemented and tested in a kind of challenge

Artificial Intelligence and Bluetooth Techniques in a Multi-user M-learning Domain.

Abstract: In this paper we present a practical implementation of a multiuser technical laboratory that combines Artificial Intelligence (AI) and Bluetooth (BT) techniques. The objective is to build an m-learning environment where students can work in a customized way. Applying BT capabilities this domain can be isolated into a classroom and used by several learners simultaneously. The student activities can be supervised by means of AI strategies (planning, scheduling and expert systems) in order to adapt the modus operandi to the characteristics of each one. Integrating these technologies, the whole system will be able to recognize each user, organize his/her work and evaluate his/her results without or little educator intervention. Nevertheless, the teacher will be reported about the student actions and will be adviced when the situation requires it 1 .

Open Source AI

Abstract: Machines significantly more intelligent than humans will require changes in our legal and economic systems in order to preserve something of our human values An open source design for artificial intelligence (AI) will help this process by discouraging corruption, by enabling many minds to search for errors, and by encouraging political cooperation The author's experience developing open source software provides anecdotal evidence for the healthy social effects of open source development

We've Come a Long Way, Maybe

Abstract: Despite all the major AI successes, when it comes to really understanding the human mind, we still know very little.

Artificial Intelligence Approach to Energy Management and Control in the HVAC Process: An Evaluation, Development and Discussion

Abstract: Computer control technology in heating, ventilating and air conditioning (HVAC) systems has evolved in two technical phases: central control and direct digital control (DDC) with central monitoring function. Many control strategies have been developed for energy conservation in HVAC energy management and control systems (EMCS) and these are reported in the literature. An artificial intelligence approach to the EMCS in HVAC process is proposed in this paper. Two real time expert systems for energy-saving operation and indoor setting are developed and integrated with an adaptive control strategy. Further, a configuration of an integrated intelligent system for intelligent building management is proposed, in which building management functions are coordinated by a meta-system. The applications of artificial intelligence (AI) techniques in a HVAC process give a new avenue for the development of EMCS in the HVAC industry.

Applications of Artificial Intelligence in an Open and Distance Learning institution

Abstract: Applying Artificial Intelligence (AI) in an educational setting presents a wealth of opportunities, particularly for Open and Distance Learning (ODL) institutions. As ODL relies heavily on human-machine interactions, AI thus naturally offers open universities various means to address issues such as how do people actually learn; what constitutes effective teaching; as well as what are the advantages and limitations of computer-based systems in education. Open University Malaysia (OUM) is Malaysia’s premier ODL institution and has been operating for seven years. As an ODL institution, OUM’s operations and services are heavily anchored on a range of information and communication technologies (ICTs) that could potentially include AI. Though the implementation of AI has not been fully realised in education, OUM foresees many areas that can benefit from it, in terms of ensuring quality, improving pedagogical methods as well as enhancing the overall teaching and learning experience. In this paper, we will explore several fields whereby AI could be potentially utilised in an ODL institution, i.e. expert system for programme advising; automated scheduling of classes; marking of assignments; plagiarism detection; retaining learners and adapting to their diverse needs and backgrounds; maintenance of property; and ensuring security. OUM also anticipates that AI could provide a significant and highly intriguing paradigm shift in the deployment of ODL and that it could greatly influence the future of all open and distance learners.

AI opponents with personality traits in Überpong

Abstract: Nowadays, the video gaming experience is shifting from merely realistic to believable. The increasing graphic power of current graphic cards has made it possible to render near lifelike images. Unfortunately, the behaviour of the computer driven player and non-playing characters is often poor when compared to their visual appearance. In this sense, there has been a recent interest in improving the video gaming experience with novel Artificial Intelligence (AI) techniques. This paper presents a robotics inspired behavioural AI technique to simulate characters' personalities in an multi-award winning commercial video game.

Practical Pathfinding in Dynamic Environments

Abstract: Welcome to the latest volume of AI Game Programming Wisdom! AI Game Programming Wisdom 4 includes a collection of more than 50 new articles featuring cutting-edge techniques, algorithms, and archit ...

The application of artificial intelligence principles to teaching and training

Abstract: This paper compares and contrasts the use of AI principles in industrial training with more normal computer-based training (CBT) approaches. A number of applications of CBT are illustrated (for example simulations, tutorial presentations, fault diagnosis, management games, industrial relations exercises) and compared with an alternative approach using AI. An evaluation of the relative merits of the two approaches will be given. Existing CBT packages are used to illustrate the points raised and the emphasis of the arguments will be on the effectiveness of AI and CBT in terms of both cost and learning. The position of AI applications within CBT is discussed, as is the task of getting started in applying these techniques.