Showing papers on "Applications of artificial intelligence published in 2011"

Adaptive submodularity: theory and applications in active learning and stochastic optimization

Abstract: Many problems in artificial intelligence require adaptively making a sequence of decisions with uncertain outcomes under partial observability. Solving such stochastic optimization problems is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse AI applications including management of sensing resources, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.

Applications of artificial intelligence in the apparel industry: A review

Abstract: This paper presents a systematic review on the state-of-art of artificial intelligence (AI) applications in the apparel industry. The existing literature is reviewed based on different research issues and AI-based methodologies. The research issues are categorized into four categories on the basis of the operation processes of the apparel industry, including apparel design, manufacturing, retailing, and supply chain management. This paper shows that research on AI applications in the apparel industry is still limited by analyzing the limitations of previous studies and research challenges. Finally, suggestions for further studies are offered.

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies

Abstract: The need for intelligent machines in areas such as medical diagnostics, biometric security systems, and image processing motivates researchers to develop and explore new techniques, algorithms, and applications in this evolving field.Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies provides a common platform for researchers to present theoretical and applied research findings for enhancing and developing intelligent systems. Through its discussions of advances in and applications of pattern recognition technologies and artificial intelligence, this reference highlights core concepts in biometric imagery, feature recognition, and other related fields, along with their applicability.

Digital Signal Processing in Power System Protection and Control

Abstract: Digital Signal Processing in Power System Protection and Control bridges the gap between the theory of protection and control and the practical applications of protection equipment. Understanding how protection functions is crucial not only for equipment developers and manufacturers, but also for their users who need to install, set and operate the protection devices in an appropriate manner.After introductory chapters related to protection technology and functions, Digital Signal Processing in Power System Protection and Control presents the digital algorithms for signal filtering, followed by measurement algorithms of the most commonly-used protection criteria values and decision-making methods in protective relays. A large part of the book is devoted to the basic theory and applications of artificial intelligence techniques for protection and control. Fuzzy logic based schemes, artificial neural networks, expert systems and genetic algorithms with their advantages and drawbacks are discussed. AI techniques are compared and it is also shown how they can be combined to eliminate the disadvantages and magnify the useful features of particular techniques.The information provided in Digital Signal Processing in Power System Protection and Control can be useful for protection engineers working in utilities at various levels of the electricity network, as well as for students of electrical engineering, especially electrical power engineering. It may also be helpful for other readers who want to get acquainted with and to apply the filtering, measuring and decision-making algorithms for purposes other than protection and control, everywhere fast and on-line signal analysis is needed for proper functioning of the apparatus.

Player modeling: Towards a common taxonomy

Abstract: Artificial Intelligence (AI) is gradually receiving more attention as a fundamental feature to increase the immersion in digital games. Among the several AI approaches, Player Modeling is becoming an important one. The main idea is to try to understand and model the player characteristics and behaviors in order to develop a better AI. This paper presents a survey of the field, discussing the main concepts and proposing a taxonomy to better organize them. We also present several game platforms that can be used by player modeling and AI researchers. We believe that compiling this information can be important to the field, specially to new researchers.

Artificial intelligence in cyber defense

Abstract: The speed of processes and the amount of data to be used in defending the cyber space cannot be handled by humans without considerable automation. However, it is difficult to develop software with conventional fixed algorithms (hard-wired logic on decision making level) for effectively defending against the dynamically evolving attacks in networks. This situation can be handled by applying methods of artificial intelligence that provide flexibility and learning capability to software. This paper presents a brief survey of artificial intelligence applications in cyber defense (CD), and analyzes the prospects of enhancing the cyber defense capabilities by means of increasing the intelligence of the defense systems. After surveying the papers available about artificial intelligence applications in CD, we can conclude that useful applications already exist. They belong, first of all, to applications of artificial neural nets in perimeter defense and some other CD areas. From the other side - it has become obvious that many CD problems can be solved successfully only when methods of artificial intelligence are being used. For example, wide knowledge usage is necessary in decision making, and intelligent decision support is one of yet unsolved problems in CD.

Applications of Artificial Intelligence in Safe Human–Robot Interactions

Abstract: The integration of industrial robots into the human workspace presents a set of unique challenges. This paper introduces a new sensory system for modeling, tracking, and predicting human motions within a robot workspace. A reactive control scheme to modify a robot's operations for accommodating the presence of the human within the robot workspace is also presented. To this end, a special class of artificial neural networks, namely, self-organizing maps (SOMs), is employed for obtaining a superquadric-based model of the human. The SOM network receives information of the human's footprints from the sensory system and infers necessary data for rendering the human model. The model is then used in order to assess the danger of the robot operations based on the measured as well as predicted human motions. This is followed by the introduction of a new reactive control scheme that results in the least interferences between the human and robot operations. The approach enables the robot to foresee an upcoming danger and take preventive actions before the danger becomes imminent. Simulation and experimental results are presented in order to validate the effectiveness of the proposed method.

Online estimation of system parameters for artificial intelligence applications to load frequency control

Abstract: Making balance between generation and demand is the operating principle of the load frequency control. As the automatic generation control (AGC) has been commissioned to serve load-generation balance for several decades, the developmental roadmap of the AGC is leading to the adaptive or artificial intelligence (AI) applications to the controller design. Among the AI applications, the genetic algorithm (GA) and the fuzzy inference system (FIS) are often adopted to optimise AGC gains under the time-varying system conditions. Relevant studies indicate that the real-time acquisition of system states would be advantageous for AGC to drive system to the optimal condition. However, some of the system parameters are not easily accessible. The object of getting good estimate of system parameters in real-time basis would definitely enhance the performance of the intelligence applications to AGC. This study explores the use of recursive least square algorithm for acquiring the system parameters in real time. As such, a more accurate reference model would be gainfully used in the AI controller design. Issues related to the model validation in the simulation and field test are presented. Following that, the use of the reference model in the GA as well as the FIS applications for the optimal gain scheduling of the AGC is demonstrated.

Game AI as Storytelling

Abstract: Much research on artificial intelligence in games has been devoted to creating opponents that play competently against human players. We argue that the traditional goal of AI in games-to win the game-is but one of several interesting goals to pursue. We promote the alternative goal of making the human player’s play experience “better,” i.e., AI systems in games should reason about how to deliver the best possible experience within the context of the game. The key insight we offer is that approaching AI reasoning for games as “storytelling reasoning” makes this goal much more attainable. We present a framework for creating interactive narratives for entertainment purposes based on a type of agent called an experience manager. An experience manager is an intelligent computer agent that manipulates a virtual world to dynamically adapt the narrative content the player experiences, based on his or her actions and inferences about his or her preferred style of play. Following a theoretical perspective on game AI as a form of storytelling, we discuss the implications of such a perspective in the context of several AI technological approaches.

Artificial Intelligence and Human Institutions

Abstract: The book argues that successful applications of artificial intelligence are possible only within an understanding of human institutions and the limitations of technology.

GEP modeling of oxygen transfer efficiency prediction in aeration cascades

Abstract: Artificial intelligence is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent In the past few years, the applications of artificial intelligence methods have attracted the attention of many investigators Many artificial intelligence methods have been applied in various areas of civil and environmental engineering The aim of this study is to develop models to estimate oxygen transfer efficiency in nappe, transition and skimming flow regimes over stepped cascades For this aim, genetic expression programming, a new member of genetic computing techniques, is used It is similar, but not equivalent to genetic algorithms, nor genetic programming For nappe, transition and skimming flow regimes, three models are constructed using the experimental data The test results indicate that for the model equations obtained, the correlation coefficients are very high and the minimum square error values are less than 00033 So, genetic expression programming approach can be successfully used in stepped cascades to predict the oxygen transfer efficiency

What is an ideal logic for reasoning with inconsistency

Abstract: Many AI applications are based on some underlying logic that tolerates inconsistent information in a non-trivial way. However, it is not always clear what should be the exact nature of such a logic, and how to choose one for a specific application. In this paper, we formulate a list of desirable properties of "ideal" logics for reasoning with inconsistency, identify a variety of logics that have these properties, and provide a systematic way of constructing, for every n > 2, a family of such n-valued logics.

Artificial Intelligence for Computer Games

Abstract: The book presents some of the most relevant results from academia in the area of Artificial Intelligence for games. It emphasizes well theoretically supported work supported by developed prototypes, which should lead into integration ofacademic AI techniques into current electronic entertainment games.The book elaborates on the main results produced in Academia within the last 10 years regarding all aspects of Artificial Intelligence for games, including pathfinding, decision making, and learning. A general theme of the book is thecoverage of techniques for facilitating the construction of flexible not prescripted AI for agents in games. Regarding pathfinding, the book includes new techniques for implementing real-time search methods that improve the results obtained through AI, as well as techniques for learning pathfinding behavior by observing actual players.Regarding decision making, the book describes new techniques for authoring tools that facilitate the construction by game designers (typically nonprogrammers) of behavior controlling software, by reusing patterns or actualcases of past behavior. Additionally, the book will cover a number of approaches proposed for extending the essentially pre-scripted nature of current commercial videogames AI into a more interactive form of narrative, where the story emerges from the interaction with the player. Some of those approachesrely on a layered architecture for the character AI, including beliefs, intentions and emotions, taking ideas from research on agent systems. The book also includes chapters on techniques for automatically or semiautomatically learning complex behavior from recorded traces of human or automatic players using different combinations of reinforcement learning, case-based reasoning, neural networks and genetic algorithms.

General game playing in AI research and education

Abstract: Introduced in 2005 as a new AI Challenge and Competition, general game playing has quickly evolved into an established research area. More recently it is also gaining popularity as a useful addition to AI curricula at universities around the world. The first part of this paper will survey the research landscape of general game playing, which covers a broad range of classic AI topics, including knowledge representation, search, planning and learning. The second part will argue that general game playing provides a unique approach to teaching a number of different topics such as problem solving by search, logic, logic programming and planning. The inherent competitive aspect also can be used as a great motivator for students to design and implement their own AI systems.

The Evolution of Gamebots for 3D First Person Shooter (FPS)

Abstract: The implementation of Artificial Intelligence (AI)in 3-Dimensional (3D) First Person Shooter (FPS) game is quite general nowadays. Most of the conventional AI bots created are mostly from hard coded AI bots. Hence, it has limited the dynamicity of the AI bots and therefore it brings to a fixed strategy for gaming. The main focus of this paper is to discuss the methodologies used in generating the AI bots that is competitive in the FPS gaming. In this paper, a decision making structure is proposed. It has been combined with the Evolutionary Programming in generating the required AI controllers. Hence, there are two methodology discussions involved: (1) the proposed decision making structure and (2)the Evolutionary Programming used. The experiments show highly promising testing results after the generated AI bots have been tested and compared with the conventional ruled based AI bots. It proves that the generated AI bots using the combination of Evolutionary Programming and decision making structure performed better than those AI bots generated using conventional ruled based strategy which is hard coded and time consuming to develop.

Artificial intelligence technologies in business and engineering

Abstract: Artificial intelligence (AI) is making its way back into the mainstream of corporate technology, this time at the core of business systems which are providing competitive advantage in all sorts of industries, including electronics, manufacturing, marketing, human resource, financial services software, medicine, entertainment, engineering and communications. Designed to leverage the capabilities of humans rather than replace them, today's AI technology enables an extraordinary array of applications that forge new connections among people, computers, knowledge, and the physical world. Some AI enabled applications are information distribution and retrieval, database mining, product design, manufacturing, inspection, training, user support, surgical planning, resource scheduling, and complex resource management. AI technologies help enterprises reduce latency in making business decisions, minimize fraud and enhance revenue opportunities.

Decision tree-based algorithms for implementing bot AI in UT2004

Abstract: This paper describes two different decision tree-based approaches to obtain strategies that control the behavior of bots in the context of the Unreal Tournament 2004. The first approach follows the traditional process existing in commercial videogames to program the game artificial intelligence (AI), that is to say, it consists of coding the strategy manually according to the AI programmer's experience with the aim of increasing player satisfaction. The second approach is based on evolutionary programming techniques and has the objective of automatically generating the game AI. An experimental analysis is conducted in order to evaluate the quality of our proposals. This analysis is executed on the basis of two fitness functions that were defined intuitively to provide entertainment to the player. Finally a comparison between the two approaches is done following the subjective evaluation principles imposed by the "2k bot prize" competition.

Information Retrieval and Question Answering NLP Approach: An Artificial Intelligence Application

Abstract: Natural Language Processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, the focus in AI applications in NLP was on knowledge representation, logical reasoning, and constraint satisfaction—first applied to semantics and later to the grammar. In the last decade, a dramatic shift in the NLP research has led to the prevalence of very large-scale applications of statistical methods, such as machine learning and data mining. Naturally, this also opened the way to the learning and optimization methods that constitute the core of modern AI, most notably genetic algorithms and neural networks. In this talk I will give an overview of the current trends in NLP and discuss the possible applications of traditional AI techniques and their combination in this fascinating area. Question- Answering(QA)aims at delivering concise information that contains answers to user questions. Intelligent Questing Answering Systems (IQASs) aim to help students become better readers. The computational challenges involved are (1) to assess the students' natural language inputs and (2) to provide appropriate feedback and guide students through the IQASs.

Artificial intelligence and human thinking

Abstract: Research in AI has built upon the tools and techniques of many different disciplines, including formal logic, probability theory, decision theory, management science, linguistics and philosophy. However, the application of these disciplines in AI has necessitated the development of many enhancements and extensions. Among the most powerful of these are the methods of computational logic. I will argue that computational logic, embedded in an agent cycle, combines and improves upon both traditional logic and classical decision theory. I will also argue that many of its methods can be used, not only in AI, but also in ordinary life, to help people improve their own human intelligence without the assistance of computers.

Intelligent Learning Automata-based Strategies Applied to Personalized Service Provisioning in Pervasive Environments

Abstract: In this paper, we propose a novel solution to the problem of identifying services of high quality. The reported solutions to this problem have, in one way or the other, resorted to using so-called “Reputation Systems” (RSs). Although these systems can offer generic recommendations by aggregating user-provided opinions about the quality of the services under consideration, they are, understandably, prone to “ballot stuffing” and “badmouthing” in a competitive marketplace. In general, unfair ratings may degrade the trustworthiness of RSs, and additionally, changes in the quality of service, over time, can render previous ratings unreliable. As opposed to the reported solutions, in this paper, we propose to solve the problem using tools provided by Learning Automata (LA), which have proven properties capable of learning the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational The first author gratefully acknowledges the financial support of the Ericsson Research, Aachen, Germany, and the third author is grateful for the partial support provided by NSERC, the Natural Sciences and Engineering Research Council of Canada. A preliminary version of this paper was presented at IEA/AIE’10, the 2010 International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Cordoba, Spain, in June 2010. A. Yazidi · O.-C. Granmo · B.J. Oommen ( ) Department of ICT, University of Agder, Grimstad, Norway e-mail: oommen@scs.carleton.ca A. Yazidi e-mail: anis.yazidi@uia.no O.-C. Granmo e-mail: ole.granmo@uia.no B.J. Oommen School of Computer Science, Carleton University, Ottawa, Canada K1S 5B6 complexity. In addition to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems associated with RSs. Instead, it gradually learns the identity and characteristics of the users which provide fair ratings, and of those who provide unfair ratings, even when these are a consequence of them making unintentional mistakes. Comprehensive empirical results show that our LA-based scheme efficiently handles any degree of unfair ratings (as long as these ratings are binary—the extension to non-binary ratings is “trivial”, if we use the S-model of LA computations instead of the P -model). Furthermore, if the quality of services and/or the trustworthiness of the users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA-based scheme forms a promising basis for improving the performance of RSs in general.

Machine Learning and Data Mining

Abstract: One of the major AI applications is the development of intelligent autonomous robots. Since flexibility and adaptivity are important features of really intelligent agents, research into learning mechanisms and the development of machine learning algorithms is one of the most important branches of AI. After motivating and introducing basic concepts of machine learning like classification and approximation, this chapter presents basic supervised learning algorithms such as the perceptron, nearest neighbor methods and decision tree induction. Unsupervised clustering methods and data mining software tools complete the picture of this fascinating field.

A GIS Integrated Solution for Traffic Management

Abstract: With the increased dissemination and computing power of mobile devices, it is now possible to execute distributed artificial intelligence applications for various situations: intelligent routing using algorithms, planning, distributed optimization of traffic lights. Our solution provides a complex way of integrating intelligent traffic simulation with a distributed traffic management system that is both useful for the drivers and for the persons in charge with traffic light adjustment in urban regions. At the top lays a desktop application that provides the main features and has a distributed built-in server to process the data. This one is connected with a web application capable of showing the optimum route for a point of interest. The output is adjusted accordingly with a set of parameters related to weather conditions, road restrictions, driver's average speed or specific hour intervals. The application that runs on mobile phones it's very useful to collect the data that will be stored daily in the database as trace logs.

The Application of AI for the Non Player Character in Computer Games

Abstract: Artificial intelligence technology for non player character has become the key technology for computer games. This paper describes the attributes of non player character and application of artificial intelligence. Then it discusses the interaction designing of artificial intelligence in designing computer games. The latter part focuses on researching the realization of artificial intelligence for non player character. At last, the paper envisions the application of AI will attract more players.

Construction and adaptation of ai behaviors in computer games

Abstract: Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC's in computer games is that construction of high quality AI behaviors is a hard problem. There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player experience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience. The two issues give rise to a couple of interesting questions that need to be investigated: (a) How can the AI behavior construction process be simplified so that a novice user (without programming and design experience) can easily conduct the authoring activity and (b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach. To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation formalism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player experience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world. The work contributes in the following three ways by providing: (1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; (2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and (3) Design, debugging and testing scaffolding to help novice users modify their authored AI behaviors and achieve higher quality modified AI behaviors compared to their original unmodified behaviors.

Emergency Control in Electric Power Systems within Smart Grid Concept

Abstract: The objective trends in electric power systems (EPSs) are discussed. New measurement, communication and control tools, information and computer technologies are considered as good possibilities for improving EPS controllability. Current emergency control system in Russia is presented. A modern approach to monitoring, forecasting and control is suggested. Some artificial intelligence applications for development of automatic emergency control in EPS are discussed.

Limitations of Logic

Abstract: Until the end of the 20th century many logicians believed that theorem provers for first-order logic will be the major component of intelligent agents. Almost all successful modern AI applications however use different formalisms. This is due to some severe problems with first-order logic that we will explain in this chapter.