Showing papers on "Applications of artificial intelligence published in 2010"
TL;DR: Comparative research review of three famous artificial intelligent techniques in financial market shows that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns.
Abstract: Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.
404 citations
TL;DR: This paper explores various sub-fields of AI that are most suitable for solving practical problems relevant to SCM and identifies the most fruitful areas of SCM in which to apply AI.
Abstract: Artificial intelligence (AI) was introduced to develop and create “thinking machines” that are capable of mimicking, learning, and replacing human intelligence. Since the late 1970s, AI has shown great promise in improving human decision-making processes and the subsequent productivity in various business endeavors due to its ability to recognise business patterns, learn business phenomena, seek information, and analyse data intelligently. Despite its widespread acceptance as a decision-aid tool, AI has seen limited application in supply chain management (SCM). To fully exploit the potential benefits of AI for SCM, this paper explores various sub-fields of AI that are most suitable for solving practical problems relevant to SCM. In so doing, this paper reviews the past record of success in AI applications to SCM and identifies the most fruitful areas of SCM in which to apply AI.
224 citations
01 Jan 2010
194 citations
TL;DR: Various AI based techniques focusing on development of Intrusion detection system (IDS) have been reviewed and related studies have been compared by their source of audit data, processing criteria, technique used, dataset, classifier design, feature reduction technique employed and other experimental environment setup.
Abstract: The Internet connects hundreds of millions of computers across the world running on multiple hardware and software platforms providing communication and commercial services. However, this interconnectivity among computers also enables malicious users to misuse resources and mount Internet attacks. The continuously growing Internet attacks pose severe challenges to develop a flexible, adaptive security oriented methods. Intrusion detection system (IDS) is one of most important component being used to detect the Internet attacks. In literature, different techniques from various disciplines have been utilized to develop efficient IDS. Artificial intelligence (AI) based techniques plays prominent role in development of IDS and has many benefits over other techniques. However, there is no comprehensive review of AI based techniques to examine and understand the current status of these techniques to solve the intrusion detection problems. In this paper, various AI based techniques have been reviewed focusing on development of IDS. Related studies have been compared by their source of audit data, processing criteria, technique used, dataset, classifier design, feature reduction technique employed and other experimental environment setup. Benefits and limitations of AI based techniques have been discussed. The paper will help the better understanding of different directions in which research has been done in the field of IDS. The findings of this paper provide useful insights into literature and are beneficial for those who are interested in applications of AI based techniques to IDS and related fields. The review also provides the future directions of the research in this area.
112 citations
Proceedings Article•
05 Jul 2010TL;DR: Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac-Man environment using search heuristics, evaluation functions, and feature functions.
Abstract: The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.
66 citations
18 Nov 2010
TL;DR: This paper focuses on in-depth analysis and discussion of applicable perspective and development of artificial Intelligence in modern physical education technology, and raises the corresponding development strategies to the use of artificial intelligence in modem sports educational technology.
Abstract: Artificial intelligence is a comprehensive cutting-edge discipline which is developing, and an important research direction in the fields of science computer and technology. This paper uses the principles and methods of artificial intelligence on the basis of describing the concept and research areas of artificial intelligence, focuses on in-depth analysis and discussion of applicable perspective and development of artificial intelligence in modern physical education technology, and raises the corresponding development strategies to the use of artificial intelligence in modem sports educational technology, in order to provide the theoretical support for the establishment and development of modern physical education technical disciplines.
21 citations
19 Jun 2010
TL;DR: A definition for real- time team-mate AI is proposed, work to date to implement real-time team-mates AI for games is reviewed, and a brief discussion of significant issues about the state of the art are discussed.
Abstract: Many contemporary games are team-based and there is a growing interest in, and need for, advances in team-mate AI for games. However, although there have been surveys of agent AI in games, to date there has been no survey of work on team-mate AI. Furthermore, the concept of "team-mate AI" is not currently well delineated to distinguish between work on independently-acting agents that happen to be on the same side from work on agents the coordinate their behaviors and decision-making in terms of their teammates behaviors, intentions, and the like. Also, it is important to distinguish between game AI that is used as an optimization technique from real-time game AI, so this paper proposes a definition for real-time team-mate AI (highlighted with examples by game genre), reviews work to date to implement real-time team-mate AI for games in terms of a number of AI research areas (e.g., coordinated action, prediction, learning), and concludes with a brief discussion of significant issues about the state of the art.
20 citations
TL;DR: Deep Blue, the chess playing supercomputer, was developed to defeat the top rated human chess player and successfully did so by defeating Gary Kasporov in 1997, but it only played chess; it did not play checkers, or any other games.
Abstract: Generalized intelligence is much more difficult than originally anticipated when Artificial Intelligence (AI) was first introduced in the early 1960s. Deep Blue, the chess playing supercomputer, was developed to defeat the top rated human chess player and successfully did so by defeating Gary Kasporov in 1997. However, Deep Blue only played chess; it did not play checkers, or any other games. Other examples of AI programs which learned and played games were successful at specific tasks, but generalizing the learned behavior to other domains was not attempted. So the question remains: Why is generalized intelligence so difficult? If complex tasks require a significant amount of development, time and task generalization is not easily accomplished, then a significant amount of effort is going to be required to develop an intelligent system. This approach will require a system of systems approach that uses many AI techniques: neural networks, fuzzy logic, and cognitive architectures.
14 citations
TL;DR: The idea of generating non-trivial behaviour by analysing vast amounts of data has enabled recommendation systems, search engines, spam filters, optical character recognition, machine translation and speech recognition, among other things.
14 citations
TL;DR: In this paper, a number of computational methods have been employed in an effort to model and simulate air quality (AQ) in order to assess, manage and forecasting air pollution.
Abstract: The problem of assessing, managing and forecasting air pollution (AP) has been in the top of the environmental agenda for decades, and contemporary urban life has made this problem more intense and severe in terms of quality of life degradation. A number of computational methods have been employed in an effort to model and simulate air quality (AQ). Air pollution is related to various substances, is affected by physical and chemical mechanisms of various spatial and temporal scales, and is regulated in terms of target values that are different to each other. Thus, AP requires for computational and knowledge management tools that are able to deal with its complex (and exiting from the scientific point of view) nature. Moreover, such methods should be able to deal with missing observation data, data of mixed nature (be it nominal, categorical, binary or other), and imitate the behavior and the \"intelligence\" of the phenomena that need to be modeled and simulated. This means that deterministic modeling, employing fluid mechanics, atmospheric chemistry and physics (the \"traditional way for modeling AQ\") are not able to \"catch\" all the aspects of the AP problem. Other methods should be employed, that are able to deal with knowledge extraction and management, and are able to map knowledge into the \"intelligence\" of the algorithms that they apply. On this basis, Artificial Intelligence should be used. This is a thesis recognized already from the 90ties, where the first sets of scientific publications in areas like neural networks and fussy logic have appeared with applications in AQ.
12 citations
01 Jan 2010
TL;DR: The clear message is that AI has to join forces with neuroscience and other brain disciplines in order to make a step towards the development of truly intelligent machines.
Abstract: Artificial Intelligence (AI) is a branch of computer science concerned with making computers behave like humans. At least this was the original idea. However, it turned out that this is no task easy to be solved. This article aims to give a comprehensible review on the last 60 years of artificial intelligence taking a philosophical viewpoint. It is outlined what happened so far in AI, what is currently going on in this research area, and what can be expected in future. The goal is to mediate an understanding for the developments and changes in thinking in course of time about how to achieve machine intelligence. The clear message is that AI has to join forces with neuroscience and other brain disciplines in order to make a step towards the development of truly intelligent machines.
TL;DR: Artificial Neural Network, one of the Artificial Intelligence techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG), shows promising results after testing.
Abstract: In this paper, Artificial Neural Network, one of the Artificial Intelligence (AI) techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG) is proposed. Artificial neural networks have been considered due to their ability for real time control, simpler calculations and adaptability to different operating conditions. Neuro-controllers are much more effective, fast acting than conventional controllers. Neural network for controlling Step voltage regulator (SVR) with line rise compensation (LRC) /line drop compensation (LDC) function has been presented. The neural network based controller has been simulated for a radial distribution system with DG and the neuro-controller shows promising results after testing. Keywords Artificial Intelligence, Artificial neural network, Dispersed generation, Distribution system, Line drop compensation, Line rise compensation, Step Voltage regulator, Voltage / Reactive power control.
18 Jul 2010
TL;DR: This paper introduces an alternate architecture that is inspired from the biological world, and is fundamentally different from traditional processing which uses arithmetic operations, targeted towards robust artificial intelligence applications.
Abstract: The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. Despite the abundance of AI algorithms and machine learning techniques, the state of the art still fails to capture the rich analytical properties of biological beings or their robustness. Most parallel hardware architectures tend to combine Von Neumann style processors to make a multi-processor environment and computation is based on Arithmetic and Logic Units (ALU). This paper introduces an alternate architecture that is inspired from the biological world, and is fundamentally different from traditional processing which uses arithmetic operations. The architecture proposed here is targeted towards robust artificial intelligence applications.
Journal Article•
22 Aug 2010-World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering
TL;DR: A comprehensive review of applications of AI techniques in manufacturability evaluation of sheet metal parts, die design and process planning of metal stamping die is presented in this paper, where salient features of major research work published in the area of metal stamping are presented in tabular form and scope of future research work is identified.
Abstract: Metal stamping die design is a complex, experiencebased and time-consuming task. Various artificial intelligence (AI) techniques are being used by worldwide researchers for stamping die design to reduce complexity, dependence on human expertise and time taken in design process as well as to improve design efficiency. In this paper a comprehensive review of applications of AI techniques in manufacturability evaluation of sheet metal parts, die design and process planning of metal stamping die is presented. Further the salient features of major research work published in the area of metal stamping are presented in tabular form and scope of future research work is identified. Keywords—Artificial Intelligence, Die design, Manufacturability Evaluation, Metal Stamping Die.
Proceedings Article•
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TL;DR: A dog AI making use of influence mapping, state machines and A* pathfinding to respond intelligently to real-life shepherding commands issued by a high-level shepherd AI steering the flock of sheep through waypoints on a variety of maps by using pathfinding and influence maps is presented.
Abstract: Shepherding with a dog presents an interesting challenge for artificial intelligence, with multiple intelligent systems assessing and interacting with each other in order to achieve a variety of goals. We present a solution to this problem, which consists of a dog AI making use of influence mapping, state machines and A* pathfinding to respond intelligently to real-life shepherding commands issued by a high-level shepherd AI steering the flock of sheep through waypoints on a variety of maps by using pathfinding and influence maps. The role of the AI shepherd can also be taken by a human player (using either a point and click or voice recognition interface) for matches against the artificial shepherd which proved to be a worthy opponent for human testers. The system was evaluated through user testing and provided a high degree of realism and engaging gameplay relying heavily on the workings of the presented AI components.
14 Jun 2010
TL;DR: This paper focuses the discussion on power system reliability evaluation and this natural transition from AI topics to a more sophisticated software design, known as intelligent agent (IA) technology, instead of applying AI techniques to improve a single stage of the Monte Carlo Simulation (MCS).
Abstract: A natural movement towards artificial intelligence (AI) techniques took place in the last years in power system analysis. Many research works have used AI topics like search techniques, knowledge representation, reasoning and learning systems, as well as heuristic tools to address power system problems. This paper focuses the discussion on power system reliability evaluation and this natural transition from AI topics to a more sophisticated software design, known as intelligent agent (IA) technology. Instead of applying AI techniques to improve a single stage of the Monte Carlo Simulation (MCS), the IA architecture explores new ways to support AI topics. However, this natural movement needs to be managed through the proposal of a modern framework of power system tools, where several different techniques have to be combined in order to maximize each one's benefits and advantages.
Proceedings Article•
20 Feb 2010TL;DR: The Artificial Intelligence (AI) ingredient permits to explore a greater range of options, enabling the staff to analyze more possible options in the same amount of time, together with a deeper analysis of these options.
Abstract: Military decision making demands an increasing ability to understand and structure the critical information on the battlefield. As the military evolves into a networked force, decision makers should select and filter information across the battlefield in a timely and efficient manner. Human capability in analyzing all the data is not sufficient because the modern battlefield is characterized by dramatic movements, unexpected evolutions, chaotic behavior and non-linear situations. The Artificial Intelligence (AI) ingredient permits to explore a greater range of options, enabling the staff to analyze more possible options in the same amount of time, together with a deeper analysis of these options.
01 Nov 2010
TL;DR: Procedures of constructing a multi-linguistic agriculture ontology system are introduced, and the using scenario of AOS is demonstrated, which would benefit the agricultural knowledge management and other semantic applications.
Abstract: Domain ontologies are the vital infrastructure for semantic web and AI applications. An system for agriculture online service(AOS) is presented in this paper. The AOS provides a shared portal for ontology retrieval, which would benefit the agricultural knowledge management and other semantic applications. This paper introduces procedures of constructing a multi-linguistic agriculture ontology system, demonstrates the using scenario of AOS. The architecture of AOS and the related theory and technique are detailed.
TL;DR: This paper revisited the epistemology of artificial intelligence in the light of the opposition between the "sciences of nature" and the sciences of culture, which has been introduced by German neo-Kantian philosophers.
Abstract: Artificial intelligence has often been seen as an attempt to reduce the natural mind to informational processes and, consequently, to naturalize philosophy. The many criticisms that were addressed to the so-called “old-fashioned AI” do not concern this attempt itself, but the methods it used, especially the reduction of the mind to a symbolic level of abstraction, which has often appeared to be inadequate to capture the richness of our mental activity. As a consequence, there were many efforts to evacuate the semantical models in favor of elementary physiological mechanisms simulated by information processes. However, these views, and the subsequent criticisms against artificial intelligence that they contain, miss the very nature of artificial intelligence, which is not reducible to a “science of the nature”, but which directly impacts our culture. More precisely, they lead to evacuate the role of the semantic information. In other words, they tend to throw the baby out with the bath-water. This paper tries to revisit the epistemology of artificial intelligence in the light of the opposition between the “sciences of nature” and the “sciences of culture”, which has been introduced by German neo-Kantian philosophers. It then shows how this epistemological view opens on the many contemporary applications of artificial intelligence that have already transformed—and will continue to transform—all our cultural activities and our world. Lastly, it places those perspectives in the context of the philosophy of information and more particularly it emphasizes the role played by the notions of context and level of abstraction in artificial intelligence.
Book•
19 Jan 2010
TL;DR: A Guide to Artificial Intelligence with Visual Prolog is less like a traditional textbook and more like a workshop where you can learn at your own pace - so you can start harnessing the power of Visual Pro Log for whatever your mind can dream up.
Abstract: Get started with the simplest, most powerful prolog ever: Visual Prolog If you want to explore the potential of Artificial Intelligence (AI), you need to know your way around Prolog.Prolog - which stands for "programming with logic" - is one of the most effective languages for building AI applications, thanks to its unique approach. Rather than writing a program that spells out exactly how to solve a problem, with Prolog you define a problem with logical Rules, and then set the computer loose on it. This paradigm shift from Procedural to Declarative programming makes Prolog ideal for applications involving AI, logic, language parsing, computational linguistics, and theorem-proving.Now, Visual Prolog (available as a free download) offers even more with its powerful Graphical User Interface (GUI), built-in Predicates, and rather large provided Program Foundation Class (PFC) libraries. A Guide to Artificial Intelligence with Visual Prolog is an excellent introduction to both Prolog and Visual Prolog. Designed for newcomers to Prolog with some conventional programming background (such as BASIC, C, C++, Pascal, etc.), Randall Scott proceeds along a logical,easy-to-grasp path as he explains the beginnings of Prolog, classic algorithms to get you started, and many of the unique features of Visual Prolog.Readers will also gain key insights into application development, application design, interface construction, troubleshooting, and more. In addition, there are numerous sample examples to learn from, copious illustrations and information on helpful resources.A Guide to Artificial Intelligence with Visual Prolog is less like a traditional textbook and more like a workshop where you can learn at your own pace - so you can start harnessing the power of Visual Prolog for whatever your mind can dream up.
Proceedings Article•
16 Jun 2010
TL;DR: In this article, the authors describe the development of an AI system for the First-Person Shooter (FPS) videogame genre that avoids this problem through the creation of adaptable rule-based behaviors, enabling AI characters to learn the best strategy for any given situation.
Abstract: Videogame Artificial Intelligence (AI) is growing more complex and realistic to keep up with player requirements. Despite this, most games still fail to provide true adaptability in their AI, resulting in situations where an intermediate level player is able to predict the AI's behavior in a short amount of time, leading to a predictable and boring game experience. Creating a truly adaptive AI would greatly benefit a videogame's intrinsic value by providing a more immersive and unpredictable game experience. This paper describes the development of an AI system for the First-Person Shooter (FPS) videogame genre that avoids this problem through the creation of adaptable rule-based behaviors, enabling AI characters to learn the best strategy for any given situation.
Journal Article•
TL;DR: A unique approach to teaching a general purpose Artificial Intelligence course which strengthens traditional AI concepts through a semester long industrial robotic game playing project and requires an industrial robotics laboratory that includes robotic arms which are available for students to use.
Abstract: A unique approach to teaching a general purpose Artificial Intelligence (AI) course is presented in this paper. The course provides an active learning environment which strengthens traditional AI concepts through a semester long industrial robotic game playing project. It requires an industrial robotics laboratory that includes robotic arms which are available for students to use. Several foundational topics related to AI are implemented by the students on an actual robotic arm, including basic robotic coordinate systems and transformations, game playing algorithms, image segmentation, and feed forward neural networks.
TL;DR: This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately, and explains why bio-inspired solutions may fail and how to correct these failures.
Abstract: Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances). Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.
30 Sep 2010
TL;DR: This paper aims to show adaptation using online evolution is feasible and that it can can be incorporated with minimal change to the existing AI.
Abstract: Traditional approaches to game AI often feature behaviour that is scripted and predictable. Previous attempts at adaptive AI have struggled to get agents to learn quickly enough. This paper aims to show adaptation using online evolution is feasible and that it can can be incorporated with minimal change to the existing AI. A new approach is presented for evolving game agents online using an Evolution Strategy.
01 Jan 2010
TL;DR: In the twenty-eight sgai 2008 conference on innovative techniques and applications of artificial intelligence as the choice of reading as mentioned in this paper, a condition is the on that will make you feel that you must read.
Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd research and development in intelligent systems xxv proceedings of ai 2008 the twenty eighth sgai international conference on innovative techniques and applications of artificial intelligence as the choice of reading, you can find here.
06 Oct 2010
TL;DR: An insight into applications of AI techniques in software engineering and how innovative application of AI can assist in achieving ever competitive and firm schedules for software development projects as well as Information Technology (IT) management.
Abstract: Artificial Intelligence (AI) techniques have been successfully applied in many areas of software engineering. The complexity of software systems has limited the application of AI techniques in many real world applications. This talk provides an insight into applications of AI techniques in software engineering and how innovative application of AI can assist in achieving ever competitive and firm schedules for software development projects as well as Information Technology (IT) management. The pros and cons of using AI techniques are investigated and specifically the application of AI in IT management, software application development and software security is considered.
30 May 2010
TL;DR: The author gives a personal view on the current development direction of smart home, and propos the “wisdom” of a smart home should be based on the biological intelligence, rather than a series of automated electrical equipments.
Abstract: In this paper, the author gives a personal view on the current development direction of smart home, and propos the “wisdom” of a smart home should be based on the biological intelligence, rather than a series of automated electrical equipments, and emphasizes that the central control computer system is the core part of the smart home, the control model of software is the heart part of Smart Home, the artificial intelligence applications for the smart home are the ultimate goal.
25 Oct 2010
TL;DR: This paper studied the two most commonly used artificial intelligence methods to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex.
Abstract: In this paper, we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex. On this basis, the author combines econometric analysis of the experience, through logistic regression the model can filter the variables with a high degree of correlation, which greatly reduces the complexity of the model, while the model has a better explanation, and thus improve the effect of neural network prediction models. The method can also be used for a variety of artificial intelligence applications to improve forecast model results.
Journal Article•
TL;DR: The basis of the definition of artificial intelligence is explained, detailed analysis of the applications and the current state of development are explained, and depth of the current development of new artificial intelligence technology to human impact is explained.
Abstract: AI(Artificial Intelligence) is to use artificial methods and techniques to imitate,extension and expansion of human intelligence,to achieve some of the "machine thinking." In explaining the basis of the definition of artificial intelligence,detailed analysis of the applications of artificial intelligence and the current state of development,depth of the current development of new artificial intelligence technology to human impact,and put forward the prospects for future development of artificial intelligence,the artificial the direction of intelligent applications have a certain value.
01 Jan 2010
TL;DR: The generalization of OGY method is applied to the case of multi-ergodic nonlinear infinite dimensional systems and complexity theory also for the parts of stochastic behavior of plasma via temporal and spatial Jaynes entropy, KSE, with fuzzy logic and ANNs methods.
Abstract: Many applications of computer software involve the modeling and control of systems which depend on both discrete and continuous variables, and so a good understanding of the interaction between these components of the system is important. We apply the generalization of OGY method to the case of multi-ergodic nonlinear infinite dimensional systems and complexity theory also for the parts of stochastic behavior of plasma via temporal and spatial Jaynes entropy, KSE, with fuzzy logic and ANNs methods.