Showing papers on "Applications of artificial intelligence published in 2006"

Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach

Abstract: Priority-dispatching rules have been studied for many decades, and they form the backbone of much industrial scheduling practice. Developing new dispatching rules for a given environment, however, is usually a tedious process involving implementing different rules in a simulation model of the facility under study and evaluating the rule through extensive simulation experiments. In this research, an innovative approach is presented, which is capable of automatically discovering effective dispatching rules. This is a significant step beyond current applications of artificial intelligence to production scheduling, which are mainly based on learning to select a given rule from among a number of candidates rather than identifying new and potentially more effective rules. The proposed approach is evaluated in a variety of single machine environments, and discovers rules that are competitive with those in the literature, which are the results of decades of research.

Applications of artificial intelligence in bioinformatics: A review

Abstract: Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology. With the availability of different types of AI algorithms, it has become common for the researchers to apply the off-shelf systems to classify and mine their databases. At present, with various intelligent methods available in the literature, researchers are facing difficulties in choosing the best method that could be applied to a specific data set. Researchers need tools, which present the data in a comprehensible fashion, annotated with context, estimates of accuracy and explanation. This article aims to review the use of AI in the areas of bioinformatics and computational molecular biology (DNA sequencing). These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. The underlying motivation for many of the bioinformatics and DNA sequencing approaches is the evolution of organisms and the complexity of working with erroneous data. This article also describes the kind of software programs which were developed by the research community in order to (1) search, classify and mine different available biological databases; (2) simulate biological experiments with and without errors.

Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search

Abstract: This paper describes a novel approach to medical diagnosis based on the SP theory of computing and cognition. The main attractions of this approach are: a format for representing diseases that is simple and intuitive; an ability to cope with errors and uncertainties in diagnostic information; the simplicity of storing statistical information as frequencies of occurrence of diseases; a method for evaluating alternative diagnostic hypotheses that yields true probabilities; and a framework that should facilitate unsupervised learning of medical knowledge and the integration of medical diagnosis with other AI applications.

Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior

Abstract: While the term neural networks may be unfamiliar to many organizational psychologists, exciting new applications of artificial intelligence are attracting notice among organizational behavior researchers. In "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", authors David Scarborough and Mark Somers bring researchers, academics, and practitioners up to speed on this emerging field, in which powerful computing capabilities offer new insights into longstanding, complex I/O questions such as employee selection and behavioral prediction. Neural networks mimic the way the human brain works, using interconnected nodes and feedback loops to "learn" to recognize even subtle patterns in vast amounts of data. They can process data far more quickly and efficiently than conventional techniques can, and produce better empirical results. They are especially useful for modeling nonlinear processes. The book traces the development of this methodology and demonstrates how it opens up new ways of thinking about traditional problems. Academic researchers will gain a design template for studying both the linear and non-linear elements of a given problem, and thus enhance their own research.

Artificial intelligence and natural magic

Abstract: Robotics with AI is part of a long tradition that has run from ancient times that treated the precursors of robots, the automata, as part of Natural Magic or conjury Deception is an integral part of AI and robotics; in some ways they form a science of illusion There are many robot tasks, such as caring for the elderly, minding children, doing domestic chores and being companionable, that involve working closely with humans and so require some illusion of animacy and thought We discuss how the natural magic of robotics is assisted by the cultural myth of AI together with innate human predispositions such as zoomorphism, the willing suspension of disbelief and a tendency to interpret AI devices as part of the social world This approach provides a justifiable way of meeting the goals of AI and robotics provided that researchers do not allow themselves to be deceived by their own illusions

Applications of Artificial Intelligence Systems in the Analysis of Epidemiological Data

Abstract: A brief review of the germane literature suggests that the use of artificial intelligence (AI) statistical algorithms in epidemiology has been limited. We discuss the advantages and disadvantages of using AI systems in large-scale sets of epidemiological data to extract inherent, formerly unidentified, and potentially valuable patterns that human-driven deductive models may miss.

Biometrics, Computer Security Systems and Artificial Intelligence Applications

Abstract: This book contains over 30 articles by well-known scientists/engineers reflecting the latest developments in advanced computer systems and their applications within Biometrics, Information Technology Security and Artificial Intelligence.

Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management

Abstract: Decision support for real-time traffic management is a critical component for the success of intelligent transportation systems. Theoretically, microscopic simulation models can be used to evaluate traffic management strategies in real time before a course of action is recommended. However, the problem is that the strategies would have to be evaluated in real time; this might not be computationally feasible for large-scale networks and complex simulation models. To address this problem, two artificial intelligence (AI) paradigms—support vector regression (SVR) and case-based reasoning (CBR)—are presented as alternatives to the simulation models as a decision support tool. Specifically, prototype SVR and CBR decision support tools are developed and used to evaluate the likely impacts of implementing diversion strategies in response to incidents on a highway network in Anderson, South Carolina. The performances of the two prototypes are then evaluated by a comparison of their predictions of traffic conditio...

Preferences in answer set programming

Abstract: Preferences play a major role in many AI applications. We give a brief overview of methods for adding qualitative preferences to answer set programming, a promising declarative programming paradigm. We show how these methods can be used in a variety of different applications such as configuration, abduction, diagnosis, inconsistency handling and game theory.

Towards Integrating AI Story Controllers and Game Engines: Reconciling World State Representations

Abstract: : Recently, many AI researchers working on interactive storytelling systems have turned to off-the-shelf game engines for simulation and visualization of virtual 3D graphical worlds. Integrating AI research into game engines can be difficult due to the fact that game engines typically do not use symbolic or declarative representations of characters, settings, or actions. This is particularly true for interactive storytelling applications that use an AI story controller to subtly manipulate a virtual world in order to bring about a structured narrative experience for the user. In this paper, I describe a general technique for translating between an arbitrary game engine's proprietary and procedural world state representation into a declarative form that can be used by an AI story controller. The work is placed in the context of building a narrative- based training simulation.

Evolution, Sociobiology, and the Future of Artificial Intelligence

Abstract: What will AI systems be like in the near and long terms? Basically, we'll get the AI that people are willing to pay for. Consequently, many specialized applications will appear long before AI demonstrates its "Manifest Destiny" of human-level general intelligence. The AI demonstrations and applications we're going to see in the near future will trend strongly toward "cognitive prostheses" - systems that do well things that humans do poorly or don't like to do. Both near-term and far-future systems will need to interact smoothly with humans, which will put special constraints on them. In particular, to build systems that we'll trust and want to use, we'll need to carefully consider and craft their implicit and explicit values

Historical and current machine intelligence

Abstract: In this article, we provide an initial insight into the study of MI and what it means for a machine to be intelligent. We discuss how MI has progressed to date and consider future scenarios in a realistic and logical way as much as possible. To do this, we unravel one of the major stumbling blocks to the study of MI, which is the field that has become widely known as "artificial intelligence" (AI)

Teaching AI through machine learning projects

Abstract: An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. It is generally recognized that an introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach [9]. Others have been working to integrate Robotics into the AI course [1, 2, 3].We present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence course around the theme of machine learning. This involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning also provides a bridge between AI technology and modern software engineering. In his article, Mitchell discusses the increasingly important role that machine learning plays in the software world and identifies three important areas: data mining, difficult-to-program applications, and customized software applications [6].We have developed a suite of adaptable, hands-on laboratory projects that can be closely integrated into the introductory AI course. Each project involves the design and implementation of a learning system which will enhance a particular commonly-deployed application. The goal is to enhance the student learning experience in the introductory artificial intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the important relationship between AI and computer science in general, and software development in particular. Details on this project as well as samples of course materials developed are published in [4, 5, 7, 8] and are available at the project website at http://uhaweb.hartford.edu/compsci/ccli.We present an overview of our work along with a detailed presentation of one of these projects and how it meets our goals.The project involves the development of a learning system for web document classification. Students investigate the process of classifying hypertext documents, called tagging, and apply machine learning techniques and data mining tools for automatic tagging. Our experiences using the projects are also presented.

5 th Anniversary Special Issue — CALL Technologies and the Digital Learner ARTIFICIAL INTELLIGENCE AND IDIOMATICITY 1

Abstract: Despite immense progress in Artificial Intelligence (AI) technologies, the deployment of AI knowledge systems for idiom learning has yet to receive attention in either research or development. This article speculates about how AI technologies might be used to foster knowledge of idiomaticity in the future. It argues that the deployment of AI knowledge systems for idiom learning require critical examination. It is suggested that AI knowledge systems can aid the development of idiomatic competence, and should be incorporated into the design of multimedia programs for first and second/foreign language learners as early in their education as possible. Potential applications of future AI knowledge systems for idiom learning are discussed.

A Transformation-oriented Methodology to Knowledge-based Conceptual Data Warehouse Design

Abstract: Applications of artificial intelligence (AI) technology in the form of knowledge-based systems within the context of database design have been extensively researched particularly to provide support within the conceptual design phase. However, a similar approach to the task of data warehouse design has yet to be seriously initiated. In this paper, we proposed a design methodology for conceptual data warehouse design called the transformation-oriented methodology, which transforms an Entity-Relationship (ER) model into a multidimensional model based on a series of transformation and analysis rules. The transformation-oriented methodology translates the ER model into a specification language model and transformed it into an initial problem domain model. A set of synthesis and diagnosis rules will then gradually transform the problem domain model into the multidimensional model. A prototype KB tool called the DWDesigner has been developed to implement the aforementioned methodology. The multidimensional model produces by the DWDesigner as output is presented in a graphical form for better visualization. Testing has been conducted to a number of design problems, such as university, business and hospital domains and consistent results have been achieved.

Challenges for developing intelligent, interactive and cooperative PLM systems: introductory article on applications of artificial intelligence and virtual reality to product lifecycle management

Abstract: Current Product Lifecycle Management (PLM) systems are document-oriented, have difficulties with customisation and present drawbacks to the enhancement of inter-enterprise collaboration. To elaborate on a modern concept of PLM, it is necessary to adopt intelligent, cooperative and interactive technologies. To do so, we present a paper that contains useful ideas to be challenged and debated: the notions of Artificial Intelligence (AI) and soft-computing and a literature review regarding AI, soft-computing and Virtual Reality (VR) approaches to the PLM systems. To conclude, we present the guest editors' comments on the papers of this Special Issue.

Peer Review To Improve Artificial Intelligence Teaching

Abstract: Using a team-work, project-based methodology is a common approach when teaching Artificial Intelligence. However, a major drawback of such approach is that AI courses comprise a wide syllabus composed of quite independent topics. In consequence, students focus on one single topic from the entire course contents: although deep learning of such topic is probably ensured, learning of the rest of the contents is also probably much more superficial. In this paper, peer review is proposed as a complement to project-based learning. Students work not only on their project about a chosen topic, but also review peers' projects on distinct topics, providing them with a wider comprehension of the global syllabus of the course. Empirical results of the application of this approach to an actual course on Artificial Intelligence for senior students in Telecommunication Engineering are presented too. Analysis focuses on the effects of the reviewing task, as it is the one which broadens students learning. Positive results have been achieved, thus validating the interest of peer review as a useful instrument for learning improvement.

Collective Intelligence: It's All in the Numbers

Abstract: AI has been an exporter of ideas to computing in general (neural networks, agents, though robotics is more complex). But AI is now embracing ideas from elsewhere that were initially scorned because they were thought to have nothing to do with modeling intelligence and, especially, human intelligence. These are the statistical and probabilistic approaches to information capture and use that have become particularly prominent in machine learning but have spread all over AI in the last two decades. Pattern recognition was accepted in particular areas, like machine vision, as a kind of technological fix. But statistical and probabilistic approaches are now mainstream

Teaching artificial intelligence using web-based applications

Abstract: Instructors teaching courses in artificial intelligence are challenged to illustrate how various topics covered in class can be applied to everyday computing applications. As an instructor of an artificial intelligence course, one can often sense a student's disconnection between the "weak AI" algorithms covered and the mechanisms possible for merging them into typical information systems, including client management systems, resource scheduling systems, financial applications, and fraud detection systems. Lisp and Prolog have withstood as the favorite choices for implementing AI methods discussed in an artificial intelligence course, but they lack means for input conversion and validation, and output formatting. Furthermore, their GUI capabilities are very limited. This paper describes a web-based shell interface that addresses these issues and provides the student with insight into how AI algorithms can be embedded within broader applications.

Getting Meaning into the Machine

Abstract: The semantic Web is a development of great importance to AI as a whole - even though we still dispute what it means and how it can come into being

Developing a "Fundamentals of Artificial Intelligence" E-Learning Course

Abstract: Artificial Intelligence (AI) is a growing and demanding subject in most Information Technology (IT) faculties. This in turn makes it a nominated topic for an e-learning development. Such development is a challenging one that requires a more powerful approach to show an e-learner what AI concepts are? In this paper we will present our experience in developing a course entitled "Fundamentals of Artificial Intelligence". The development was under the AVECENNA VIRTUAL CAMPUS project (539INT2000), Philadelphia University, Amman, Jordan, acting on behalf of and for UNESCO.

Artificial Intelligence Techniques in Real-Time Strategy Games - Architecture and Combat Behavior

Abstract: The general purpose of this research is to investigate the possibilities offered for the use of Artificial Intelligence theory and methods in advanced game environments. The real-time strategy (RTS) game genre is investigated in detail, and an architecture and solutions to some common issues are presented. An RTS AI controlled opponent named “KAI” is implemented for the “TA Spring” game engine in order to advance the state of the art in usin AI techniques in games and to gain some insight into the strengths and weaknesses of AI Controlled Player (AI CP) architectures. A goal was to create an AI with behavior that gave the impression of intelligence to the human player, by taking on certain aspects of the style in which human players play the game. Another goal for the benefit of the TA Spring development community was to create an AI which played with sufficient skill to provide experienced players with resistance, without using obvious means of cheating such as getting free resources or military assets. Several common techniques were used, among others Rule-based decision making, path planning and path replanning, influence maps, and a variant of the A* search algorithm was used for searches of various kinds. The AI also has an approach to micromanagement of units that are fighting in combination with influence maps. The AI CP program was repeatedly tested against human players and other AI CP programs in various settings throughout development. The availability of testing by the community but the sometimes sketchy feedback lead to the production of consistent behavior for tester and developer alike in order to progress. One obstacle that was met was that the rule-based approach to combat behavior resulted in high complexity. The architecture of the RTS AI CP is designed to emerge a strategy from separate agents that were situation aware. Both the actions of the enemy and the properties of the environment are taken into account. The overall approach is to strengthen the AI CP through better economic and military decisions. Micromanagement and frequent updates for moving units is an important part of improving military decisions in this architecture. This thesis goes into the topics of RTS strategies, tactics, economic decisions and military decisions and how they may be made by AI in an informed way. Direct attempts at calculation and prediction rather than having the AI learn from experience resulted in behavior that was superior to most AI CPs and many human players without a learning period. However, having support for all of the game types for TA Spring resulted in extra development time. Keywords: computer science information technology RTS real time strategy game artificial intelligence architecture emergent strategy emergence humanlike behavior situation situational aware awareness combat behavior micro micromanagement pathfinder pathfinding path planning replanning influence maps threat DPS iterative algorithm algorithms defense placement terrain analysis attack defense military control artificial intelligence controlled player computer opponent game games gaming environmental awareness autonomous action actions agent hierarchy KAI TA Spring Total Annihilation

AI re-emerging as research in complex systems

Abstract: The history and the future of Artificial Intelligence could be summarized into three distinctive phases: embryonic, embedded and embodied. We briefly describe early efforts in AI aiming to mimic intelligent behavior, evolving later into a set of the useful, embedded and practical technologies. We project the possible future of embodied intelligent systems, able to model and understand the environment and learn from interactions, while learning and evolving in constantly changing circumstances. We conclude with the (heretical) thought that in the future, AI should re-emerge as research in complex systems. One particular embodiment of a complex system is the Intelligent Enterprise.

Maintenance of Game Character's AI by Players

Abstract: With the development of computer games, different game worlds and various game characters are found within them. Various Artificial Intelligence (AI) techniques are usually used to define behavior s of the characters within game worlds, which are controlled by AI algorithms in the computer as well as by the user. The AI techniques defined for these characters are generally developed by the game creators and cannot be changed without going to some effort, which means that if a user wished to control the behavior s of a character within a game, they could not easily do so. Being able to edit the behaviors of AI characters is beneficial as it gives the user extra control over their characters. Therefore a method for allowing a user to easily personalize the AI characters was needed. This goal was achieved by using an incremental knowledge acquisition method, called the MCRDR. The MCRDR allows the user to easily acquire new control knowledge of the AI characters by combining rule-based and case-based knowledge acquisition approach. Our experiment results showed that AI of a character could be personalized with this method of knowledge extraction.

Intelligence Development from the Logic Artificial Intelligence to Social Intelligence

Abstract: This paper summarizes the development of Artificial Intelligence(AI) from the viewpoint of Noetic sciences as well as System sciences as thus: Traditional AI with physical symbol system hypothesis(the simulation of logical thinking),Artificial Neural Networks representative of distributed AI(the simulation of thinking in imagery),Situation AI(the simulation of logical thinking and thinking in imagery in their environments),the Emergence of Social Intelligence from the research of agent technology and artificial society(the simulation of social thinking and collective wisdom in their environments).Qian Xuesen proposed a new discipline of science-the study of Open Complex Giant System and its methodology in 1990,and then pointed out what we study was not intelligent computer,we must pay attention to the human-centered man-computer cooperated intelligent system.On the basis of that,Cyberspace for workshop of Meta-synthetic engineering has been established to provide an operational intelligent platform from which social intelligence can be emerged.

Introducing the Future of AI

Abstract: To explore our field's future, we invited a number of well-known AI scientists to contribute articles speculating about where AI is headed and how we might get there. This article is part of a special issue on the Future of AI.

AI and Music: Toward a Taxonomy of Problem Classes

Abstract: The application of Artificial Intelligence technology to the field of music has always been fascinating, from the first attempts in automating human problem solving behavior till this day. Human activities related to music vary in their complexity and in their amenability of becoming automated, and for both musicians and AI researchers various questions arise intuitively, e. g.: What are music-related activities or tasks that can be automated? How are they related to each other? Which problem solving methods have proven well? In which places does AI technology contribute? Actually, the literature in the intersection of AI and music focuses on single problem classes and particular tasks only, and a comprehensive picture is not drawn. This paper, which outlines key ideas of our research in this field, provides a step toward closing this gap: it proposes a taxonomy of problem classes and tasks related to music, along with methods solving them.