Showing papers on "Applications of artificial intelligence published in 1995"
27 Nov 1995
TL;DR: It is concluded that Go is a domain in which the development of new programming techniques is not only possible but is in fact necessary.
Abstract: Go provides artificial intelligence (AI) and cognitive science researchers with an easily specified formal domain in which skills of human intelligence cannot be matched by currently known programming techniques. Go is a much more widely played game than chess (principally in Japan, Korea and China), yet it is not well known to AI and cognitive science researchers and our goal in this paper is to introduce some of the challenges of the game to the AI community in the form of a comparison with chess. Go has been called a possible "task par excellence for AI" by Berliner (1978) and we conclude that Go is a domain in which the development of new programming techniques is not only possible but is in fact necessary.
28 citations
TL;DR: It is argued that these issues can be resolved by creating an environment which supports all the interfaces between the artificial intelligence (AI) applications and the EMS, and this environment should also be responsible for maintaining a model of the power system common to all the AI applications.
Abstract: This paper discusses the issues which must be addressed when integrating artificial intelligence (AI) and, in particular, expert system applications in an energy management system (EMS) environment. It is argued that these issues can be resolved by creating an environment which supports all the interfaces between the artificial intelligence (AI) applications and the EMS. This environment should also be responsible for maintaining a model of the power system common to all the AI applications. Once this environment has been created, AI applications can be easily "plugged" into the EMS. The design of such an environment, the operator decision environment, is described and studies of its implementation are provided to illustrate its flexibility.
21 citations
TL;DR: It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories.
18 citations
01 May 1995
TL;DR: In this paper, a case-based reason-based approach is used for adaptive architectures in computer aided manufacturing (CAM) and Fuzzy logic and control (FLC) systems.
Abstract: : CONTENTS: Adaptive architectures; Case-based reasonong; Computer aided manufacturing; Diagnosis; Fuzzy logic and control; Image; Knowledge acquisition; Knowledge based systems; Model based- reasoning; Modelling; Software engineering; Invited presentation. (KAR) p. 3
14 citations
30 Jun 1995
TL;DR: An approach to model hybrid (ruleand frame-based) expert systems using Coloured Petri Nets and the concept of controlled state tokens is described and has an implication for supporting the verification process in hybrid systems.
Abstract: High level Petri Nets have recently been used for many AI applications, particularly for modelling traditional rule-based expert systems. The major effect is to facilitate the analysis of the knowledge inference during the reasoning process, and to support the system verification which increasingly becomes an integral part of expert system development. Nevertheless, there is not much attention being put on systems other than the traditional ones. In this paper, we described an approach to model hybrid (ruleand frame-based) expert systems using Coloured Petri Nets and the concept of controlled state tokens. The analysis of the proposed model is by constructing and examining the reachability tree spanned by the knowledge inference. Such methodology has an implication for supporting the verification process in hybrid systems.
12 citations
TL;DR: The works reviewed here constitute the early steps of a nascent program of study, which suggest many potentially fruitful research avenues in widely varied application areas, such as computational text analysis, logic programming, computer learning, and conflict simulation.
Abstract: Scholars who apply artificial mtelligence to political questions seek, most generally, to expand the scope and relevance of political model analysis. By incorporating the effects of variable human notions, traditions, and meanings, they seek to humanize political models. Most early applications of artificial intelligence in political science research address substantive issues pertaining to political decision making. Most of these works apply production-system technology to construct choice models in for eign-policy decision contexts. In recent years, political applications have begun to diver sify. Today, lively research efforts flourish in widely varied application areas, such as computational text analysis, logic programming, computer learning, and conflict sim ulation. The works reviewed here constitute the early steps of a nascent program of study. Much remains to be accomplished. Nevertheless, the efforts conducted thus far suggest many potentially fruitful research avenues. Keywords: artificial int...
10 citations
Journal Article•
TL;DR: The purpose is to bring together a description of the concepts and to relate them in a common framework for open planning and scheduling systems at the Artificial Intelligence Applications Institute (AIAI) in Edinburgh.
Abstract: TAe development of open planning and scheduling systems seeks to support incremental extension and change, and facilitate communication between processing agents (both computer and human) This paper presents the open planning and scheduling approach adopted in the O-Plan and TOSCA systems at the Artificial Intelligence Applications Institute (AIAI) in Edinburgh The purpose is to bring together a description of the concepts and to relate them in a common framework References to more detailed descriptions are provided
10 citations
22 Oct 1995
TL;DR: In this paper, the authors described an approach to model hybrid (rule and frame-based) expert systems using coloured Petri nets and the concept of controlled state tokens, and the analysis of the proposed model is by constructing and examining the readability tree spanned using the knowledge inference.
Abstract: High level Petri nets have recently been used for many AI applications, particularly for modelling traditional rule-based expert systems. The major effect is to facilitate the analysis of the knowledge inference during the reasoning process, and to support the system verification which increasingly becomes an integral part of expert system development. Nevertheless, there is not much attention being put on systems other than the traditional ones. In this paper, we described an approach to model hybrid (rule- and frame-based) expert systems using coloured Petri nets and the concept of controlled state tokens. The analysis of the proposed model is by constructing and examining the readability tree spanned using the knowledge inference. Such methodology has an implication for supporting the verification process in hybrid systems.
10 citations
TL;DR: Computer journals and magazines are currently full of stories about object technology, just as they were filled with stories about artificial intelligence (AI) and expert systems in the early 1980s.
Abstract: Computer journals and magazines are currently filled with stories about object technology, just as they were filled with stories about artificial intelligence (AI) and expert systems in the early 1980s. The naive reader might think that expert systems technology had failed and that object-oriented technology was the hot new software approach of the 1990s.
8 citations
Book•
01 Jan 1995
TL;DR: Knowledge representation neural networks genetic algorithms, validation/verification concurrent engineering intelligent vehicles robotics and control planning and scheduling modelling uncertainties electronic industry aeronautics and space chemical engineering environmental engineering civil engineering (TBA).
Abstract: Knowledge representation neural networks genetic algorithms, validation/verification concurrent engineering intelligent vehicles robotics and control planning and scheduling modelling uncertainties electronic industry aeronautics and space chemical engineering environmental engineering civil engineering (TBA).
7 citations
TL;DR: AI as a field has undergone rapid growth in diversification and practicality, and has shown a growing influence on other computer science areas, such as databases, software engineering, distributed computing, computer graphics, user interfaces, and stimulation.
Abstract: AI as a field has undergone rapid growth in diversification and practicality. For the past 10 years, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have recently been added to the traditional domains of practical AI. Although much practical AI is still best characterized as advanced computing rather than intelligence, applications in everyday commercial and industrial settings have certainly increased, especially since 1990. Additionally, AI has shown a growing influence on other computer science areas, such as databases, software engineering, distributed computing, computer graphics, user interfaces, and stimulation.
01 Jan 1995
TL;DR: This note focuses on how to present a coherent view of the core subject matter of AI, which is to study AI's intellectual content, perhaps presenting related topics in psychology and philosophy.
Abstract: Several challenges confront the organizer of an introductory course in artificial intelligence (AI). First, one has to decide what subject matter to include. The union of everything in all of the popular AI textbooks is much too large, and the intersection undoubtedly won't include enough of what the organizer thinks important. The second challenge is how to blend the selected topics into a coherent whole. The third involves matters such as problem sets, programming exercises, laboratory work, case studies, and collateral readings. Finally, one must decide on the main purpose of the course: is it to teach AI techniques and skills, or is it to study AI's intellectual content, perhaps presenting related topics in psychology and philosophy? In this note we concentrate on the first and second of these topics---how to present a coherent view of the core subject matter of AI.
TL;DR: The results in Parthasarathy's papers are compared with an axiomatic approach given previously by the authors, which overcomes these deficiencies.
TL;DR: This article provides an initial view of the history of the CAIA and of AI applications in general since 1984 and gathers information regarding both the research papers and committee members, the two primary sources of data in the proceedings.
Abstract: An understanding of the history of AI applications through its primary conferences is important for several reasons. First, it provides insight into some of the pioneers to the field and their contributions. Second, it sheds light on the nature of the contributions of academic and nonacademic organizations to the field of AI applications. Third, the national origins of the organizations supporting AI applications reflect the international structure of AI applications. Accordingly, this review focuses on the authors of papers specializing in AI applications and their organizations and countries of origin, and on the program committee members, and their organizations and countries of origin. This review takes a quantitative approach to analyzing the history of AI applications. The IEEE Conference on Artificial Intelligence Applications (CAIA) is one of the first and longest running applications conferences to specialize on AI application issues. This article provides an initial view of the history of the CAIA and of AI applications in general since 1984. It gathers information regarding both the research papers and committee members, the two primary sources of data in the proceedings. This article considers quantity issues, making inferences based on various statistics. However, it ignores what might be called quality issues, since each paper goes through the same referee process required for the conference. >
01 Dec 1995
TL;DR: It is argued that a representation rich in primitives but fairly restricted in logical form will suffice for many of these purposes and one such representation language is presented that is expressive enough to capture exactly or closely approximate many of the representations used in the AI literature.
Abstract: AI applications require the representation and manipulation of partial spatial knowledge of many different kinds. This paper argues that a representation rich in primitives but fairly restricted in logical form will suffice for many of these purposes. We present and discuss one such representation language. We demonstrate that the language is expressive enough to capture exactly or closely approximate many of the representations that have been used in the AI literature. It also contains some original constructs for dealing with collections of regions of unknown cardinality.
02 Jan 1995
TL;DR: Space as mentioned in this paper is an associative processor architecture designed to allow experimentation with data parallel AI applications, such as semantic network processing and knowledge-based system for speech understanding, which requires extensive searching and processing within large data structures.
Abstract: Many AI tasks require extensive searching and processing within large data structures. Two example applications are semantic network processing [Higuchi et al, 1991], and maintaining hypothesis blackboards in a multi-agent knowledge-based system for speech understanding [Asanovic and Chapman, 1988]. Associative processors promise significant improvements in cost/performance for these data parallel AI applications [Foster, 1976, Lea, 1977, Kohonen, 1980]. SPACE is an associative processor architecture designed to allow experimentation with such applications.
TL;DR: Using IEEE Expert articles from the past nine years as its source, this article initiates a history of AI applications by codifying research subject areas and researchers, along with features of their sponsoring organizations.
Abstract: Using IEEE Expert articles from the past nine years as its source, this article initiates a history of AI applications by codifying research subject areas and researchers, along with features of their sponsoring organizations. >
01 Jan 1995
TL;DR: The increasing complexity of medicine has emphasized the need to accurately detect, even to anticipate changes in the patient’s condition, and to closely follow the effects of therapy, paving the way for applications of artificial intelligence in medicine in general, and critical care in particular.
Abstract: The increasing complexity of medicine has emphasized the need to accurately detect, even to anticipate changes in the patient’s condition, and to closely follow the effects of therapy. At the same time, the advancements of technology are enabling us to measure and monitor many physiological variables simultaneously in real time, and in some cases to administer therapy automatically. The need to quickly process and interpret an overwhelming amount of information is paving the way for applications of artificial intelligence in medicine in general, and critical care in particular.
01 Jun 1995
TL;DR: Artificial intelligence, including expert systems, fuzzy logic, neural networks and genetic algorithms, is increasingly being applied to the solution of a wide range of problems in the monitoring and operation of electricity supply systems as mentioned in this paper.
Abstract: Artificial intelligence, including expert systems, fuzzy logic, neural networks and genetic algorithms, is increasingly being applied to the solution of a wide range of problems in the monitoring and operation of electricity supply systems. Following the privatization of the Electricity Supply Industry in England and Wales in 1990, there is an overriding commercial incentive for the privatized electricity companies to operate the high voltage transmission networks as economically as possible without compromizing their reliability in a climate of substantial uncertainty as to the generator prices and availabilities that are bid into the pool from day to day and the energy trading contracts that have to be implemented. These circumstances often mean that the transmission and distribution networks must operate close to their defined security limits and still be capable of surviving severe disturbances. Hence artificial intelligence is being applied to the development of online real‐time monitoring systems to assist the electricity supply companies' control room engineers. This paper reviews this field and presents two case studies.
30 Jun 1995
TL;DR: This paper presents the most important temporal reasoning requirements for real-time applications in power systems and presents the methods used in some systems that have been developed to deal with temporal reasoning, with particular emphasis on SPARSE, an expert system for fault analysis and service restoration, developed for portuguese Control Centers.
TL;DR: In this paper, the authors presented the coupling of artificial intelligence and object-oriented methodology applied for the construction of the model-based decision support system MBDSS, which is designed for support the strategic decision making lead to the achievement of optimal path towards market economy from the central planning situation in China.
20 Feb 1995
TL;DR: This paper analyzes studies of the impacts of accounting related expert systems and the methodologies employed on several dimensions: time focus, informants, goal, frameworks, and impacts studied.
Abstract: Organizational impacts studies of artificial intelligence applications are becoming more common. A plethora of methods have been used to investigate such impacts. This paper analyzes studies of the impacts of accounting related expert systems and the methodologies employed on several dimensions: time focus, informants, goal, frameworks, and impacts studied. >
TL;DR: An overview of Artificial Intelligence (AI) and its applications to Intelligent Transportation Systems (ITS) is given.
01 Jan 1995
TL;DR: A new massively parallel computation model that supports pattern and symbol processing as well as numerical computation is established that is targeting other massively parallel AI applications, such as learning and self-organization.
Abstract: Our research objective is to establish a new massively parallel computation model that supports pattern and symbol processing as well as numerical computation. Programming languages and a runtime system under the multiuser environment are being developed to demonstrate the model. Those will also be distributed to researchers. We are currently designing and developing programming languages, an operating system, and a programming environment on the RWC-1 machine [1]. The following paragraphs summarize our target applications:
1.
Real-World Applications and AI Applications Speech recognition, image recognition, robot controls, and other real-world applications will be developed by our colleagues in the RWC project. Those applications will be made possible through flexible processing[2] on massively parallel machines. We are also targeting other massively parallel AI applications, such as learning and self-organization.
2.
Large-Scale Simulation Scientific calculation applications such as the simulation of fluid dynamics and molecular dynamics are, of course, major target applications. Microscopic simulations for the sociology such as auto traffic control are also being considered.
22 Oct 1995
TL;DR: This paper proposes an implementation of the Davis-Putnam procedure based on the RETE-like network that can gain much efficiency in both CPU time and memory usage.
Abstract: The satisfiability (SAT) problem is an important topic in many AI applications, such as theorem proving, decision making, etc. One of the widely adopted approach for SAT problems is the Davis-Putnam procedure (1960). In this paper, we propose an implementation of the Davis-Putnam procedure based on the RETE-like network. By this way, we can gain much efficiency in both CPU time and memory usage.