Showing papers on "Applications of artificial intelligence published in 1991"

Time and Time Again: The Many Ways to Represent Time

Abstract: One of the most crucial problems in any computer system that involves representing the world is the representation of time. This includes applications such as databases, simulation, expert systems, and applications of Artificial Intelligence in general. In this brief article, I will give a survey of the basic techniques available for representing time, and then talk about temporal reasoning in a general setting as needed in AI applications. Quite different representations of time are usable depending on the assumptions that can be made about the temporal information to be represented. the most crucial issue is the degree of certainty one can assume. Can one assume that a timestamp can be assigned to each event, or barring that, that the events are fully ordered? Or can we only assume that a partial ordering of events is known? Can events be simultaneous? Can they overlap in time and yet not be simultaneous? If they are not instaneous, do we know the durations of events? Different answers to each of these questions allow very different representations of time.

An Introduction to Neural Networks and a Comparison with Artificial Intelligence and Expert Systems

Abstract: Artificial intelligence (including expert systems) (AI/ES) and neural networks (NN) provide methods for formalizing qualitative aspects of business systems. They complement quantitative methods in solving business problems. While AI and NN have the common goal of simulating human intelligence, they use different methods. AI/ES assumes the brain is a black box and imitates the human reasoning process. It processes knowledge sequentially, represents it explicitly, and mostly uses deductive reasoning. Learning takes place outside the system. NN treats the brain as a white box and imitates its structure and function, using a parallel approach to simulate human intelligence. It represents knowledge implicitly within its structure and applies inductive reasoning to process knowledge. Learning takes place within the system. Both AI/ES and NN have great potential to solve qualitative problems, and their integration could provide a powerful tool for dealing with problems outside the domain of current problem-solvi...

Where's the AI?

Abstract: I survey four viewpoints about what AI is. I describe a program exhibiting AI as one that can change as a result of interactions with the user. Such a program would have to process hundreds or thousands of examples as opposed to a handful. Because AI is a machine's attempt to explain the behavior of the (human) system it is trying to model, the ability of a program design to scale up is critical. Researchers need to face the complexities of scaling up to programs that actually serve a purpose. The move from toy domains into concrete ones has three big consequences for the development of AI. First, it will force software designers to face the idiosyncrasies of its users. Second, it will act as an important reality check between the language of the machine, the software, and the user. Third, the scaled-up programs will become templates for future work.

Artificial intelligence in chemical engineering

Abstract: From the Publisher: This book seeks to provide broad and in-depth coverage of AI programming, AI principles, expert systems and neural networks in chemical engineering. It introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer should have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering.

The role of artificial intelligence in information retrieval

Abstract: This opinion article presents a view of the scope for artificial intelligence in information retrieval. It considers four potential roles for AI in IR, evaluating AI from a realistic point of view and within a wide information management context. The conclusion is that AI has limited potential, not just because AI is itself insufficiently developed, but because many information management tasks are properly shallow information processing ones. There is nevertheless an important place for specific applications of AI or AI‐derived technology when particular constraints can be placed on the information management tasks involved. © 1991 John Wiley & Sons, Inc.

A tool for tree pattern matching

Abstract: A description is presented of a system, called approximate-tree-by-example (ATBE), which supports AI applications that involve comparing ordered labeled trees or retrieving/extracting information from repositories of such trees. The ATBE system interacts with users through a powerful query language; graphical devices are provided to facilitate inputting the queries. The system is designed to be extensible, customizable, and portable, which makes it a very useful tool for tree pattern matching in various environments. The use of the tool is illustrated. Several examples taken directly from the complete implementation are discussed. >

Intelligent Library Systems: Artificial Intelligence Technology and Library Automation Systems

Abstract: Artificial Intelligence (AI) encompasses the following general areas of research: (1) automatic programming, (2) computer vision, (3) expert systems, (4) intelligent computer-assisted instruction, (5) natural language processing, (6) planning and decision support, (7) robotics, and (8) speech recognition. Intelligent library systems utilize artificial intelligence technologies to provide knowledge-based services to library patrons and staff. This paper examines certain key aspects of AI that determine its potential utility as a tool for building library systems. It discusses the barriers that inhibit the development of intelligent library systems, and it suggests possible strategies for making progress in this important area. While all of the areas of AI research indicated previously may have some eventual application in the development of library systems, this paper primarily focuses on a few that the author judges to be of most immediate significance--expert systems, intelligent computer-assisted instruction, and natural language applications. This paper does not discuss the use of AI knowledge-bases in libraries as subject-oriented library materials.

AI on Wall Street

Abstract: The question of why some Wall Street AI applications are successful while others fail is explored. The Wall Street problem domain is modeled from a knowledge-based perspective, and the environment is represented from both an object-oriented view and a process view. Both models demonstrate the complexity inherent in formally defining the Wall Street environment. Analytic and cognitive models of the Wall Street environment are discussed, focusing on the shortcomings of each approach. The use of a new type of intelligent tool, called a collaborative system, that combines analytic models, human judgment, and cognitive models is proposed. >

A voice-activated robot with artificial intelligence

Abstract: A voice-activated robot arm with intelligence is presented. The proposed system consists of an IBM PC/AT microcomputer, MICROEAR voice activated hardware, and a Scorbot ER-III robot with a controller. The proposed robot is capable of understanding the meaning of natural language commands. After interpreting the voice commands a series of control data for performing a task are generated. Finally, the robot actually performs the task. Artificial intelligence techniques were used to make the robot understand voice commands and act in the desired mode. >

An APL rule-based system architecture for image interpretation strategies

Abstract: APL is both a language of choice for image processing and image description tasks, and a language for artificial intelligence applications, typically expert systems. The paper presents the architecture of an APL rule-based system for realizing description synthesis strategies i.e., sequences of actions which allow the evaluation and interpretation of the properties of structures detected in an image. Descriptions of images are stored in a data structure which is formally presented in APL2 syntax. Describing an image often requires that reflective actions be taken i.e., actions in which the system examines the state of the computation and its internal state to select the next action to be executed. It is shown how APL provides all the features needed to implement a reflective mechanism through the use of metarules of the same format as rules.

The many uses of “Belief” in AI

Abstract: Within AI and the cognitively related disciplines, there exist a multiplicity of uses of ‘belief’. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called ‘belief’. In this paper I distinguish six distinct ways in which ‘belief’ is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special AI applications. In other cases, however, genuine differences exist about the nature of what we pre-theoretically call belief. To an extent the multiplicity of opinions about, and uses of ‘belief’, echoes the discrepant motivations of AI researchers. The relevance of this discussion for cognitive scientists and philosophers arises from the fact that (a) many regard theoretical research within AI as a branch of cognitive science, and (b) even if theoretical AI is not cognitive science, trends within AI influence theories developed within cognitive science. It should be beneficial, therefore, to unravel the distinct uses and motivations surrounding ‘belief’, in order to discover which usages merely reflect differing pragmatic concerns, and which usages genuinely reflect divergent views about reality.

KOS: a knowledge-based operating system for real-time onboard applications of artificial intelligence

Abstract: The paper lays out the fundamental requirements for complex real-time applications. Then, it gives an overview of the KOS (Knowledge-based Operating System) real-time executive designed to meet these requirements. KOS is the first software combining modern real-time programming and artificial intelligence. Finally, the paper proposes a definition of real-time before concluding by describing the 'Copilote Electronique' project under development in cooperation with Dassault Aviation. >

A methodology for systematic verification of OPS5-based AI applications

Abstract: One of the critical problems in putting AI applications into use in the real world is the lack of sufficient formal theories and practical took that aid the process of reliability assessment. Adhoc testing, which is widely used as a means of verification, serves limited purpose. A need for systematic verification by compile-time analysis exists. In this article, we focus our attention on OPS5-based AI applications and present a methodology for verification which is based on compile-time analysis. The methodology is based on the principle of converting the antecedent and action-parts of productions into a linear system of inequalities and equalities and testing them for a feasible solution. The implemented system, called SVEPOA, supports interactive and incremental analysis.

An APL rule-based system architecture for image interpretation strategies

Abstract: APL is both a language of choice for image processing and image description tasks, and a language for artificial intelligence applications, typically expert systems. The paper presents the architec...

Artificial intelligence techniques for urban traffic control

Abstract: This paper deals with the applications of artificial intelligence techniques to urban traffic control problems, with the aim of improving the performance of current signal plan selection systems. In particular, an architecture of an intelligent traffic control system is outlined with regards to the different levels of data collection, data analysis and interpretation, decision, and control. The functionalities of hybrid modules introduced are discussed and the artificial intelligence methods used are mentioned. Finally the ongoing research in the field is presented.

Learning for skill refinement

Abstract: It is claimed that 'robotics' is not a test bed for AI but should involve a research frontier relating to the physics underlying human activities such as perception, remembering, planning, practice, and skill. In addition to traditional AI and neural network approaches, other domains that can account for any aspect of human intellectual behavior must be exploited, and tools that actualize real implementation of intelligence in machines need to be devised. A practice-based learning domain for skill refinement and a design tool for a signal-based structured information base for skill acquisition are presented. >

AI trends in virus control

Abstract: It is suggested that modern forms of programming techniques through the use of artificial intelligence (AI) can be used in the fight against computer viruses. Through the use of AI programming techniques, systems will be capable of dynamically representing human expertise through investigating, analyzing, and simulating human problem-solving behavior. >

Developing Artificial Intelligence Applications: A Small Business Development Center Case Study

Abstract: DEVELOPING ARTIFICIAL INTELLIGENCE APPLICATIONS: A SMALL BUSINESS DEVELOPMENT CENTER CASE STUDY During the last decade, artificial intelligence (AI) programs have emerged as a promising new technology for structuring, guiding, and improving information processing for decision making. AI programs give consultative advice to physicians about infectious diseases; help physicists examine unknown molecules and predict their molecular structures with spectroscopic analysis; assist mathematicians in solving complex problems (Harmon and King 1985); process credit requests for American Express; hunt submarines for the U.S. Navy (Kupfer 1987); help create advertisements for retailers (McCann, Tadlaqui, and Gallagher 1990); and evaluate a client's potential for repaying a loan (Waterman 1985). AI computer software, in some sense, can think. In other words, the programs can "solve problems in a way that would be considered intelligent if done by a human" (Waterman 1985, 267). AI programs will build human knowledge and processing into an interactive system, draw from that knowledge, and then present selected information that helps solve problems (Nilsson 1980, Van Horn 1986). The subdivision of AI with the most promise for small businesses is "expert systems." Expert systems are programs that contain the information processing ability of experts in a given area. With the proliferation of personal computers and programs for designing AI applications, expert systems are within the reach of small businesses and Small Business Development Centers (SBDCs). While some AI applications are costly and time consuming, potential cost-effective applications for small businesses exist. SBDCs and small businesses need to know the capabilities of expert systems and become familiar with case studies of applications. This article documents an AI application for an information transfer task at an SBDC. THE STRUCTURE OF EXPERT SYSTEMS The major components of expert systems are the knowledge base and the inference engine. The knowledge base is the set of information collected from expert(s) for the structuring, focusing, and processing of information to answer questions. The definition of objects and variables in a chosen area or domain creates the elements of the knowledge base. The relationships between variables and objects are defined and coded as rules. The most common example of a knowledge-base rule is the "if-then" statement: If a given event occurs, then a given step or question follows. To make the knowledge base compatible with the AI software, each process is reduced to a detailed logical sequence with rules and branching. Since even simple processes have several decision paths, many alternative sets of logic are necessary. Knowledge bases are usually large and complex. The inference engine controls the internal logic relating the facts and rules to the user's information. The direction of analysis can be forward or backward, working from either the beginning logic to a conclusion or from a conclusion to the beginning logic. The inference engine will prompt the computer to ask the user questions; and the answers determine branching to other questions and the outcome. The inference engine controls the AI's speed and style, while the knowledge base controls the AI's content. The result will be expert opinions and information about a given opportunity. DEVELOPMENT OF AN AI APPLICATION Two major elements in the development of AI systems are the selection of the program shell to be used and the process of developing information for the shell. Several companies offer computer programming shells--general frameworks with defined logic paths and rules--for the development of AI applications. The developer uses the logical templates to create the AI application. The shells contain the raw material and a general structure, but the developer needs to supply the specific blueprint, frame the application, and adjust the fit. …

A Dataflow Architecture for AI

Abstract: This paper describes a dataflow architecture for artificial intelligence applications in particular parallel logic programming. This machine has a decentralized approach for parallel processing. Dynamic creation of dataflow nodes not only provides higher performance but also reduces bookkeeping overhead.

A Frame-like Knowledge Representation Integrating Abstract Data Types and Logic.

Abstract: This paper describes a very general, formal knowledge representation based on algebraic ideas from abstract data types, i.e., on ideas originating outside the AI literature in research on foundations for programming and specification languages. Nevertheless, the representation incorporates practically necessary features found in inheritance systems such as AI frame systems used for natural language understanding, while offering a precise algebraic semantics. We term the approach conceptually oriented description. The contribution of this chapter is (1) to reformulate and simplify these ideas for AI applications, incorporating the useful features found in many practical AI inheritance systems, while retaining the theoretical foundation, and (2) to show how the approach is valuable in natural language semantics applications. This chapter will use some difficult examples motivated by natural language applications, but the formalism is very general and could be used for other applications, such as software requirements specification. The approach is based mainly on ideas from the language LOTOS, intended for the formal specification of concurrent systems, and also on similar work by Goguen and Meseguer [1987] on algebraically based functional specification. LOTOS adds to basic ADT concepts additional concepts for defining the notions like state, event, and temporal relationships including causality and synchronization. The main components of a software system for creating and debugging conceptual definitions using the formalism have been implemented and are briefly mentioned.

On issues in software engineering and artificial intelligence

Abstract: Software engineering (SE) needs to make substantial breakthroughs in many different areas to allow order of magnitude improvements in software development times, software quality, and system cost. Artificial intelligence (AI) is uniquely positioned to help the SE research community in many of these areas, and we examine issues in AI for SE research. Given the fuzzy definition of AI, we provide a list of AI techniques to identify how much AI there is in specific AI for SE research. We recommend using the divide and conquer approach for SE automation and provide criteria for dividing the SE problems. We provide a vision of the future CASE environment, a knowledge and database management system at the center in a client-server architecture, and argue that it constitutes an ideal test-bed for research in AI for SE. We recommend an AI for SE research approach that includes dividing the problem up, using protocol analysis, implementing on a realistic CASE environment, and evaluating in industrial settings. We give criteria to evaluate applications of AI to SE including generality, scalability, and combinability. We conclude that AI will help SE to make slow and steady progress, but that it constitutes no silver bullet.

An overview of the A architecture for optimisation problems in a logic programming environment

Abstract: Search of a state space is a common function in artificial intelligence applications consuming a large proportion of the total processing time. In this paper we propose a hierarchical architecture using content addressable memory to accelerate the A* algorithm in a Prolog system and remove the associated high memory overheads.

From natural language processing to logic for expert systems: a logic based approach to artificial intelligence

Abstract: Covers some of the most significant applications of artificial intelligence, namely: natural language processing, speech understanding, expert system design, requirement engineering, machine learning, truth maintenance systems, advanced concepts and methods of logic programming. Together with the previous two volumes edited by Thayse, this completes a comprehensive exposition of the subject of logics applied to AI.

Expert systems and antibiotic sensitivity test

Abstract: Artificial intelligence is a part of computer science that deals with programs mimicking intelligence of man. Artificial intelligence is now used to check the quality of the determination of antibiotics susceptibility of bacteria. This application is useful because antibiotic susceptibility is subject to biological and technical variation that have to be detected. Three types of reasoning are used either by the biologist or by expert systems: low level quality checking dealing with individual results, microbiological interpretation of the whole set of results and medical interpretation of the results. The use of artificial intelligence in these fields is sustained by the structured nature of the knowledge. Two type of expert systems are already of routine use, either based on production rules (ATB plus EXPERT, bioMerieux, La Balme-les-Grottes, France and SIR, 12A, Montpellier, France), or on object-oriented representation of the knowledge (EXPRIM from our laboratory). The main problem is, as usually in artificial intelligence applications, to transfer human expertise into an adapted knowledge base. The advantage of experts systems over man are their reproducibility of answer and their availability.

AI science vs. applications: accomplishments, failures and long-term goals

Abstract: The author expresses the belief that the central problems in advancing artificial intelligence (AI) systems continue to be problems of representing knowledge, especially world and common-sense knowledge, extracting knowledge from sensory data, problems of knowledge acquisition and learning, understanding plausible reasoning, such as approximate deduction, induction and analogy, and related problems. The author suggests that AI is a very successful discipline despite the failure some not-carefully-expressed predictions. It needs steady, long-term-oriented support, although perhaps not necessarily at a very high level. The expectations need to be toned down, and intensive and imaginative research should continue unabated. Potential benefits to society are very high. >

Representation and use of metaknowledge

Abstract: The need for expressing and using metalevel knowledge is emerging in the design of several kinds of AI systems. The careful distinction between object-level and metalevel notions and the formalization of the latter has first been carried out by logicians for foundational reasons; subsequently, the distinction has been exploited in Artificial Intelligence and Computation Theory, revealing itself to be of great relevance to Automated Deduction and Problem Solving. This paper concentrates on the use of metaknowledge in building knowledge-based systems. In order to introduce the issue, some motivating examples are presented. We then review various paradigms for combining knowledge and metaknowledge, with the aim of abstracting general criteria that should underly the construction of viable AI systems, as far as metaknowledge is concerned. Furthermore, a general overview of the uses of metaknowledge in AI is provided and, among them, we concentrate on inference control, which can be conveniently exercised by formalizing control strategies at the metalevel and by letting the inference engine depend on metalevel descriptions. The technique is presented with the aid of some examples, chosen from practical AI applications, that are expressed in the formalism of Horn clause logic. The issue of self-descriptive systems is then addressed. A system that embodies and can use an adequate description of itself allows for self-evaluation (e.g., the estimate of the resources needed to perform a given task) and for self-modification (e.g., the automatic improvement of deduction performance by profiting from experience gained in previous deductions).

Decision support system for position optimization on currency option dealing

Abstract: Aims to develop an intelligent decision support system for a front option dealing. The system is designed by the use of a CLP (constraint logic programming) framework which easily integrates financial domains into artificial intelligence applications. The authors developed CLP language Triton for the decision support. The system facilitates the support of a dealer with an optimum answer from the candidates of option combinations. In order to realize this intelligent support, they propose the mixed type of optimisation which combines heuristic knowledge into linear programming. The system called Nereid adopts this optimization technique and is running by the of a SUN workstation. Based upon case studies, they show several examples and illustrate the dealer's evaluation of the system. >

What can AI offer the accounting profession

Abstract: Examines the accounting profession wants or expects of artificial intelligence. The author considers the accounting profession, what AI means to accountants, their sceptical reaction to what AI can offer the accounting profession and expert system applications.