Showing papers on "Knowledge representation and reasoning published in 1989"

Artificial Intelligence: Structures and Strategies for Complex Problem Solving

Abstract: From the Publisher: Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG. The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change. The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science. An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning. Includes new material on: Fundamentals of search, inference and knowledge representation AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems. Machine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of induction Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life. Object and agent-based problem solving and other forms of advanced knowledge representation

Knowledge representation in fuzzy logic

Abstract: The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control. >

Efficient management of transitive relationships in large data and knowledge bases

Abstract: We argue that accessing the transitive closure of relationships is an important component of both databases and knowledge representation systems in Artificial Intelligence. The demands for efficient access and management of large relationships motivate the need for explicitly storing the transitive closure in a compressed and local way, while allowing updates to the base relation to be propagated incrementally. We present a transitive closure compression technique, based on labeling spanning trees with numeric intervals, and provide both analytical and empirical evidence of its efficacy, including a proof of optimality.

HUGIN: a shell for building Bayesian belief universes for expert systems

Abstract: Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell - for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components-- nodes and arcs--as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an exam ple of a real world application, MUNIN an expert system for electromyography is discussed.

Expert systems for configuration at Digital: XCON and beyond

Abstract: Members of Digital Equipment Corporation's team of expert system experts reflect and recount a decade's worth of lessons learned in designing, and building a core of configuration systems

Knowledge Maps

Abstract: To get fragmented information out of people's heads, onto paper, and ultimately into a computer is a continually challenging problem. We show how to use the concepts of influence diagrams to construct knowledge maps that capture the diverse information possessed by an individual or a group. We use redundant knowledge maps assessed iteratively to handle cases where the most comfortable way to assess the information does not correspond to any proper assessment order for the diagram. We use disjoint knowledge maps when the particular assessment to be made does not require a complete joint distribution. The necessary inferential calculations are readily performed in simple cases by spreadsheet programs. Knowledge maps facilitate the processes of representing knowledge and of determining its implications.

Connectionism in Perspective

Abstract: Knowledge Representation and Memory. Perception and Action. Learning. Problem Solving and Reasoning. Trends, Research Programs. Abstracts, Work in Progress.

Expert systems for image processing: knowledge-based composition of image analysis processes

Abstract: Recently several expert systems for image processing were proposed to facilitate the development of image analysis processes. They use the knowledge about image processing techniques to compose complex image analysis processes from primitive image processing operators. In this paper, we classify them into the following four categories and discuss their objectives, knowledge representation, reasoning methods, and future problems: (1) consultation system for image processing, (2) knowledge-based program composition system, (3) rule-based design system for image segmentation algorithms, and (4) goal-directed image segmentation system. In the latter half of the paper, we emphasize the importance of image analysis strategies in realizing effective image analysis: analysis using the pyramid (multi-resolution) data structure, combination of edge-based and region-based analyses, and so on. We propose two methods of representing image analysis strategies: one from a software engineering viewpoint and the other from a knowledge representation viewpoint. Several examples are given to demonstrate the effectiveness of these methods.

Basic structure of a proposed building product model

Abstract: The basic principles enabling a building product model to be used by the application programs of the different participants in the building process have been defined in an industry-wide cooperation project in Finland. The model is conceptual, using concepts such as objects, attributes, and different types of relations between objects. The model is capable of containing all kinds of data describing a particular building. In current practice, these data are contained in drawings, specifications, bills of quantities, etc. At present the model is not fully elaborated, but it will be further developed in subdiscipline-specific projects, as well as being tested by prototypes.

Models of Expertise in Knowledge Acquisition

Abstract: Publisher Summary This chapter presents the models of expertise in knowledge acquisition The conceptual models, which are developed from interpretation models, consist most often of combinations of models for generic tasks The conceptual model consists of a diagnostic task, which has as one of its sub tasks a planning task Project management consists of alternating planning, monitoring, diagnostic, and remedying tasks The interpretation models provide support for developing the interference and task layers, but far less support exists for domain structures However, this reflects the state of the art in artificial intelligence (AI), where formalisms are developed for knowledge representation, but there is little insight in what good representations (models) are for objects and actions in a domain Current research in AI toward models for time, space, substance, and processes may in the near future become valuable for analyzing domains and constructing robust domain knowledge bases These models or domain theories can be developed into tools that provide initial structures for major domain concepts The majority of these studies indicate that KCLM as a bridge between data and the design of the KBS is useful The studies also indicate that KCLM and the interpretation models with KADS are not completely stable and fully developed

Rough classification in incomplete information systems

Abstract: The paper is concerned with the problems of rough sets theory and rough classification of objects. It is a new approach to problems from the field of decision-making, data analysis, knowledge representation, expert systems etc. Several applications (particularly in medical diagnosis and engineering control) confirm the usefulness of the rough sets idea. Rough classification concerns objects described by multiple attributes in a so-called information system. Traditionally, the information system is assumed to be complete, i.e. the descriptors are not missing and are supposed to be precise. In this paper we investigate the case of incomplete information systems, and present a generalization of the rough sets approach which deals with missing and imprecise descriptors.

Eliciting knowledge for analytical representation

Abstract: One large class of intelligent systems attempts to represent expert knowledge in terms of an analytical language. If experts can express themselves in terms of that language, then its analytical power allows sophisticated manipulation of their knowledge, perhaps leading to inferences that they could not have made reliably without the help of such a cognitive aid. In addition, these languages allow pooling the knowledge of diverse experts, each providing inputs to different components of the model in a way that is subject to independent review and revision. Examples of such languages include those of decision analysis, forecasting, cost-benefit analysis, and probabilistic risk analysis. Although they are applied to very different substantive problems (e.g. defining terms, estimating quantitative parameters), the analytical representation languages used by such systems require a common set of interdependent judgmental skills. As a result, one can draw on a common set of research results regarding the cognitive processes involved in using these systems. This perspective is illustrated in the context of a particular analytical representation language, that used in risk analysis. >

Object-oriented programming: themes and variations

Abstract: Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. The history of ideas has some additional threads including work on message passing as in ACTORS, and multiple inheritance as in FLAVORS. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.

Classification as a query processing technique in the CANDIDE semantic data model

Abstract: The use of classification and subsumption to process database queries is discussed. The data model, called CANDIDE, is essentially an extended version of the FL-1, KANDOR and BACK, frame-based knowledge representation languages. A novel feature of the approach is that the data-description language and data-manipulation language are identical, thus providing uniform treatment of data objects, query objects and view objects. The classification algorithm find the correct placement for a query object in a given object taxonomy. Tractability issues are explored, and the expressiveness of queries is compared with relational algebra. This data model has been implemented in POPLOG as the basis for a knowledge-base management system that includes an integrated natural-language query system. >

GERTIS: a Dempster-Shafer approach to diagnosing hierarchical hypotheses

Abstract: Gertis—a prototype expert system—not only demonstrates the feasibility of applying the Dempster-Shafer-based reasoning model to diagnosing hierarchically related hypotheses, but also suggests ways to generate better explanations by using knowledge about the structure of the hypothesis space and knowledge about the intended effects of the rules.

Schema evolution in object-oriented database systems

Abstract: Object-oriented database systems ususally exhibit specific advantages over traditional database management systems and programming languages. Among them stand the ease of writing, maintaining and debugging application programs, code modularity, inheritance, persistency and sharability. Of particular interest to software engineering and computer-aided design applications is also the ability to dynamically change the object definitions and the opportunity to define incrementally composite objects. This paper gives an overview of current research efforts directed towards evolving data definitions in object-oriented database systems. The emphasis is on their ability to support two complementary aspects: supporting evolving schemas, and propagating the changes on the object instances. Several projects are analyzed: Cadb, Encore, GemStone, Orion and Sherpa. Current results indicate that if most of them provide schema evolution facilities, they seldom support automatic propagation mechanisms. A proposal is described that enables Sherpa to fully support the propagation of changes and the dynamic classification of the instances whose class definitions are modified. This approach is an extension of techniques used in artificial intelligence for knowledge representation. It extends previous classification mechanisms with a dynamic capability which adequately supports evolving class definitions and instances.

Effects of scripted cooperation and knowledge maps on the processing of technical material

Abstract: This study replicates and extends prior investigations of scripted cooperation and knowledge maps by examining (a) their independent and interactive effects on procedural knowledge acquisition and (b) the transfer of these effects to individual learning. The instrumental uses and limitations of knowledge maps are discussed

Automating knowledge acquisition for aerial image interpretation

Abstract: The interpretation of aerial imagery requires substantial knowledge about the scene under consideration. Knowledge about the type of scene-airport, suburban housing development, urban city-aids in low-level and intermediate level image analysis and will drive high-level interpretation by constraining search for plausible consistent scene models. Collecting and representing large knowledge bases requires specialized tools. In this paper we describe the organization of a set of tools for interactive knowledge acquisition of scene primitives and spatial constraints for interpretation of aerial imagery. These tools include a user interface for interactive knowledge acquisition, an automated knowledge compiler that transforms schemabased assertions into productions that are directly executable by our interpretation system, and a performance analysis tool that generates a critique of the final interpretation. The generality of these tools is demonstrated by the generation of rules for a new task, suburban house scenes, and the analysis of a set of imagery by our interpretation system.

Precedence knowledge in feature mating operation assembly planning

Abstract: The authors discuss the representation and acquisition of the precedence knowledge of an assembly, which plays an important role in the generation of assembly sequences and the planning of assembly. An efficient and complete symbolic representation has been developed to express the precedence knowledge clearly and precisely. This symbolic representation makes it possible to perform reasoning and manipulation of the precedence knowledge. Furthermore, the representation is complete in the sense that it can represent the assembly precedence knowledge as well as the disassembly precedence knowledge and these two forms of knowledge can be transformed from one to another. A geometric mating graph is developed to include all the necessary geometric and topological information for the precedence knowledge acquisition. Two algorithms are developed to obtain the precedence knowledge from the geometric mating graph systematically. The disassembly precedence knowledge thus obtained is equivalent to the assembly precedence knowledge and can be used to generate all the possible sets of assembly sequences. >

Dynamic Vision Systems for Autonomous Mobile Robots

Abstract: . the architecture of multi-processor computer systems The logical structure of robot vision systems is analyzed as a basis for designing such systems. Both sensor data fusion and knowledge representation in a vision system may be broken down into four hierarchical levels where at each level knowledge represen- .

Intelligent monitoring and control

Abstract: Intelligent monitoring and control involves observing and guiding the behavior of a physical system toward some objective, with real-time constraints on the utility of particular actions. Generic functional requirements for this task include: integration of perception, reasoning, and action; integration of multiple reasoning activities; reasoning about complex, time-varying systems; coordination of multiple response modes; dynamic allocation of limited computational resources. We illustrate these requirements in the domain of patient monitoring in a surgical intensive care unit (SICU). We propose a generic architecture, designed and implemented in layers: top-level system organization; reasoning architecture; generic reasoning skills and knowledge representation; first-principles knowledge of physical systems; domain knowledge. We illustrate the architecture in the "Guardian" system for SICU monitoring and describe Guardian's performance on an illustrative scenario. Finally, we discuss the generality and limitations of the proposed architecture.

Defining interfaces at a high level of abstraction

Abstract: One of the newest user-interface management systems, the User-Interface Development Environment, is described by its creators. UIDE is built around a knowledge-based representation of the conceptual design of a user interface. UIDE supports design at a high-level specification of the interface from information provided by the designer. By using the same knowledge base, the interface developer can generate a new interface design with the same functionality as the original design. This lets users try many functionally equivalent interfaces for the same application. >

Qualitative Spatial Reasoning: A Semi-quantitative Approach Using Fuzzy Logic

Abstract: Qualitative reasoning is useful as it facilitates reasoning with incomplete and weak information and aids the subsequent application of more detailed quantitative theories. Adoption of qualitative techniques for spatial reasoning can be very useful in situations where it is difficult to obtain precise informationand where there are real constraints of memory, time and hostile threats. This paper formulates a computational model for obtaining all induced spatial constraints on a set of landmarks, given a set of approximate quantitative and qualitative constraints on them, which may be incomplete, and perhaps even conflicting.