Content and representation effects with reasoning tasks in PROLOG form

Abstract

Two experiments were carried out to examine human reasoning performance in the context of the logic programming language PROLOG. Two factors, ‘content’ (familiar versus unfamiliar) and ‘representation’ (diagrammatic versus PROLOG-like list) were investigated. Subjects answered questions about hierarchical relationships in each condition. A significant interaction was obtained in both experiments, subjects making fewer errors in the familiar-diagram and unfamiliar-list conditions than in the familiar-list and unfamiliar-diagram conditions.

It is hypothesized that a lower percentage of correct responses was given in familiar-list and unfamiliar–diagram conditions because the representation of information prevented successful use of an appropriate reasoning strategy. Working memory limitations provide a basis for understanding constraints on reasoning strategies for solving task questions. These strategies may involve either a serial or a spatial solution process. One strategy may require a larger working memory load than another, depending on the representation and content of the task information. Implications for PROLOG programming instruction are discussed.     

Temporal data representation and reasoning in REAKT

The ability of reasoning about temporal data, representing past, current and expected application states is an important function to be accomplished by Real-Time Knowledge-Based Systems (RTKBS). The application of Knowledge-based systems to real-time problems has to deal with dynamic time-constrained environments and assess two of the most important requirements in real-time systems: the ability to react rapidly to changes in the environment and the guarantee of a bound on the response time. This paper presents a temporal framework for reasoning about the future behaviour of a dynamic time-constrained problem. The proposed mechanism is integrated into a multiagent blackboard architecture and will provide a perspective of the temporal functionalities offered in the REAKT tool     

Knowledge representation and reasoning in a software synthesisarchitecture

The knowledge representation and reasoning strategies in an automatic program synthesis architecture called ELF are described. ELF synthesizes computer-aided design (CAD) tools that automatically route wires in VLSI circuits. The design space ELF confronts, requires it to understand various physical technologies, to select an appropriate procedure-level decomposition, to choose algorithms and data structures, to manage any interdependencies, and to generate efficient code. ELF manages the design space using a variety of knowledge sources, including domain-specific knowledge. The manner in which knowledge is used determines the representation method of choice. The effectiveness of these ideas is illustrated via a tour through the synthesis steps for a specific routing tool, and a brief discussion of the performance of the resulting synthetic router as measured against an industrial tool     

Scenario-based knowledge representation in case-based reasoning systems

A scenario-based representation model for cases in the domain of managerial decision-making is proposed. The scenarios in narrative texts are converted to scenario units of knowledge organization. The elements and structure of the scenario unit are defined. The scenario units can be linked together or coupled with others. Compared with traditional case representation methods based on database tables or frames, the proposed model is able to represent knowledge in the domain of managerial decision-making at a much deeper level and provide much more support for case-based systems employed in business decision-making.     

Strategic business modeling: representation and reasoning

Business intelligence (BI) offers tremendous potential for business organizations to gain insights into their day-to-day operations, as well as longer term opportunities and threats. However, most of today’s BI tools are based on models that are too much data-oriented from the point of view of business decision makers. We propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses. The business intelligence model (BIM) offers concepts familiar to business decision making—such as goals, strategies, processes, situations, influences, and indicators. Unlike many enterprise models which are meant to be used to derive, manage, or align with IT system implementations, BIM aims to help business users organize and make sense of the vast amounts of data about the enterprise and its external environment. In this paper, we present core BIM concepts, focusing especially on reasoning about situations, influences, and indicators. Such reasoning supports strategic analysis of business objectives in light of current enterprise data, allowing analysts to explore scenarios and find alternative strategies. We describe how goal reasoning techniques from conceptual modeling and requirements engineering have been applied to BIM. Techniques are also provided to support reasoning with indicators linked to business metrics, including cases where specifications of indicators are incomplete. Evaluation of the proposed modeling and reasoning framework includes an on-going prototype implementation, as well as case studies.     

Expressiveness and tractability in knowledge representation and reasoning1

A fundamental computational limit on automated reasoning and its effect on knowledge representation is examined. Basically, the problem is that it can be more difficult to reason correctly with one representational language than with another and, moreover, that this difficulty increases dramatically as the expressive power of the language increases. This leads to a tradeoff between the expressiveness of a representational language and its computational tractability. Here we show that this tradeoff can be seen to underlie the differences among a number of existing representational formalisms, in addition to motivating many of the current research issues in knowledge representation.     

Knowledge representation and reasoning in the design of compositesystems

The design process that spans the gap between the requirements acquisition process and the implementation process, in which the basic architecture of a system is defined, and functions are allocated to software, hardware, and human agents. is studied. The authors call this process composite system design. The goal is an interactive model of composite system design incorporating deficiency-driven design, formal analysis, incremental design and rationalization, and design reuse. They discuss knowledge representations and reasoning techniques that support these goals for the product (composite system) that they are designing, and for the design process. To evaluate the model, the authors report on its use to reconstruct the design of two existing composite systems rationally     

Extending CLIPS to support temporal representation and reasoning

Applications using expert systems for monitoring and control problems often require the ability to represent temporal knowledge and to apply reasoning based on that knowledge. Incorporating temporal representation and reasoning into expert systems leads to two problems in development: dealing with an implied temporal order of events using a non-procedural tool; and maintaining the large number of temporal relations that can occur among facts in the knowledge base. In this paper we explore these problems by using an expert system shell, CLIPS (C Language Integrated Production System), to create temporal relations using common knowledge-based constructs. We also build an extension to CLIPS through a user-defined function which generates the temporal relations from those facts. We use the extension to create and maintain temporal relations in a workflow application that monitors and controls an engineering design change review process. We also propose a solution to ensure truth maintenance among temporally related facts that links our temporal extension to the CLIPS facility for truth maintenance.     

Superposition with equivalence reasoning and delayed clause normal form transformation

This paper describes a superposition calculus where quantifiers are eliminated lazily. Superposition and simplification inferences may employ equivalences that have arbitrary formulas at their smaller side. A closely related calculus is implemented in the Saturate system and has shown useful on many examples, in particular in set theory. The paper presents a completeness proof and reports on practical experience obtained with the Saturate system.     

属性RBAC策略的OWL表示和推理

将属性作为授权约束,给出了属性扩展的RBAC模型。提出了一种基于OWL的属性RBAC策略定义和表示方法。该方法支持复杂属性表达式、属性值偏序关系、角色层次关系和约束的定义;在推理机的支持下,可以执行访问控制决策推理,属性表达式支配关系判定和策略知识一致性检测。具体应用案例说明了该方法的可行性。     

A representation method of assembly tasks for dealing with uncertainty

This article proposes a representation method of assembly tasks that enables a robotic system to carry out the tasks under uncertainty. The assembly task is represented by the sequence of contact states and control parameters that are generated by repeated measurements of force sensor outputs with the guide of control sphere conditions. The article describes how the control sphere conditions guarantee the correct transitions of contact states between objects along the sequence of the contact states. Finally the article demonstrates the effectiveness of our representation method by experimental works. © 1998 John Wiley & Sons, Inc.     

Temporal representation and reasoning in artificial intelligence: Issues and approaches

Time is one of the most relevant topics in AI. It plays a major role in several areas, ranging from logical foundations to applications of knowledge‐based systems. In this paper, we survey a wide range of research in temporal representation and reasoning, without committing ourselves to the point of view of any specific application. The organization of the paper follows the commonly recognized division of the field in two main subfields: reasoning about actions and change, and reasoning about temporal constraints. We give an overview of the basic issues, approaches, and results in these two areas, and outline relevant recent developments. Furthermore, we briefly analyze the major emerging trends in temporal representation and reasoning as well as the relationships with other well‐established areas, such as temporal databases and logic programming. This revised version was published online in June 2006 with corrections to the Cover Date.     

Temporal knowledge representation and reasoning techniques usingtime Petri nets

In this paper, we present temporal knowledge representation and reasoning techniques using time Petri nets. A method is also proposed to check the consistency of the temporal knowledge. The proposed method can overcome the drawback of the one presented in Yao (1994). It provides a useful way to check the consistency of the temporal knowledge.     

Invariant representation of information and information quantity in image-processing tasks

A model of a signal (image) containing the information codes in its own digital virtual memory independent of stretching, packing according to intensity, and other prescribed image transformations is proposed. Virtual memory cells contain pixel values of information representation. Storage elements of virtual memory cells are classified as either fixed or modifiable so that arbitrary variation of the latter does not cause image destruction. The number of modifiable storage elements juxtaposed with image pixels is regarded as the integer-valued information quantity. A comparison of image information quantity estimations is curried out. The applications of the model to image recognition, document protection, and other tasks are discussed. The text was submitted by the author in English. Mikhail V. Kharinov. Born 1953. Graduated from Leningrad State University in 1978. Received candidateís degree in 1993. Senior researcher at and St. Petersburg Institute of Informatics and Automation, Russian, Academy of Sciences. Scientific interests: analysis of numerical information, system of numerical representation, hierarchical data structures, idempotent transformations, unified algorithms for processing of images and audio signals, color transformation of images. Author of 60 papers, including the patents.     

Seven layers of knowledge representation and reasoning in supportof software development

The authors' experience in the Programmer's Apprentice project in applying knowledge representation and automated reasoning to support software development is summarized. A system, called Cake, is described that comprises seven layers of knowledge representation and reasoning facilities: truth maintenance, Boolean constraint propagation, equality, types, algebra, frames, and Plan Calculus. Sessions with two experimental software development tools implemented using Cake, the Requirements Apprentice and the Debugging Assistant, are also included     

Operations on permutations and representation in graph form

Two operations on permutations are defined and some of their properties are illustrated. A particular graph is defined as representation of the permutation set under such operations; this graph consists of strongly connected components only, having at most eight vertices.     

OWL-POLAR: A framework for semantic policy representation and reasoning

In a distributed system, the actions of one component may lead to severe failures in the system as a whole. To govern such systems, constraints are placed on the behaviour of components to avoid such undesirable actions. Policies or norms are declarations of soft constraints regulating what is prohibited, permitted or obliged within a distributed system. These constraints provide systems-level means to mitigate against failures. A few machine-processable representations for policies have been proposed, but they tend to be either limited in the types of policies that can be expressed or are limited by the complexity of associated reasoning mechanisms. In this paper, we present a language that sufficiently expresses the types of policies essential in practical systems, and which enables both policy-governed decision-making and policy analysis within the bounds of decidability. We then propose an OWL-based representation of policies that meets these criteria and reasoning mechanisms that use a novel combination of ontology consistency checking and query answering. The proposed policy representation and reasoning mechanisms allow development of distributed agent-based systems that operate flexibly and effectively in policy-constrained environments.