Knowledge representation for mathematical discovery: Three experiments in graph theory

This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graphy theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factural knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.     

A flexible architecture for distributed knowledge based systems with nomadic access through handheld devices

Nowadays any Knowledge Based System (KBS) realization needs of intercommunication among distributed components and to use non-connected and distributed data sources, which poses several challenges to the classical Artificial Intelligence field of KBS.

The multiagent paradigm and the use of ontologies are considered to be suitable tools to face the problems of designing and developing today KBS. On the other hand, using such networked KBS through handheld devices makes more efficient exploitation and interaction with the system.

This paper presents an open and flexible architecture for a distributed KBS and an application of it to construct a system for Psychological Disorders consulting, the so called PDA² (Psychological Disorder Assistant through PDA). We analyze the main features of the architecture as well as the agent tools we may use to construct it. Additionally, we present a support ontology for Psychological Disorders.     

Automating application software generation

Many approaches have been proposed to enhance software productivity and reliability. These approaches typically fall into three categories: the engineering approach, the formal approach and the knowledge-based approach. But the optimal gain in software productivity cannot be obtained if one relies on only one of these approaches. This paper describes the work in knowledge-based software engineering conducted by the authors for the past 10 years. The final goal of the research is to develop a paradigm for software engineering which integrates the three approaches mentioned above. A knowledge-based tool which can support the whole process of software development is provided in this paper.     

The validity of psychophysiological detection of information with the Guilty Knowledge Test: A meta-analytic review.

The authors performed a meta-analysis based on 169 conditions, gathered from 80 laboratory studies, to estimate the validity of the Guilty Knowledge Test (GKT) with the electrodermal measure. The over-all average effect size was 1.55, but there were considerable variations among studies. In particular, mock-crime studies produced the highest average effect size (2.09). Three additional moderators, were identified: Motivational instructions, deceptive ("no") verbal responses, and the use of at least 5 questions were associated with enhanced validity. Finally, a set of 10 studies that best approximated applications of the GKT under optimal conditions produced an average effect size of 3.12. The authors discuss factors that might limit the generalizability of these results and recommend further research of the GKT in realistic setups. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

MKR──一种有效的增量式概念获取系统

本文对示例式学习的ＩＤ５Ｒ算法进行了深入的研究并作了进一步的改进，提高了其计算效率．并提出一种适合于增量式学习的抗干扰机制，拓广了算法的适用范围．     

On the ontological expressiveness of information systems analysis and design grammars

Abstract. Information systems analysis and design (ISAD) methodologies provide facilities for describing existing or conceived real-world systems. These facilities are ontologically expressive if they are capable of describing all real-world phenomena completely and clearly. In this paper we formally examine the notion of the ontological expressiveness of a grammar and discuss some of its implications for the design and use of ISAD methodologies. We identify some generic ways in which ontological expressiveness may be undermined in a grammar and some potential consequences of these violations. We also examine ontological expressiveness within the context of some other desirable features that might be considered in the design of ISAD methodologies.     

知识驱动的UG中的产品开发

重点论述了UG中的KBE即KF模块的知识驱动产品开发设计过程及其关键技术，介绍了知识驱动自动化过程，实例剖析了UG中的KF在产品开发设计过程中的应用。     

Merging uncertain information with semantic heterogeneity in XML

Semistructured information can be merged in a logic-based framework [6, 7]. This framework has been extended to deal with uncertainty, in the form of probability values, degrees of beliefs, or necessity measures, associated with leaves (i.e. textentries) in the XML documents [3]. In this paper we further extend this approach to modelling and merging uncertain information that is defined at different levels of granularity of XML textentries, and to modelling and reasoning with XML documents that contain semantically heterogeneous uncertain information on more complex elements in XML subtrees. We present the formal definitions for modelling, propagating and merging semantically heterogeneous uncertain information and explain how they can be handled using logic-based fusion techniques. Anthony Hunter received a B.Sc. (1984) from the University of Bristol and an M.Sc. (1987) and Ph.D. (1992) from Imperial College, London. He is currently a reader in the Department of Computer Science at University College London. His main research interests are: Knowledge representation and reasoning, Analysing inconsistency, Argumentation, Default reasoning and Knowledge Fusion. Weiru Liu is a senior lecturer at the School of Computer Science, Queen's University Belfast. She received her B.Sc. and M.Sc. degrees in Computer Science from Jilin University, P.R China, and her Ph.D. degree in Artificial Intelligence from the University of Edinburgh. Her main research interests include reasoning under uncertainty, knowledge representation and reasoning, uncertain knowledge and information fusion, and knowledge discovery in databases. She has published over 50 journal and conference papers in these areas.     

基于知识的服装CAD

本文提出一种基于知识的计算机辅助服装设计系统，该系统运用了人工智能中产生式和框架思想，把服装设计中智能和各种常识和技能进行抽象，把服装按人体部位进行自上而下的分层，从而把一个很复杂的问题转变为简单问题。本文同时提出真实显示服装效果图的数学模型。