Model formulation as a problem-solving task: Computer-assisted engineering modeling

A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem-solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high-level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models. © 1993 John Wiley & Sons, Inc.     

THE SCOPE OF DIMENSIONAL ANALYSIS IN QUALITATIVE REASONING

Dimensional analysis, traditionally used in physics and engineering to identify quantitative relationships, has recently been applied to qualitative reasoning of physical systems. We illustrate some problems of this approach. In the light of this, we reexamine the fundamentals of dimensional analysis in order to more precisely characterize its scope and limitations as a tool in qualitative reasoning. We also explore its relationship to state equation representations of physical systems. In particular, we describe its value in providing a set of constraints to reduce the ambiguity that bedevils qualitative reasoning schemes. We argue that dimensional analysis should not be seen as a substitute for knowledge about the physics but rather a supplement to other sources of knowledge.     

Artificial intelligence in Computer Aided Design

The combination of Artificial Intelligence (AI) and Computer Aided Design is a trend in computer aided techniques. In this paper, we discuss some AI techniques used in special CAD systems developed at Zhangjiang University. First, an expert system used in art pattern CAD is presented. Accessing the knowledge bases, the expert system reasons to produce various kinds of pseudo-3D art patterns which can be used for textile such as cotton, silk, and carpet. We shall focus on its knowledge representation and fuzzy reasoning mechanism. In the second part of the paper, a geometric modelling system for architectural CAD is described; and in particular, a method to construct the hierarchical geometry model of complex scene environment is introduced. The method is based on the clustering technique of Pattern Recognition. The geometric modelling system can build the hierarchical 3D geometric model automatically and the efficiency of the system is obviously improved with the method.     

A feature-based approach to structural design

Despite the continuing improvements in computeraided design (CAD) systems and improvements in geometric modeling, most CAD systems are used as advanced drafting and drawing management tools by structural designers. A computer model of a structural design usually is generated by creating a detailed geometric model of the primitive components of the design and then attaching attributes, such as physical properties and loading conditions, to the various geometric components to reveal the structural characteristics of those components. Thisbottom-up approach has been inherited from early drafting techniques and contrasts sharply with a structural designer's natural way of thinking and reasoning about the design. Geometric features, on the other hand, provide high-level abstractions of design information and can be tailored to a designer's specific engineering needs. In this paper the advantages of using feature-based techniques in structural CAD systems are discussed. These techniques provide better modeling primitives for users and superior data models for CAD systems for reasoning about the geometry, topology, and engineering properties of a structure.     

PROLEGOMENA TO ANY FUTURE QUALITATIVE PHYSICS

We evaluate the success of the qualitative physics enterprise in automating expert reasoning about physical systems. The field has agreed, in essentials, upon a modeling language for dynamical systems, a representation for behavior, and an analysis method. The modeling language consists of generalized ordinary differential equations containing unspecified constants and monotonic functions; the behavioral representation decomposes the state space described by the equations into discrete cells; and the analysis method traces the transitory response using sign arithmetic and calculus. The field has developed several reasoners based on these choices over some 15 years. We demonstrate that these reasoners exhibit severe limitations in comparison with experts and can analyze only a handful of simple systems. We trace the limitations to inappropriate assumptions about expert needs and methods. Experts ordinarily seek to determine asymptotic behavior rather than transient response, and use extensive mathematical knowledge and numerical analysis to derive this information. Standard mathematics provides complete qualitative understanding of many systems, including those addressed so far in qualitative physics. Preliminary evidence suggests that expert knowledge and reasoning methods can be automated directly, without restriction to the accepted language, representation, and algorithm. We conclude that expert knowledge and methods provide the most promising basis for automating qualitative reasoning about physical systems.     

复杂性和意义含糊问题的定性仿真和控制

许多系统是高阶、时变、非线性，传统的模型分析方法不容易应用。定性推理能避免复杂的数学计算，定性推理有吸引力是因为它涉及到物理系统一般原理的描述，这意味着一个特殊的定性模型能用于描述一个大范围的起作用的条件。在工程上定性推理用于监控和诊断。本文阐述如何用定性推理技术去仿真和控制液位系统的问题。     

Comparing Alternatives in the law*

This paper argues the thesis that a particular style of reasoning, qualitative comparative reasoning (QCR), plays a role in at least three areas of legal reasoning that are central in AI and law research, namely legal theory construction, case-based reasoning in the form of case comparison, and legal proof. The paper gives an informal exposition of one particular way to deal with QCR, based on the author’s previous work on reason-based logic (RBL). Then it contains a substantially adapted formalisation of RBL, to make RBL suitable for dealing with QCR. The paper concludes with a brief discussion of related work. *This paper is based on the chapters 3 and 4 of Hage 2005.     

Development of an integrated expert system for fluvial hydrodynamics

During recent decades, rapid developments in computer science and industry have been influencing every aspect of our social life. Computer Aided Design (CAD) techniques have a significant impact in engineering fields. For fluvial engineering fields, a large number of microcomputer-based programs are applied for hydrodynamics analyses during the project design stage. Recently, achievements in Artificial Intelligence (AI) have been applied in many fields. The application of expert system techniques is an attractive trend in modern design practice. It is believed that the synthetic utilization of CAD and AI techniques would become more attractive for engineers. Yet the studies on this topic are scarce. This paper describes the architecture and implementation of an integrated expert system that assists hydraulic engineers to solve the unsteady open channel flow in river networks. A methodology of combining numerical analysis software into an expert system is presented. The verification, validation, application, and future development of this system are also presented.     

基于STEP的CAD知识表达

建立AI系统与CAD系统都能接受的统一的数据表达方式,是解决系统间失配的方法之一,由于越来越多的CAD系统一始支持STEP标准,基于STEP技术的CAD知识表达,就成为建立统一数据模型的有效方法。文章从知识建模、知识编译、物理实现等角度论述了STEP技术在知识表达领域的应用,并在此基础上阐述了KBS与CAD模块间的无缝集成技术。     

A diagnosis scheme for a large-scale system

This paper presents a method for diagnosis in a large-scale system environment. The method utilizes the theory of hierarchical systems and hybrid diagnostic reasoning from AI (artificial intelligence). In shallow reasoning, which is a part of hybrid reasoning, the concept of entropy is used to determine which component (that might be responsible for a symptom observed) is to be tested next. The procedure is illustrated using a simulated example of a CIM (computer-integrated manufacturing) system, and is implemented on IntelliCorp's knowledge engineering environment (KEE).     

定性空间推理的分层递阶框架

定性空间推理是定性推理和空间推理的重要组成部分 .拓扑和形状是定性空间推理研究的关键问题 .针对定性空间推理已有一般框架存在的问题 ,提出了定性空间推理的分层递阶框架 ,并结合拓扑和形状方面的定性空间推理研究工作阐述了所提出的框架的有效性和合理性 .最后总结了分层递阶框架的要点并提出了基于该框架的进一步研究工作 .     

Efficient and non-parametric reasoning over user preferences

We consider the problem of modeling and reasoning about statements of ordinal preferences expressed by a user, such as monadic statement like “X is good,” dyadic statements like “X is better than Y,” etc. Such qualitative statements may be explicitly expressed by the user, or may be inferred from observable user behavior. This paper presents a novel technique for efficient reasoning about sets of such preference statements in a semantically rigorous manner. Specifically, we propose a novel approach for generating an ordinal utility function from a set of qualitative preference statements, drawing upon techniques from knowledge representation and machine learning. We provide theoretical evidence that the new method provides an efficient and expressive tool for reasoning about ordinal user preferences. Empirical results further confirm that the new method is effective on real-world data, making it promising for a wide spectrum of applications that require modeling and reasoning about user preferences.     

Lp, a logic for representing and reasoning with statistical knowledge

This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an important component of our world knowledge and that such knowledge is used in many different reasoning tasks. The work is further motivated by the observation that previous formalisms for representing probabilistic information are inadequate for representing statistical knowledge. The representation mechanism takes the form of a logic that is capable of representing a wide variety of statistical knowledge, and that possesses an intuitive formal semantics based on the simple notions of sets of objects and probabilities defined over those sets. Furthermore, a proof theory is developed and is shown to be sound and complete. The formalism offers a perspicuous and powerful representational tool for statistical knowledge, and a proof theory which provides a formal specification for a wide class of deductive inferences. The specification provided by the proof theory subsumes most probabilistic inference procedures previously developed in AI. The formalism also subsumes ordinary first-order logic, offering a smooth integration of logical and statistical knowledge.     

Qualitative Spatial Representation and Reasoning with the Region Connection Calculus

This paper surveys the work of the qualitative spatial reasoning group at the University of Leeds. The group has developed a number of logical calculi for representing and reasoning with qualitative spatial relations over regions. We motivate the use of regions as the primary spatial entity and show how a rich language can be built up from surprisingly few primitives. This language can distinguish between convex and a variety of concave shapes and there is also an extension which handles regions with uncertain boundaries. We also present a variety of reasoning techniques, both for static and dynamic situations. A number of possible application areas are briefly mentioned.     

CONSTRAINT MECHANISMS IN AUTOMATED KNOWLEDGE GENERATION

Abstract

In the past decade, the use of control and diagnostic reasoning systems in different areas of government, industry, and university operations has increased. A great number of these systems find their basis in engineering, specifically in process control. The majority of the time devoted to the development of these systems is spent in the areas of Knowledge Engineering (KE) and Knowledge Acquisition (KA). Extensive research for the development of systems that perform the KE task is under way. This article presents an approach toward automatic knowledge acquisition. The objective of this research was to construct a complete knowledge base for a diagnostic and control reasoning system from information that resides in Computer Aided Design (CAD) databases. This work will decrease the amount of time spent in the manual generation of knowledge bases for diagnostic reasoning systems, ft will also enable the creation of more reliable knowledge bases since less hand coding is required.     

QUBICS: A general quantity base management system

The problem of ambiguities has presented an obstacle to the practical use of qualitative simulation in dealing with real-life dynamic systems. One of the dominant causes of these prevailing ambiguities is the parsimonious use of information by current qualitative simulation models. Humans seem to utilize more quantity information to produce less ambiguous predictions when reasoning on dynamic mechanisms. To cover such human algebraic reasoning, the representation and processing of quantity information should be extended. This paper presents an inference system specialized for processing quantity information in order to support qualitative simulation. The system stores the information in the form of constraints into a quantity base, a notion that is parallel to the symbolic fact base, and processes queries based on linear programming techniques and goal-tree search. Temporal reasoning is also facilitated through an improved utilization of derivative information.