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Timing analysis of reactive rule-based programs

CONTROL ENGINEERING PRACTICE 1998年第3期6卷 403-408页

作者： Lin, M Malec, J Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Malardalens Hogskola Dept Comp Engn S-72123 Vasteras Sweden

Predictability is the crucial feature of real-time systems. It requires that all the tasks in a system meet their timing constraints. For an embedded complex system involving a number of subsystems, the timing properties of the subsystems play an important role, since the total system's response depends on the temporal behaviour of all the subsystems. In this paper, the timing properties of reactive programs written in a rule-based language RL are analyzed. RL is a relatively simple rule language for programming the discrete part of hybrid applications, implemented using a layered architecture. The language has already been used in prototyping a complex automotive application. The upper timing bounds for RL programs, executed using either of the two evaluation strategies, have been studied. Moreover, an analysis tool has been implemented and applied to derive the timing behaviour of a non-trivial application. Some comments on the temporal behaviour of a layered system, consisting of a discrete RL program combined with a set of periodic tasks, are given. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： timing analysis rule-based programming embedded systems layered software

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Timing analysis of reactive rule-based programs

Timing analysis of reactive rule-based programs

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IFAC/IFIP Workshop on Real-Time programming (WRTP 97)

作者： Lin, M Malec, J Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Malardalens Hogskola Dept Comp Engn S-72123 Vasteras Sweden

关键词： timing analysis rule-based programming embedded systems layered software

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Design schemes for rule-based systems

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INTERNATIONAL JOURNAL OF EXPERT SYSTEMS 1997年第1期10卷 1-36页

作者： Chander, PG Radhakrishnan, T Shinghal, R Concordia Univ Montreal Que Canada

The design of rule-based systems is often plagued by errors and anomalies. In this paper, we assume that a way to abstract the domain knowledge is to use goals. The problem solving model of the system is in terms of goal inferences that occur in the rule base level using sets of rule sequences called paths. The mapping between goals and their representation in a rule base is one-to-many and results in several design schemes for rule-based systems: the various design schemes impose different mapping restrictions for representing a goal in a rule base. Thus, the applicability of a design scheme and the relationships between the various design schemes are important factors that need to be considered before choosing a scheme for development. We explore one specific relationship, called inheritance, between the design schemes. The pros and cons of using two schemes D-A and D-B, that have an inheritance relationship between them are analyzed for development versus maintenance. The paper also provides metric measures to facilitate general comparison between design schemes. Finally, we outline an application of inheritance for automatically transforming a rule base from one scheme into another.

关键词： COMPUTER software development rule-based programming

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The use of mathematical programming to verify rule-based knowledge

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ANNALS OF OPERATIONS RESEARCH 1996年第1期65卷 181-193页

作者： OLeary, DE Schaffer, J UNIV SO CALIF SCH BUSINESSLOS ANGELESCA 90089 RYDER DEDICATED LOGIST MIAMIFL 33166

The purpose of this paper is to use mathematical programming, including linear programming, dynamic programming, integer programming and goal programming to verify rule-based knowledge. We investigate both domain independent verification, exploiting the general structure of rules, and domain dependent verification, exploiting structure in the domain. Mathematical programming software is readily available and is very efficient. As a result, verification using mathematical programming can be very efficient at finding errors. Mathematical programming can be used to more than just find errors in knowledge representation. Once an error has been found, mathematical programming can be used to ''recommend'' an alternative. The recommendation can take into account the previous verified knowledge to mitigate the potential introduction of redundant knowledge and to help guide the choice process. Normally the development of recommendations to fix errors has been ignored in the verification literature, and treated as a separate knowledge acquisition task. Accordingly, this paper also extends the verification effort by providing a recommendation on how to fix errors.

关键词： MATHEMATICAL programming KNOWLEDGE management COMPUTER programming LINEAR programming DYNAMIC programming INTEGER programming MATHEMATICAL optimization rule-based programming

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Design of a knowledge-based simulation environment

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SIMULATION 1996年第2期67卷 121-135页

作者： Williams, MD Gorman, EC Shiva, SG UNIV ALABAMA DEPT COMP SCIHUNTSVILLEAL 35899

This paper presents a design of a knowledge-based simulation environment based on techniques from conventional simulation methods, object-oriented programming methods, and knowledge-based programming methods. The environment has cooperating knowledge-based and simulation parts integrated into one modeling system. An idealized knowledge-based simulation model is presented along with a language for implementing such a model. This language is based on Symbolics Common Lisp with Flavors package. A model of the software development process is presented to illustrate the use of the knowledge-based simulation environment. This software development model is not intended to be a rigorous model of the software development process, but only to illustrate the use of the knowledge-based development environment which is presented in this paper.

关键词： knowledge-based simulation discrete-event simulation object-oriented programming rule-based programming

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Intelligent critiquing and tutoring of spatial reasoning skills

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 1996年第3期10卷 235-249页

作者： Mengshoel, OJ Chauhan, S Kim, YS UNIV ILLINOIS DEPT GEN ENGNURBANAIL 61801 UNIV ILLINOIS DEPT COMP SCIURBANAIL 61801

The ability to reason spatially is an important skill required for engineers, particularly in engineering design and construction. One aspect of spatial reasoning is visualizing and constructing three-dimensional (3D) solid objects from two-dimensional (2D) projections. To assist in teaching this to engineering students, an instructional software system is being developed at the University of Illinois. This instructional software system is comprised of the Visual Sweeper and the Visual Teacher. The Visual Sweeper is a geometric framework for solving missing view problems. In missing view problems, students create 3D solid objects from two 2D projections by applying operations inverse to orthographic projection. The Visual Teacher, which is the focus of this article, is an intelligent critiquing and tutoring module that gives feedback to the student regarding partial solutions to missing view problems. The Visual Teacher is comprised of a Recognizer and a Critiquer. The Recognizer identifies which solution solid the student's partial solution is closest to. based on the solution solid and a student's partial solution, the Critiquer gives critique and advice to the student. The Recognizer is based on an algorithm for bipartite graph matching, while the Critiquer uses a rule-based approach. This paper describes the Visual Teacher, gives examples of how it can be used, presents preliminary evaluation results, and discusses the system's assumptions and limitations.

关键词： intelligent tutoring spatial reasoning missing view problem bipartite graph matching rule-based programming

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INTEGRATING MULTIPLE PARADIGMS WITHIN THE BLACKBOARD FRAMEWORK

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1995年第3期21卷 244-262页

作者： VRANES, S STANOJEVIC, M Mihajlo Pupin institute Computer Systems Department Belgrade 11060 Yugoslavia

While early knowledge-based systems suffered the frequent criticism of having little relevance to the real world, an increasing number of current applications deal with complex, real-world problems. Due to the complexity of real-world situations, no one general software technique can produce adequate results in different problem domains, and artificial intelligence usually needs to be integrated with conventional paradigms for efficient solutions. The complexity and diversity of real-world applications have also forced the researchers in the AI field to focus more on the integration of diverse knowledge representation and reasoning techniques for solving challenging, real-world problems. Our development environment, BEST (Blackboard-based Expert Systems Toolkit), is aimed to provide the ability to produce large-scale, evolvable, heterogeneous intelligent systems. BEST incorporates the best of multiple programming paradigms in order to avoid restricting users to a single way of expressing either knowledge or data. It combines rule-based programming, object-oriented programming, logic programming, procedural programming and blackboard modelling in a single architecture for knowledge engineering, so that the user can tailor a style of programming to his application, using any or arbitrary combinations of methods to provide a complete solution. The deep integration of all these techniques yields a toolkit more effective even for a specific single application than any technique in isolation or collections of multiple techniques less fully integrated. Within the basic, knowledge-based programming paradigm, BEST offers a multiparadigm language for representing complex knowledge, including incomplete and uncertain knowledge. Its problem solving facilities include truth maintenance, inheritance over arbitrary relations, temporal and hypothetical reasoning, opportunistic control, automatic partitioning and scheduling, and both blackboard and distributed problem-solving paradi

关键词： ARTIFICIAL INTELLIGENCE BLACKBOARD MODEL EXPERT SYSTEMS LOGIC programming MODEL-based REASONING MULTIPARADIGM APPROACH OBJECT-ORIENTED programming PROCEDURAL-programming rule-based programming

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KNOWLEDGE-based REACTIVE SCHEDULING IN A FLEXIBLE MANUFACTURING SYSTEM

KNOWLEDGE-BASED REACTIVE SCHEDULING IN A FLEXIBLE MANUFACTUR...

引用

IFIP TC5/WG5.7 International Workshop on Knowledge-based Reactive Scheduling

作者： FISCHER, K

ISBN: (纸本)0444818146

This paper presents a framework for the design of the planning and controlling components in a flexible manufacturing system. A hierarchical planning structure is presented which consists of six layers: the layer of the production planning and control system, the layer of the shop floor control system, the task coordination layer, the task planning layer, the task execution layer and the machine control layer. The last four layers mentioned are present separately for each manufacturing device which is able to execute tasks autonomously. For the abstract system which consists of the task coordination component, the task planner, the task execution component, the machine control component, and the flexible manufacturing device itself the term auto;rzomous system is introduced. Special emphasis is placed on solving the scheduling problem in the shop floor control system and task planning in the task planning layer of the autonomous systems. The implementation of the planning and controlling components of the hierarchical planning structure was done for the example of the model of a manufacturing plant [4, 7] with the help of the rule-based multi-agent system MAGSY [8, 6], which is briefly described at the end of the paper.

关键词： KNOWLEDGE-based REACTIVE SCHEDULING PRODUCTION PLANNING PRODUCTION CONTROL COMPUTER-AIDED MANUFACTURING FLEXIBLE MANUFACTURING SYSTEM AUTONOMOUS SYSTEMS TASK PLANNING MULTIAGENT SYSTEMS rule-based programming

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HISTORICAL RETE NETWORKS TO SUPPORT THE DEBUGGING OF FORWARD-CHAINING rule-based PROGRAMS

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International Journal on Artificial Intelligence Tools 1993年第1期2卷 47-70页

作者： SHARON M. TUTTLE CHRISTOPH F. EICK Department of Computer Science University of Houston Houston TX 77204–3475 USA

Forward-chaining rule-based programs, being data-driven, can function in changing environments in which backward-chaining rule-based programs would have problems. But, degugging forward-chaining programs can be tedious; to debug a forward-chaining rule-based program, certain ‘historical’ information about the program run is needed. Programmers should be able to directly request such information, instead of having to rerun the program one step at a time or search a trace of run details. As a first step in designing an explanation system for answering such questions, this paper discusses how a forward-chaining program run’s ‘historical’ details can be stored in its Rete inference network, used to match rule conditions to working memory. This can be done without seriously affecting the network’s run-time performance. We call this generalization of the Rete network a historical Rete network. Various algorithms for maintaining this network are discussed, along with how it can be used during debugging, and a debugging tool, MIRO, that incorporates these techniques is also discussed.

关键词： Rete networks Forward-chaining programming rule-based programming Debugging Explanation

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KNOWLEDGE IN OPERATION

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IBM JOURNAL OF RESEARCH AND DEVELOPMENT 1992年第6期36卷 965-989页

作者： BOLLINGER, T PLETAT, U IBM Germany GSDL Software Architectures and Technologies Hans-Klemm-Str. 45 0-7030 Boeblingen Germany|c|

The L(LILOG) knowledge representation language and an inference engine to interpret it have been developed as part of the LILOG project, where new concepts for understanding natural-language texts were investigated. L(LILOG) is a typed predicate logic whose type system has adopted the concepts of KL-One-like languages. Further language constructs allow the formulation of default and control knowledge. The inference engine for L(LILOG) was designed as an experimental theorem prover, allowing us to investigate the behavior of various inference calculi as well as a number of search strategies. Processing with L(LILOG) is not restricted to a propositional reasoner for logical formulas;we are also able to delegate special kinds of inferences to external deductive components. Currently, one such external reasoner for processing spatial information on the basis of analog representation is attached to the inference engine.

关键词： .theorem prover natural-language language constructs search strategies inference engine rule-based programming knowledge based system external reasoner processing spatial LLILoG external deductive

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