Activation pattern controlled rules: Towards an integration of data-driven and command-driven programming

The attractions and drawbacks of data-driven programming are discussed in the context of rule-based forward chaining systems. The relationships between data-driven and command-driven programming are analyzed in the context of a course-registration example. A new form of production rule, called an activation pattern controlled rule, that generalizes classical forward chaining rules is introduced. Activation pattern controlled rules are triggered by calls of commands; that is, by the intension to perform a command but not necessarily by the result of applying the command itself. We demonstrate that activation pattern controlled rules facilitate the integration of data-driven and command-driven programming, support preventive programming as well, and allow for writing rule-based programs more transparently. We also survey our experiences in implementing an inference engine for activation pattern controlled rules.     

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

A rule-based system for automatic seismic discrimination

This paper proposes a rule-based system for automatic seismic discrimination, i.e. classification of earthquakes and underground nuclear explosions. It incorporates rule-based deduction, pattern recognition and signal processing for an effective identification. The seismological knowledge and heuristics are represented by a set of production rules. Facts and assertions of the production rules are derived from seismic signals using signal processing and pattern recognition methods. Due to the uncertainty nature of this problem there are certainly factors associated with both antecedents and the rules. The control strategy is data-driven, i.e. forward-chaining for better efficiency. This approach can be applied to other signal and image interpretation problems.     

A generic, object-oriented case-knowledge representation scheme, and its integration into a wider information management scenario

Abstract: A knowledge base management system (KBMS) realises a combination of techniques found in database management systems and knowledge-based systems. At the data model and knowledge representation level, many systems of this kind constitute a marriage of the relational data model and the rule-based reasoning. Experience has shown that either approach is restricted in the way it can express the demanding information and knowledge structures required for applications like decision support systems. Two new technologies offer an exciting new integrated approach to knowledge management. Object-oriented database management systems (OODBMS) provide an object model that supports powerful abstraction mechanisms to facilitate the modelling of highly structured information. Whereas case-based reasoning (CBR) systems are knowledge bases which organise their capabilities around a memory of past cases and the notion of similarity. Both types of system are built upon two fundamental concepts: 1) the retrieval of entities with potentially complex structure, called objects in the former, and cases in the latter type of system; 2) the organisation of those entities in collections with common characteristics. In an OODBMS such collections are termed extents, and in CBR they are usually called categories. In either system, the conceptual meta notion to represent both, objects as well as extents, and cases as well as categories, is the class.
Revolving around a Conceptual Case Class and extending a standard object model, this paper proposes a novel and general approach to represent case-knowledge and to build KBMSs. The work presented here is a spin-off of the design of an object query language within the ESPRIT project Lynx.     

From decision tables to expert system shells

Building and maintaining high quality knowledge based systems is not a trivial task. Decision tables have sometimes been recommended in this process, mainly in verification and validation. In this paper, however, it is shown how decision tables can also be used to generate, and not just to validate, knowledge bases and how the transformation process from decision tables to knowledge bases can be organized. Several options to generate rules or other knowledge representation from decision tables are described and evauluated.

The proposed generation strategy enables the knowledge engineer to concentrate on the acquisition and modelling issues and allows him to isolate the knowledge body from its implementation. The generation process has been implemented for two commercial tools, AionDS and KBMS and has been applied to real world applications.     

The design and implementation of K: a high-level knowledge-base programming language of OSAM*.KBMS

The OSAM*.KBMS is a knowledge-base management system, or the so-called next-generation database management system, for non-traditional data/knowledge-intensive applications. In order to define, query, and manipulate a knowledge base, as well as to write codes to implement any application system, we have developed an object-oriented knowledge-base programming language called K to serve as the high-level interface of OSAM*.KBMS. This paper presents the design of K, its implementation, and its supporting KBMS developed at the Database Systems Research and Development Center of the University of Florida. Edited by Dennis McLeod. Received July 1992 / Accepted August 1995     

An algebraic approach to revising propositional rule-based knowledge bases

One of the important topics in knowledge base revision is to introduce an efficient implementation algorithm. Algebraic approaches have good characteristics and implementation method; they may be a choice to solve the problem. An algebraic approach is presented to revise propositional rule-based knowledge bases in this paper. A way is firstly introduced to transform a propositional rule-based knowledge base into a Petri net. A knowledge base is represented by a Petri net, and facts are represented by the initial marking. Thus, the consistency check of a knowledge base is equivalent to the reachability problem of Petri nets. The reachability of Petri nets can be decided by whether the state equation has a solution; hence the consistency check can also be implemented by algebraic approach. Furthermore, algorithms are introduced to revise a propositional rule-based knowledge base, as well as extended logic programming. Compared with related works, the algorithms presented in the paper are efficient, and the time complexities of these algorithms are polynomial.     

Towards a theory of fuzzy causality for diagnosis

Parsimonious covering offers an alternative to rules for building diagnostic expert systems. Abductive paradigms, such as parsimonious covering, are a departure from the forward-chaining, rule-based approach, which is based on deduction. Parsimonious covering addresses weaknesses of rule-based systems where the diagnosis may contain multiple faults or disorders, or where the need to include all the necessary context for each rule's application in the antecedent clauses of each rule would make the representation of the knowledge base too large or overly complex.

In this paper, we compare the notions of deterministic covering and the probabilistic causal model with two fuzzy analogies: fuzzy subsethood and fuzzy similarity. Monotonic upper and lower bounds for fuzzy similarity are derived, and pruning opportunities are identified for search through the power set of disorders, given a measured, crisp manifestation set.     

Knowledge tracing: Modeling the acquisition of procedural knowledge

This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has mastered each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.     

Cape: extending Clips for the internet

This paper describes Cape, a programming environment combining Clips And Perl with Extensions. Clips is an efficient and expressive forward-chaining rule-based system with a flexible object system. Perl is a popular procedural language with extremely powerful regular expression matching facilities, and a huge library of freely available software. Cape closely integrates these languages, and provides extensions to facilitate building systems with an intimate mixture of the two. The paper describes the facilities Cape offers programmers and the demonstration systems and “component applications” distributed with it. The use of the system is then discussed with reference to dime (Distributed Information Manipulation Environment), a toolkit being developed to support identifying and coordinating the use of external knowledge sources. Finally, planned developments of the system are indicated.     

装配知识库模型及其核心设计

本文分析了研究和建立基于智能机器人装配系统的知识库的必要性，并探讨了实现途径．接着，我们提出了支持装配知识库的知识模型及其核心的体系结构，并讨论了支持该知识模型的建模工具ＥＸＰＲＥＳＳ语言．最后，给出结论.     

Robustness of decentralized knowledge systems: a grammar-theoretic view

This paper introduces the formal framework of grammar systems to handle a practical and important property of decentralized rule-based knowledge systems. The property is called robustness. In our framework, a rule-based system is robust when some rules can be removed from it and yet its critical functionality remains unchanged. As a theoretical framework for study robustness of decentralized rule-based systems we use grammar systems. We prove within that framework that the question whether a knowledge system is robust or not is undecidable. In contrast, we prove with the same framework that whether or not a component is ever enabled, or whether or not a component working in a special—so called maximal—mode blocks the further functioningof a systems when enabled, are decidable. Some open problems are also formulated.     

Rule-based consistency enforcement for knowledge-based systems

A rule-based approach for the automatic enforcement of consistency constraints is presented. In contrast to existing approaches that compile consistency checks into application programs, the approach centralizes consistency enforcement in a separate module called a knowledge-base management system. Exception handlers for constraint violations are represented as rule entities in the knowledge base. For this purpose, a new form of production rule called the activation pattern controlled rule is introduced: in contrast to classical forward chaining schemes, activation pattern controlled rules are triggered by the intent to apply a specific operation but not necessarily by the result of applying this operation. Techniques for implementing this approach are discussed, and experiments in speeding up the system performance are described. Furthermore, an argument is made for more tolerant consistency enforcement strategies, and how they can be integrated into the rule-based approach to consistency enforcement is discussed     

A fuzzy Petri net-based expert system and its application to damageassessment of bridges

In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfil the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.     

Genetic production systems for intelligent problem solving

The paper discusses an evolutionary knowledge approach to intelligent problem solving. A rule-based production system is used to model the problem and the means by which the problem space should be searched. Search heuristics are modelled as production rules. These rules are redundant as there may be more than one view on the best method for building solutions. Some rules may have complex reasoning for their actions, others have none. Deciding which rule is most appropriate is solved by a genetic algorithm and ultimately only the fitter rules will survive. The approach eliminates the necessity of designing problem specific search or variation operators, leaving the genetic algorithm to process patterns independent of the problem at hand. Learning methods and how they aid evolution is also discussed: they are Lamarckian learning and the Baldwin effect. The approach is tested on a scheduling problem.     

Some knowledge-acquisition methods for prospector-like systems

The paper deals with the knowledge-acquisition methods designed and tested under the FEL-EXPERT Project, which is aimed at the development of rule-based diagnostic shells. Three different approaches have been used for knowledge acquisition: pattern-recognition, decision-tree, and intensional, pure probabilistic approaches. The designed methods may be applied to a wide subclass of rule-based diagnostic systems that exploit the pseudo-Bayesian model for uncertainty handling. The experimental results are discussed.     

A software engineering methodology for rule-based systems

Current expert systems are typically difficult to change once they are built. The authors introduce a method for developing more easily maintainable rule-based expert systems, which is based on dividing the rules into groups and focusing attention on those facts that carry information between rules in different groups. They describe a new algorithm for grouping the rules of a knowledge base automatically and a notation set of software tools for the proposed method. The approach is supported by a study of the connectivity of rules and facts in rule-based systems; it is found that they indeed have the latent structure necessary for the programming methodology. Recent experimental results also support the approach. In contrast to the homogeneous way in which the facts of a rule-based system are usually viewed, this approach shows that certain facts are more important than others with regard to future modifications of the rules