A Reduced Instruction Set Machine for Knowledge Based Systems

In this work, a system level design and conception of a System-on-a-Chip (SoC) for the execution of cognitive agents in robotics will be presented. The cognitive model of the Concurrent Autonomous Agent (CAA), which was already successfully applied in several robotics applications, is used as a reference for the development of the hardware architecture. This cognitive model comprises three levels that run concurrently, namely the reactive level (perception-action cycle that executes predefined behaviours), the instinctive level (receives goals from cognitive level and uses a knowledge based system for selecting behaviours in the reactive level) and the cognitive level (planning). For the development of such system level hardware model, the C++ library SystemC with Transaction Level Modelling (TLM) 2.0 will be used. A system model of a module that executes a knowledge based system is presented, followed by a system level description of a processor dedicated to the execution of the Graphplan planning algorithm. The buses interconnecting these modules are modelled by the TLM generic payload. Results from simulated experiments with complex knowledge bases for solving planning problems in different robotics contexts demonstrate the correctness of the proposed architecture. Finally, a discussion on performance gains takes place in the end.

Evaluating the success of a knowledge acquisition (KA) task is difficult and expensive. Most evaluation approaches rely on the expert themselves, either directly, or indirectly by relying on data previously prepared with the help of experts. In incremental KA, knowledge base (KB) errors are monitored and corrected by an expert. Thus, during its evolution a record of the knowledge based system (KBS) performance is usually easy to keep. We propose to integrate with the incremental KA process, an evaluation process based on a statistical analysis to estimate the effectiveness of the KBS, as the KBS is actually evolved. We tailor such an analysis for Ripple Down Rules (RDR), which is an effective incremental KA methodology where a record of the KBS performance can be easily derived and updated as new cases are processed by the system. An RDR KB is a collection of rules with hierarchical exceptions, which are entered and validated by the expert in the context of their use. This greatly facilitates the knowledge maintenance task which, characteristically in RDR, overlaps with the incremental KA process. The work in this paper aims to overlap evaluation with maintenance and development of the knowledge base. It also minimises the major expense in deploying the RDR KBS, that of keeping a domain expert on-line during maintenance and the initial period of deployment. The expert is not kept on-line longer than it is absolutely necessary. We use the structure and semantics of an evolving RDR KB, combined with proven machine learning statistical methods, to estimate the added value in every KB update, as the KB evolves. Using these values, the decisionmakers in the organisation employing the KBS can apply a cost-benefit analysis of the continuation of the incremental KA process. They can then determine when this process, involving keeping an expert on-line, should be terminated.

Research in verification and validation (V&V) of knowledge based systems (KBS) has emerged as a distinct field in the last decade and is intended to address issues associated with KBS quality aspects and to credit such applications to the same degree as conventional applications. Part of the knowledge and techniques developed are now ready to be transferred to industry for the implementation of software certification. Certification is probably the most advanced use of the V&V methods, techniques and knowledge that have been developed so far. The paper approaches the challenging issues of certifying the knowledge base (KB) component of a KBS. KB certification uses a number of different certification methods such as static analysis, testing, inspection, and modeling to assess the quality of the KB component. Although these methods are currently used in KBS development, it is not yet clear how they would be applied by an independent certification organization to arrive at a pass/fail decision. The article discusses the components of KB certification and identifies application areas directly connected with industrial needs.

It is very difficult to build a knowledge base in the mechanical engineering field. Because there is a great deal of complicated knowledge in mechanical engineering in comparison with other engineering fields. To build a large knowledge base, much research to connect Data Base System (DBS) and Knowledge Base System (KBS) systematically has been conducted. DBS is suitable for storing much knowledge but is not suitable for the representation of knowledge. Therefore, we believe it is possible in the mechanical engineering field to realize DBS characters in KBS by using an extended deamon function and extended system slot. If this system is realized, KBS will be suitable for managing and representing knowledge, and it will be easy to build a knowledge base in the mechanical engineering field. This paper describes the method of building a large knowledge base in mechanical engineering using an extended daemon function and an extended system slot, and "the realization of such a knowledge base."

Ever since MYCIN introduced the idea of computer-based explanations to the artificial intelligence community, it has come to be taken for granted that all knowledge-based systems (KBS) need to provide explanations. The first part of this dissertation proposes a cognitive learning theory based model that both clarifies the reasons as to why KBS need to provide explanations and serves as the basis for conceptualizing the provision of KBS explanations. Using the concepts of the feedforward and feedback operators of cognitive learning it develops strategies for providing KBS explanations and uses them to classify the various types of explanations found in current KBS applications. The roles of feedforward and feedback explanations within the context of the theory of cognitive skill acquisition and a model of expert judgment are also analyzed. These, together with past studies of KBS explanations, suggest that user expertise, the types of explanations provided, and the level of user agreement are significant factors that influence the explanation seeking behavior of users. The dissertation also explores the effects of the use of KBS explanations in judgmental decision making situations supported by a KBS. It identifies and considers four distinct categories of potential effects of the use of explanations--learning effects, perceived effects, behavioral effects, and effects on judgmental decision making. The second part of the dissertation empirically evaluates the explanation provision strategies in a laboratory experiment in which 80 novice and expert subjects used a KBS for financial analysis to make judgments under conditions of uncertainty. The experiment was designed specifically to investigate the following fundamental research questions: (1) To what extent are the various kinds of explanations used? (2) How does user expertise, the feedforward and feedback provision of explanations, and the level of user agreement influence the amount and the types of explanations that are used? and (3) Does the use of explanations affect the accuracy of judgmental decision-making and user perceptions of usefulness? Some of the major results relating to the determinants of the use of KBS explanations include: (1) user expertise is not a determinant of the proportion of explanations used but influences the types of explanations that are used, (2) explanation provision strategy is a critical determinant of the use of KBS explanations with feedback explanations being used significantly more than feedforward explanations, and (3) the three types of explanations are used in different proportions with the Why and How explanations being used significantly more that the Strategic explanations. (Abstract shortened by UMI.)

Artificial intelligence can assist in industrial control in many ways, but this paper concentrates on the role of knowledge-based systems in the areas of control, detection and diagnosis of faults, scheduling and planning. The paper argues that the application of knowledge-based techniques can provide a unification of these and other activities, and outlines the requirements of knowledge representation and inference techniques that are needed to make such systems a reality. The advances in hardware, including sensors as well as computers, that will be driven by and in turn stimulate the introduction of the knowledge-based industrial control systems are also considered.

Dutch research on knowledge-based instructional systems: Introduction to the special issue

Knowledge-based systems (KBS) are being widely used for risk analysis of structures. The KBS make use of various types of observations made during postdisaster damage surveys and also the expertise of various domain engineers. Consolidation of all the observations to form the knowledge base (KB) is a difficult task. Each of the observations consists of various parameters. Establishing parameter interdependencies is important for creating a KB. The use of a graph theoretic technique is proposed in this paper to determine the interdependencies among various parameters useful in the creation of a KB. This approach helps in identifying necessary and sufficient data required to develop an effective KBS for risk analysis of structures. Using the proposed approach, an example problem of establishing parameter interdependencies for risk analysis of roof structure against damage due to cyclonic winds is solved. This example also demonstrates how sufficiency and consistency of the observations can be checked.

Knowledge-based Systems and Expert Systems, in particular, are expensive to build and difficult to validate and debug because of their complexity and dynamism. Therefore, it is not easy for knowledge engineer and domain expert to identify the gaps and mistakes in knowledge base. Unit testing is unable to cover validation process at all stages, in many cases manual thorough review of decision process is needed. In this paper we spot main approaches to validation and verification issue and describe a component that helps to debug a knowledge base by visualising execution of rules that derive a particular result. This component is developed for Knowledge-based Systems built on Drools Platform1 and we demonstrate application of this component in a knowledge-based engineering system for structural optical design.

The development of a knowledge-based software package for the analysis and control of electric distribution networks is discussed. A relational knowledge base, implemented in Prolog, is used to insure fast access to the data and an efficient post-reconfiguration update algorithm. An expert system shell DECIDE, is used to interpret human knowledge coded as knowledge clusters, and to provide a menu-driven user interface. The knowledge-based system described here has the following advantages: its expert system shell provides 'backup' and 'explanation' facilities; it can generate all the possible network reconfigurations and recommend the best choice; and the Prolog implementation of the algorithms is easy to maintain. The knowledge base is easy to expand and can be modified during execution. The prototype system demonstrates that artificial intelligence techniques can be used in power system analysis and reconfiguration, and provides a platform on which a base future research on the utility of expert system technology in distribution automation. >

Knowledge-based systems (KBS) are not necessarily based on a well-defined ontologies. In particular it is possible to build very successful KBS for classification problems, but where the classes or conclusions are entered by experts as free-text sentences with little constraint on textual consistency and little systematic organisation of the conclusions. This paper investigates how relations between such ‘classes’ may be discovered from existing knowledge bases. We have based our approach on KBS built with Ripple Down Rules (RDR). RDR is a knowledge acquisition and knowledge maintenance method which allows KBS to be built very rapidly and simply by correcting errors, but does not require a strong ontology. Our experimental results are based on a large real-world medical RDR KBS. The motivation for our work is to allow an ontology in a KBS to ‘emerge’ during development, rather than requiring the ontology to be established prior to the development of the KBS. It follows earlier work on using Formal Concept Analysis (FCA) to discover ontologies in RDR KBS.KeywordsKnowledge AcquisitionConcept LatticeClass RelationFormal ContextFormal Concept AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A Knowledge-Based System for the risk analysis of proposed building projects was developed for a selected client. The Fuzzy Decision Variables (FDVs) that cause differences between initial and final contract sums of building projects were identified, the likelihood of the occurrence of the risks were determined and a Knowledge-Based System that would rank the risks was constructed using JAVA programming language and Graphic User Interface. The Knowledge-Based System is composed a Knowledge Base for storing data, an Inference Engine for controlling and directing the use of knowledge for problem-solution, and a User Interface that assists the user retrieve, use and alter data in the Knowledge Base. The developed Knowledge-Based System was compiled, implemented and validated with data of previously completed projects. The client could utilize the Knowledge-Based System to undertake proposed building projects

The implementation of security results in a user being restricted to a subset of a knowledge base. The information content of constraints may mean that a user is not permitted to access some constraints. But without access permission to all constraints, a user may make modifications which do not satisfy the integrity of other users in the knowledge base system. The author describes an approach which shows how users who have access to a subset of the knowledge base which does not satisfy all constraints can act to restore their subset to a state where the information satisfies all integrity constraints. The user may make modifications to restore integrity, may place information in a pending state, or may work with unaffected areas of the knowledge base. >

A prototype of knowledge-based decision support system for construction cost management

In more and more companies knowledge-based systems are being considered an adequate means for building systems to support office work in order to increase its quality and efficiency. Such systems need various kinds of knowledge, like knowledge about the terminology underlying the corresponding domain of discourse, and knowledge about what kinds of events and actions can occur in the office work and what changes they cause. As many office tasks underlie organizational regulations as well as federal law, the value of systems for supporting office work is drastically increased if they know about these regulations, thus becoming able to ensure their obedience, or to explain them to the user (this insight already motivated the early LEGOL project [18]). One of the key problems of building such systems is how to properly integrate the various kinds of knowledge: On the one hand, we need a resulting system with adequate efficiency, and on the other hand we want to keep the representation of the various knowledge sources completely independent of each other, mainly due to the following reasons: Having one big, monolithic knowledge base would make construction and maintenance of the knowledge base extremely difficult and error-prone, if not impossible at all. The various knowledge sources (esp. knowledge about law and organizational regulations) will typically be employed by more than one application system and should therefore be reusable. Thus, it is extremely desirable to represent the knowledge sources only once and just “plug” them in wherever they are needed. As a consequence, they must not be tailored to the needs of the specific application system, but the knowledge represented therein must be kept in a neutral form (without sacrificing efficiency). The issue of knowledge reuse and sharing we are addressing in this paper has been recognized an important topic in the area of knowledge-based systems [12]. Law and regulations should be represented isomorphically [1]. This means that there is a one-to-one mapping between the textual structure of the law and the structure of its formal representation. An isomorphic representation is a prerequisite to generate comprehensible explanations for a user and helps to maintain the knowledge base. A representation of the legal knowledge which is adapted to fit into the representation of the other kinds of knowledge clearly cannot be isomorphic.