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An Approach for Generalising Symbolic Knowledge
Abstract
Many researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. However, they
generally treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts,
which aids generalization. This paper presents a method that models hidden context within a symbolic domain achieving a level
of generalisation. Results indicate that the method can learn the information that experts have difficulty providing by generalising
the captured knowledge.
Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness in knowledge based system (KBS) development. PA is essentially an online validation approach where as each situation or case is presented to the KBS for inferencing the result is simultaneously validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. Previous studies have shown that a modification to multiple classification ripple-down rules (MCRDR) referred to as rated MCRDR (RM) has been able to achieve strong and flexible results in simulated domains with artificial data sets. This paper presents a study into the effectiveness of RM in an eHealth document monitoring and classification domain using human expertise. Additionally, this paper also investigates what affect PA has when the KBS developer relied entirely on the warnings for maintenance. Results indicate that the system is surprisingly robust even when warning accuracy is allowed to drop quite low. This study of a previously little touched area provides a strong indication of the potential for future knowledge based system development.
The aspirations and achievements of research and applications in knowledge-based systems are reviewed and placed in the context of the evolution of information technology, and our understanding of human expertise and knowledge processes. Future developments are seen as a continuation of a long-term process of operationalizing the rational stance to human knowledge processes adopted in the enlightenment, involving further diffusion of artificial intelligence technologies into mainstream computer applications, and incorporation of deeper models of human psychological and social processes.
Knowledge maintenance (KM) is the process of reflecting over some current knowledge based system in order to handle a new situation. Changes that fix new problems must not introduce bugs into old fix es. A common technique for KM is to reflect over dependencies between concepts. In this review, we sample the KM work on modeling dependencies using either a logic-based, a network-based, or a procedural-based approach. Submitted to the Encyclopediaof Computer Science and Technology
Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of
brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled
when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the
area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence
of a model for the process of practise in current approaches is a significant contributor to brittleness. This paper will
investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers
and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity,
neglecting many connectionists’ work in learning hidden and dynamic contexts. This paper argues that the omission of these
higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally,
these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step
towards a philosophy of knowledge that could lead to the modelling of the process of practice.
The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in constructing a knowledge-level model of their search processes.Furthermore, for the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-down rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process; thus the expert provides a knowledge-level model of his search process. We call this framework nested ripple-down rules.Our approach targets the implicit representation of the less clearly definable quality criteria by allowing the expert to limit his input to the system to explanations of the steps in the expert search process. These explanations are expressed in our search knowledge interactive language. These explanations are used to construct a knowledge base representing search control knowledge. We are acquiring the knowledge in the context of its use, which substantially supports the knowledge acquisition process. Thus, in this paper, we will show that it is possible to build effective search heuristics efficiently at the knowledge level. We will discuss how our system SmS1.3 (SmS for Smart Searcher) operates at the knowledge level as originally described by Newell. We complement our discussion by employing SmS for the acquisition of expert chess knowledge for performing a highly pruned tree search. These experimental results in the chess domain are evidence for the practicality of our approach.
Context is the challenge for the coming years in artificial intelligence (AI). In the companion paper [6], we present a view of how context is considered through the literature in various domains. In this paper, we present the main results of discussions at some workshops and the first conference focusing on the notion of context. We point out the opposition between two viewpoints on context, namely the engineering and the cognitive ones. We show that this opposition is only superficial because they do not consider context at the same level, one is at the level of the knowledge representation, the other at the level of the interaction between two agents.
. This paper suggests that a distinction between knowledge acquisition methods should be made. On the one hand there are methods which aim to help the expert and knowledge engineer analyse what knowledge is involved in solving a particular type of problem and how this problem solving is carried out. These methods are concerned with classifying the different types of problem solving and providing tools and methods to help the knowledge engineer identify the appropriate approach and ensure nothing is omitted.. A different approach to knowledge acquisition focuses on ensuring incremental addition of validated knowledge as mistakes are discovered (validated knowledge here means only that the earlier performance of the system is not degraded by the addition of new knowledge). The organisation of this knowledge is managed by the system rather than the expert and knowledge engineer. This would seem to correspond to human incremental development of expertise. From this perspective...
Situated cognition (SC) claims that knowledge is mostly context-dependent and that symbolic descriptions elicited prior to direct experience are less important than functional units developed via direct experience with the current problem. If this were true, then we would need to modify the knowledge modeling approaches of KA which assume that re-using old symbolic descriptions are a productivity tool for new applications. There are numerous tools which, if added to conventional knowledge modeling, could be said to handle SC (e.g. machine learning, abduction, verification & validation tools, repertory grids, certain frameworks for decision support systems, expert critiquing systems, and ripple-down-rules). However, we require an experiment to assess the effectiveness of these tools as a response to SC. 1 Introduction "What is wanted is not the will to believe, but the will to find out, which is the exact opposite." -- Bertrand Russell "Measure what is measurable, and make measurable w...
A knowledge acquisition methodology, Ripple Down Rules, has been developed which oly allows knowledge to be used in the context in which it is acquired and ensures that only valid rules can be added to a knowledge bae. This method has now been used to build a large (1600 rule) medical expert system. This system is in routine use in a Chemical Pathology laboraotry providig clicnical interpretations for laboratory reports. It has been developed entirely by experts with no knowledge acquisiton or programming support or skills. This task was a minor extension to their normal duites. This paper describes the resultant knowledge base and concludes that such knowledge acquisiton is very simple. It claims that allowing knowledge to be easily refined is a prowerful technique which greatly simplifies dealing with complex domains
Ripple Down Rules (RDR) is a knowledge acquisition method which constrains the interactions between the expert and a shell to acquire only correct knowledge. Although RDR works well, it is only suitable for the problem of providing a single classification for a set of data. Multiple Classification Ripple Down Rules (MCRDR) is an extension of RDR which allows multiple independent classifications. The approach has been evaluated in simulation studies where the human expert is replaced by a simulated expert. MCRDR may provide a basis for building a general problem solver for a range of problems beyond classification.
Knowledge engineering, obtaining knowledge from experts and incorporating it into expert systems is difficult and time consuming. We suggest that these difficulties arise because experts never report on how they reach a decision, rather they justify why the decision is correct. These justifications vary markedly with the context in which they are required, but in context they are accurate and adequate; the difficulties in knowledge engineering arise from taking the justification out of context. We therefore hypothesise that knowledge engineering may be obviated, particularly in the long term maintenance of expert systems, if the rules experts provide are used in the context in which they are given. This paper describes work in progress to test this hypothesis.
Situated cognition is not a mere philosophical concern: it has pragmatic implications for current practice in knowledge acquisition. Tools must move from being design-focused to being maintenance-focused. Reuse-based approaches (e.g. using problem-solving methods) will fail unless the reused descriptions can be extensively modified to suit the new situation. Knowledge engineers must model not only descriptions of expert knowledge, but also the environment in which a knowledge base will perform. Descriptions of knowledge must be constantly re-evaluated. This re-evaluation process has implications for assessing representations
A knowledge acquisition methodology, Ripple Down Rules, has been developed which only allows knowledge to be used in the context in which it is acquired and ensures that only valid rules can be added to a knowledge base. This method has now been used to build a large (2000 rule) medical expert system. This system is in routine use in a Chemical Pathology laboratory providing clinical interpretations for laboratory reports. It has been developed entirely by experts with no knowledge acquisition or programming support or skills. This task was a minor extension to their normal duties. This paper describes the resultant knowledge base and concludes that such knowledge acquisition is very simple. It claims that allowing knowledge to be easily refined is a powerful technique which greatly simplifies dealing with complex domains. INTRODUCTION TO RIPPLE DOWN RULES Ripple down rules (RDR) is a knowledge acquisition methodology and a way of structuring knowledge bases which grew o...
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