. 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...

. 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 introspective access to that knowledge, their explanations of actual search considerations seems very valuable in constructing a knowledge level model of their search processes. The incremental method was inspired by the work on Ripple-Down Rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. We substantially extend Ripple Down Rules to allow undefined terms in the conditions. These undefined terms in turn become defined by Ripple Down Rules. The resulting framework is called Nested Ripple Down Rules. Our system SmS1.2 (SmS for Smart Searcher), has been employed for the acquisition of expert chess knowledge for performing a highly pruned tree search. Our first experimental results in the chess domain are ev...

The contribution of this paper is three-fold: It substantially extends Ripple Down Rules, a proven effective method for building large knowledge bases without a knowledge engineer. Furthermore, we propose to develop highly effective heuristics searchers for combinatorial problems by a knowledge acquisition approach to acquire human search knowledge. Finally, our initial experimental results suggest, that this approach may allow experts to stepwise articulate their introspectively inaccessible knowledge. 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 introspective access to that knowledge, their explanations of actual search considerations seems very valuable in constructing a knowledge level model of their search skills. Furthermore, for the basis of our knowledge acquisition approach, we substantial...

This paper proposes a new approach to the design of intelligent systems. A new framework is used in which the specification of the actual system is sought to be an interactive process taking place directly between the knowledge acquisition system and the domain expert. The need for a mediating knowledge engineer is reduced to an initial domain modelling stage. Furthermore, the interactions necessary between the expert and the KA system take place at a level that is as intuitive as possible to the expert. The emphasis of the types of systems we are addressing are KBS where search is an integral part of the problem solving process. Clearly defined combinatorial optimisation problems mark one end of the spectrum. The other end of the spectrum is marked by reducing the search process to just a single step, i.e. this covers also classification tasks. Intermediate problems being addressed are problems whose solution is probably best found by substantial search, however, where the criteria fo...

A major problem with Knowledge Based Systems (KBS) is that they do not know when they have reached the limits of their knowledge and so are likely to make stupid conclusions. One possible approach to this is have the KBS keep some sort of record of every type of case it has seen and issue a warning when it is dealing with a case outside its range of experience. In these studies we have used the simple technique of issuing a warning if any attribute has a value outside the range of those seen previously for cases satisfying the same sequence of rules during inference. This technique has been evaluated using standard machine learning data bases and a simulated expert (built by machine learning) rather than a human expert. The conclusions from this study are that single attribute warnings cannot be guaranteed to pick up all potential errors, but go a long way towards a knowledge acquisition methodology whereby a KBS will fairly reliably prompt that it needs more knowledge INT...

. 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. Our approach targets at the implicit representation of the less clearly definable quality criteria by allowing the expert to limit their input to the system to explanations of the steps in the expert search process. These explanations are expressed in our Search Knowledge Interactive Language (SKIL). The explanations are used to construct a knowledge base representing search control knowledge. 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, which integrates the knowledge elicitation and knowledge acquisition in one incremental process and allows the expe...