Incremental acquisition of search knowledge

doi:10.1006/ijhc.1999.0338

International Journal of Human-Computer Studies

Volume 52, Issue 3, March 2000, Pages 493-530

https://doi.org/10.1006/ijhc.1999.0338 Get rights and content

Abstract

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.

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This list contains attributes satisfied by the case which triggered the addition of r, and it excludes all attributes satisfied by any of the cases covered by the parent of r. In Beydoun and Hoffmann (2000) presented an approach to incrementally capture search knowledge in general based on collecting expert’s justifications for their decisions. In this paper, we apply this approach to the tolerancing problem in mechanical design proposing a system architecture that is outlined below.
We propose to use a knowledge based approach to assist in mechanical design focusing on dimensional tolerancing. To illustrate our approach, we capture the knowledge which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards. The software system we developed would help mechanical designers become more effective in the time-consuming dimensioning and tolerancing process of their designs in the future. In doing this, the paper makes a twofold contribution to the field of knowledge acquisition: firstly, interface was adjusted to receive mathematical functions with their specifications prior and during the KA process to propose an approach to exploit relationships among several classes with respect to certain numerical features of the cases in order to accelerate the convergence of the RDR knowledge acquisition process by generating artificial cases which are likely to trigger the addition of exception rules. Secondly, it introduces the above problem domain of determining suitable tolerances for mechanical parts in a design as a knowledge acquisition problem.
An incremental knowledge acquisition-based system for supporting decisions in biomedical domains
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In Intensive Care Units doctors have to manage several alarm situations in patients. When a doctor analyzes the state of the patient, (s)he has to decide if there is an alarm situation and make decisions about what actions to perform. It is desirable to detect these situations before they occur, because the solution could be easier and the doctor has more time to react. An intelligent system could analyze the information, extract conclusions, format and order the causes leading to the severe condition. This would be helpful for a doctor, and would make the decision-making process easier. A system capable of performing such operations is presented here. This is not a diagnosis application but a tool to detect alarm situations for patient safety. A prototype capable of making retrospective evaluation of the condition of the patients has been developed. This system is based on the MCRDR technology, which has been extended to deal with the requirements of this domain. The evaluation of the system is also reported in this paper.
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It was initially validated by the development of PEIRS (Edwards, Compton, Malor, Srinivasan, & Lazarus, 1993), a large medical expert system for the interpretation of chemical pathology reports. Other studies have been published in different environments since then, including control applications (Shiraz & Sammut, 1997), heuristic search (Beydoun & Hoffmann, 2000), documental management (Kang, Yoshida, Motoda, & Compton, 1997) or image processing (Park, Cao, Jin, & Wilson, 2003). All of these studies prove that it is an efficient and simple knowledge acquisition methodology (Cao, 2006).
In some real life situations, humans have to make decisions in critical environments. When a human analyzes a situation, (s)he has to decide whether there is a risky situation and what actions have to be performed. It is always desirable to detect these situations in advance, because the solution could be easier and the expert would have more time to make the best decision. An intelligent system may analyze the information, extract conclusions, format and order the causes leading to the severe condition, so becoming the decision-making process less dramatic. Multiple Classification Ripple Down Rules (MCRDR) are a successful intelligent classification technique which has proven its efficiency in several application domains, but it has some limitations to define complex situations. In this work, an extension to MCRDR to cover with complex domains is proposed. The validation of this methodological extension has been done through the development of a prototype for complex medical domains and this is also presented in this paper.
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We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient—many diets are not specifically tailored for the client’s needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system’s competency during routine use of the system. The improvement of the system takes place through a direct expert user–system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet ‘manually’, i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.
Theoretical basis for hierarchical incrementai knowledge acquisition
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Human experts tend to introduce intermediate terms in giving their explanations. The expert's explanation of such terms is operational for the context that triggered the explanation; however, term definitions remain often incomplete. Further, the expert's (re) use of these terms is hierarchical (similar to natural language). In this paper, we argue that a hierarchical incremental knowledge acquisition (KA) process that captures the expert terms and operationalizes them while incompletely defined makes the KA task more effective. Towards this we present our knowledge representation formalism Nested Ripple Down Rules (NRDR) that is a substantial extension to the (Multiple Classification) Ripple Down Rule (RDR) KA framework. The incremental KA process with NRDR as the underlying knowledge representation has confirmation holistic features. This allows simultaneous incremental modelling and KA and eases the knowledge base (KB) development process.
Our NRDR formalism preserves the strength of incremental refinement methods, that is the ease of maintenance of the KB. It also addresses some of their shortcomings: repetition, lack of explicit modelling and readability. KBs developed with NRDR describe an explicit model of the domain. This greatly enhances the reuseability of the acquired knowledge.
This paper also presents a theoretical framework for analysing the structure of RDR in general and NRDR in particular. Using this framework, we analyse the conditions under which RDR converges towards the target KB. We discuss the maintenance problems of NRDR as a function of this convergence. Further, we analyse the conditions under which NRDR offers an effective approach for domain modelling. We show that the maintenance of NRDR requires similar effort to maintaining RDR for most of the KB development cycle. We show that when an NRDR KB shows an increase in maintenance requirement in comparison with RDR during its development, this added requirement can be automatically handled using stored past seen cases.

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Regular ArticleIncremental acquisition of search knowledge

Abstract

Cognitive Science

Artificial Intelligence in Medicine

Artificial Intelligence

Artificial Intelligence

Artificial Intelligence

A mediating representation to assist knowledge acquisition with MACAO

European Knowledge Acquisition Workshop

Simultaneous modelling and knowledge acquisition using NRDR

The 5th Pacific Rim Conference on Artificial Intelligence

Rulebased Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project

Generic tasks in knowledge-based reasoning: high level building blocks for expert system design

IEEE Expert

Introduction to Artificial Intelligence

The knowledge level reinterpreted: modeling how systems interact

Machine Learning

Ripple down rules: possibilities and limitations

The 6th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop

A philosophical basis for knowledge acquisition

Knowledge Acquisition

Local patching produces compact knowledge bases

The European Knowledge Acquisition Workshop

Knowledge acquisition for explainable, multi-expert, knowledge-based system design

The European Knowledge Acquisition Workshop

What Computers Still Can't Do: A Critique for Artificial Reason

Modularity of the Mind

Induction of ripple down rules

5th Australian Conference on Artificial Intelligence

Artificial Intelligence in Design

Formal Design Methods for CAD

Regular Article
Incremental acquisition of search knowledge