EXPLANATION-BASED NATURAL LANGUAGE ACQUISITION USING UNIVERSAL LINGUISTIC PRINCIPLES AS INNATE DOMAIN THEORY

In this paper, we propose a new natural language acquisition model (called EBNLA) based on explanation-based language ( EBL). To apply EBL to the natural language acquisition domain, suitable universal linguistic principles are incorporated as domain theory. The domain theory consists of two parts: static and dynamic. The static part, which is assumed to he invariant and innate to the model, includes theta theory in government-binding theory and universal fea ture instantiation principles in generalized phrase structure grammar. The dynamic part con tains context-free grammar rules as well as syntactic and thematic features of lexicons. In parsing ( problem solving), both parts work together to parse input sentences. As parsing fails, learning is triggered to enrich and generalize the dynamic part by obeying the principles in the static part. By introducing EBL and the universal linguistic principles, portability of the model and leamabitity of knowledge in the real-world natural language acquisition domain can be improved.     

Learning expert systems by being corrected

This paper describes a new method of knowledge acquisition for expert systems. A program, KABCO, interacts with a domain expert and learns how to make examples of a concept. This is done by displaying examples based upon KABCO's partial knowledge of the domain and accepting corrections from the expert. When the expert judges that KABCO has learnt the domain completely a large number of examples are generated and given to a standard machine learning program that learns the actual expert system rules. KABCO vastly eases the task of constructing an expert system using machine learning programs because it allows expert system rule bases to be learnt from a mixture of general (rules) and specific (examples) information. At present KABCO can only be used for classification domains but work is proceedings to extend it to be useful for other domains. KABCO learns disjunctive concepts (represented by frames) by modifying an internal knowledge base to remain consistent with all the corrections that have been entered by the expert. KABCO's incremental learning uses the deductive processes of modification, exclusion, subsumption and generalization. The present implementation is primitive, especially the user interface, but work is proceeding to make KABCO a much more advanced knowledge engineering tool.     

Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem

One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is themultiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, calledabductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory.     

Knowledge acquisition method for conceptual design based on value engineering and axiomatic design theory

A method for acquiring conceptual design knowledge in physical systems is proposed and implemented based on EBL (explanation-based learning), ‘value engineering methodologies’ and ‘axiomatic design approaches’. In this method, the structural features of designed objects are analysed to yield a systematic explanation of how they function and attain their design goals and why they are used for attaining the goals.The ‘how’ explanation results in a generalized version of the functional diagram used in value engineering from which various levels of general design knowledge can be extracted. The quality of the extracted knowledge is then discussed with reference to its mode of acquisition.The ‘why’ explanation yields a deeper understanding of the designed objects from which we can extract meta-planning or strategic knowledge for selecting rational plans from among other possible alternatives. This explanation is obtained by regarding the object in question as being the result of strategically rational decisions and actions which are subject to the ‘design axioms’.     

解释学习进展

本文对解释学习近期的研究进行了论述,包括解释学习机制,当前解释学习研究的几个重要问题:如可操作性的形式化定义、典型解释学习过程的解释与概括、针对不完善领域理论的多种解决办法等,还就解释学习特点、总体发展状况及今后研究方向提出了看法。     

An interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.     

THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION

This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.     

一种用于建造多塔精馏过程故障诊断专家系统知识库的方法

提出了一种用于建造多塔精馏过程故障诊断专家系统知识库的方法，该方法借助于实例，通过基于解释学习的学习模型及定量深层知识库来产生故障诊断领域知识，若用该方法建立专家系统知识库，则相应的专家系统在进行蒸馏系统故障诊断时比较快速、准确。     

Explanation-based learning:A problem solving perspective

This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the problem solving system. 's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. 's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.     

Explanation-Based Learning: An Alternative View

In the last issue of this journal Mitchell, Keller, and Kedar-Cabelli presented a unifying framework for the explanation-based approach to machine learning. While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata, and learning from observation. This paper outlines six specific problems with the previously proposed framework and presents an alternative generalization method to perform explanation-based learning of new concepts.     

Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition

This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.     

Automated knowledge acquisition for the surface-mount PWB assembly domain

The development of knowledge-based (or expert) systems for the surface-mount printed wiring board (PWB) assembly domain requires the understanding and regulation of several complex tasks. While the knowledge base in an expert system serves as a storehouse of knowledge primitives, its design and development is a bottleneck in the expert system development life-cycle. Therefore the development of an automated knowledge acquisition (KA) facility (or KA tool) would facilitate the implementation of expert systems for any domain. This paper describes an automated KA tool that helps to elicit and store information in domain-specific knowledge bases for surface-mount PWB assembly. A salient feature of this research is the acquisition of uncertain information.     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

Book Review: Exemplar-Based Knowledge Acquisition,by Ray Bareiss. Academic Press, 1989

Summary Exemplar-Based Knowledge Acquisition is an excellent reference document describing a promising knowledge acquisition tool, the Protos system. Since it is publicly available, Protos provides a testbed for a variety of techniques and issues in machine learning: (1) the integration of similarity-based and explanation-based approaches, (2) the transition from user guidance to autonomy, and (3) the relation between knowledge representation and efficiency. Much work remains to be done to assess Protos' scaleability and to uncover and repair any possible existing complexity problems.     

A new method for explanation-based learning

In this paper we discuss using the stratified ATMS to realize explanation-based learning. As the stratified ATMS can record and maintain the reasonings for beliefs efficiently and can deal with non-monotonic reasoning, so the ATMS-based EBL system can improve the efficiency of explanation-based learning, deal with multiple explanation problems in learning from imperfect theories by prioritized reasoning and multiple example verification and can give biases for induction in integrated learning.     

装备故障诊断专家系统知识获取方法

针对导弹武器装备故障诊断专家系统的知识获取与机器学习进行研究，系统介绍了知识获取与机器学习系统的构成。提出了一种基于仿真技术的故障知识获取方式，仿真数据经过分析、变换，转化为知识，从而实现知识获取。     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

A Critical Look at Experimental Evaluations of EBL

A number of experimental evaluations ofexplanation-based learning (EBL) have been reported in the literature on machine learning. A close examination of the design of these experiments revelas certain methodological problems that could affect the conclusions drawn from the experiments. This article analyzes some of the more common methodological difficulties, and illustrates them using selected previous studies.     

基于解释的学习系统:IFLS

介绍了一个基于解释的学习系统IFLS的结构和采用的技术,克服了传统的解释学习要求学习程序拥有完善的领域知识以及没有考虑所学知识的效用性问题。