Explanation-based Learning Methods Research Articles

A Knowledge-Based System for Sharing and Reusing Tacit Knowledge in Robotic Manufacturing

A critical problem in robotic manufacturing is that the task of teaching robotics is rather time-consuming. This has become a serious problem in the present age of cost reduction. Collaboration with a company in the field has revealed that the root cause of this problem is that there is not a common knowledge base in this domain, which can serve as shared and reused knowledge. In robotic manufacturing, the skills and experiences of skilled workers are a form of tacit knowledge that is difficult to be acquired and transferred to other workers and robots. This paper proposes a knowledge-based system for sharing and reusing tacit knowledge in the robotic assembly domain. In this system, a modified EBL (Explanation-based Learning) method is proposed to generalize tacit knowledge from specific robotic programs made by skilled workers. A newly operational criterion is proposed for the generalized tacit knowledge, which demands that it should be expressed understandably by human workers and be reusable by robots to generate programs automatically.

Explanation-Based Learning and Reinforcement Learning: A Unified View

In speedup-learning problems, where full descriptions of operators are known, both explanation-based learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state. Most RL methods perform this propagation on a state-by-state basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a region-by-region basis. Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming. Based on this observation, this paper shows how to develop dynamic programming versions of EBL, which we call region-based dynamic programming or Explanation-Based Reinforcement Learning (EBRL). The paper compares batch and online versions of EBRL to batch and online versions of point-based dynamic programming and to standard EBL. The results show that region-based dynamic programming combines the strengths of EBL (fast learning and the ability to scale to large state spaces) with the strengths of reinforcement learning algorithms (learning of optimal policies). Results are shown in chess endgames and in synthetic maze tasks.

Explanation-based learning: A knowledge level analysis

This paper presents an overview of explanation-based learning (EBL) where the descriptions of EBL methods are realized at the knowledge level. Knowledge level description is a proposal that emphasizes the knowledge content and usage and abstracts away implementation details. Analyzing six different EBL methods at the knowledge level it can be shown what is their shared structure and illuminates their differences in a common framework of description. To describe EBL we use the Components of Expertise (Steels 1991), a knowledge level methodology currently used to describe, analyze, and support the development of expert systems.

Integrating ILP and EBL

This paper presents a review of recent work that integrates methods from Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL). ILP and EBL methods have complementary strengths and weaknesses and a number of recent projects have effectively combined them into systems with better performance than either of the individual approaches. In particular, integrated systems have been developed for guiding induction with prior knowledge (ML-Smart, FOCL, GRENDEL) refining imperfect domain theories (FORTE, AUDREY, Rx), and learning effective search-control knowledge (AxA-EBL, DOLPHIN).

Knowledge acquisition from an incomplete domain theory ? An application on the Stock Market

This paper describes an application of a machine learning technique, the Induction Over Unexplained (IOU) approach, which combines Explanation Based Learning (EBL) and Similarity Based Learning (SBL) methods to acquire concept descriptions from a set of training examples. IOU is designed for efficient learning with incomplete domain theory and partially explainable features of training examples. In this paper, the IOU approach is used to generate rules that can later on predict the future performance of stocks. There are many factors that can affect the price of stocks. Some of them have a direct relationship with the change of a company's stock price, and can be easily explained and modelled using the EBL method, while others may have only an indirect relationship for which the SBL method is preferable. In other words, a good rule which can accurately predict the future performance of stocks should include both explainable and unexplainable features. Finally, the performance of the learning results of the IOU approach was compared with that of a similarity based learning system and two other statistical methods. The results showed that the IOU method outperforms all the other approaches especially when the number of training examples is small.

Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn relational concepts in spite of an incorrect domain theory (e.g. a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that utilizes a trace of FOCL to suggest revisions to a rule base.

説明に基づく学習手法を用いた事例からの操作的設計知識獲得

説明に基づく学習手法を用いた事例からの操作的設計知識獲得

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 prodigy problem solving system. prodigy'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. prodigy's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.