Detecting and Correcting Errors of Omission After Explanation-Based Learning
نویسنده
چکیده
In this paper, we address an issue that arises when the background knowledge used by explanationbased learning is incorrect. In particular, we consider the problems that can be caused by a domain theory that may be overly specific. Under this condition, generalizations formed by explanation-based learning will make errors of omission when they are relied upon to make predictions or explanations. We describe a technique for detecting errors of omission, assigning blame for the error of omission to an inference rule in the domain theory, and revising the domain theory to accommodate new examples.
منابع مشابه
Detecting and Correcting Learner Korean Particle Omission Errors
We detect errors in Korean post-positional particle usage, focusing on optimizing omission detection, as omissions are the single-biggest factor in particle errors for learners of Korean. We also develop a system for predicting the correct choice of a particle. For omission detection, we model the task largely on English grammatical error detection, but employ Korean-specific features and filte...
متن کاملAn approach to fault detection and correction in design of systems using of Turbo codes
We present an approach to design of fault tolerant computing systems. In this paper, a technique is employed that enable the combination of several codes, in order to obtain flexibility in the design of error correcting codes. Code combining techniques are very effective, which one of these codes are turbo codes. The Algorithm-based fault tolerance techniques that to detect errors rely on the c...
متن کاملDetecting 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 concept...
متن کاملDesign and implementation of Persian spelling detection and correction system based on Semantic
Persian Language has a special feature (grapheme, homophone, and multi-shape clinging characters) in electronic devices. Furthermore, design and implementation of NLP tools for Persian are more challenging than other languages (e.g. English or German). Spelling tools are used widely for editing user texts like emails and text in editors. Also developing Persian tools will provide Persian progr...
متن کاملPazzani and C. Brunk. Detecting and Correcting Errors in Rule-based Expert Systems: an Integration of Empirical and Explanation-based Learning. in Proceedings of the 5th Knowledge Acquisition for Knowledge-based Systems Workshop
program plan [ Johnson, 1986 ] , or existing correct program [ Murray, 1988 ] . By comparison, PDS6 and Forte are dynamic. They run a program on speci c examples, detect errors, and use them to revise the program. 2 Consequently, dynamic methods require only partial, extensional de nitions of programs. This is an important advantage since formal speci cations are frequently unavailable. Systems...
متن کامل