Inductive Learning Methodology Research Articles

사용자 행동 패턴 선호도 학습을 위한 퍼지 귀납 학습 시스템

본 논문은 스마트 홈과 같이 다양한 센서 및 제어 네트워크가 밀집되어 있는 유비쿼터스 환경 하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)을 줄이고, 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 새로운 사용자 행동 패턴 선호도 학습기법을 제안하였다. 이를 위해 지식 발견(knowledge discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지귀납(fuzzy inductive) 학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성 있는(consistent) 퍼지 상관 롤(fuzzy association rule)을 얻어내도록 한다. Smart home is one of the ubiquitous environment platforms with various complex sensor-and-control network. In this paper, a now learning methodology for learning user's behavior preference pattern is proposed in the sense of reductive user's cognitive load to access complex interfaces and providing personalized services. We propose a fuzzy inductive learning methodology based on life-long learning paradigm for knowledge discovery, which tries to construct efficient fuzzy partition for each input space and to extract fuzzy association rules from the numerical data pattern.

Abstract Correction of First-Order Functional Programs

DEBUSSY is an (abstract) declarative diagnosis tool for functional programs which are written in OBJ style. The tool does not require the user to either provide error symptoms in advance or answer any question concerning program correctness. In this paper, we formalize an inductive learning methodology for repairing program bugs in OBJ-like programs, which is based on the so-called example-guided unfolding[6]. Correct programs are synthesized by unfolding and removing rules of the faulty program. Rules to be unfolded (deleted) are selected according to the examples, which can be automatically generated as an outcome by the DEBUSSY diagnoser.

Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach

This paper develops an adaptive scheduling policy for dynamic manufacturing systems. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. The inductive learning methodology used for constructing this state-dependent scheduling policy also provides an understanding of the relative importance of the various system parameters in determining the appropriate dispatching rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tight due date jobs and machine breakdowns.

A knowledge-intensive genetic algorithm for supervised learning

Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The full-memory approach developed here uses the same high-level descriptive language that is used in rule-based systems. This allows for an easy utilization of inference rules of the well-known inductive learning methodology, which replace the traditional domain-independent operators and make the search task-specific. Moreover, a closer relationship between the underlying task and the processing mechanisms provides a setting for an application of more powerful task-specific heuristics. Initial results obtained with a prototype implementation for the simplest case of single concepts indicate that genetic algorithms can be effectively used to process high-level concepts and incorporate task-specific knowledge. The method of abstracting the genetic algorithm to the problem level, described here for the supervised inductive learning, can be also extended to other domains and tasks, since it provides a framework for combining recently popular genetic algorithm methods with traditional problem-solving methodologies. Moreover, in this particular case, it provides a very powerful tool enabling study of the widely accepted but not so well understood inductive learning methodology.

A theory and methodology of inductive learning

The presented theory views inductive learning as a heuristic search through a space of symbolic descriptions, generated by an application of various inference rules to the initial observational statements. The inference rules include generalization rules, which perform generalizing transformations on descriptions, and conventional truth-preserving deductive rules. The application of the inference rules to descriptions is constrained by problem background knowledge, and guided by criteria evaluating the “quality” of generated inductive assertions.

A theory and methodology of inductive learning

A theory of inductive learning is presented that characterizes it as a heuristic search through a space of symbolic descriptions, generated by an application of certain inference rules to the initial observational statements (the teacher-provided examples of some concepts, or facts about a class of objects or a phenomenon). The inference rules include generalization rules, which perform generalizing transformations on descriptions, and conventional truth-preserving deductive rules (specialization and reformulation rules). The application of the inference rules to descriptions is constrained by problem background knowledge, and guided by criteria evaluating the ‘quality’ of generated inductive assertions. Based on this theory, a general methodology for learning structural descriptions from examples, called star, is described and illustrated by a problem from the area of conceptual data analysis.