Data-driven approaches to empirical discovery

Langley, Pat; Żytkow, Jan M.

doi:10.1016/0004-3702(89)90051-9

Cited by 62 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are two basic methods for discovering functional relations from data: 2. Machine learning methods such as data-driven discovery (Langley and Zytkow, 1989;Langley et al, 1987).…”

Section: Generalmentioning

confidence: 99%

KOSI — An integrated system for discovering functional relations from databases

Zhong

Ohsuga

1995

J Intell Inf Syst

View full text Add to dashboard Cite

Abstract. This paper describes an integrated system called KOSI (Knowledge Oriented Statistic Inference) for discovering functional relations from databases. Tile key feature of KOSI is that AI techniques and statistical methods are cooperatively used in the discovery process based on incipient hypothesis generation and evaluation, in which multi-search is performed in meta control. Two types of search, which use respectively different type of heuristics, are used in attribute calculation which is a kind of operation in which new attribute is generated as a function of the existing attributes. Third type of search, which is based on regression analysis, is mainly used for evaluating/selecting the best functional relation from the results of attribute calculation. Furthermore, a model-base and metaldomain knowledge are used for controlling the multi-search, and the methods of forming scopes/clusters can be used as a step of pre-processing before the search. KOSI is organized into multi-level structure for knowledge discovery and management efficiently. We try to provide a systematic manner of discovering functional relations, to support qualitative/quantitative discovery, and develop a more robust, general-purpose discovery system.

show abstract

“…There are two basic methods for discovering functional relations from data: 2. Machine learning methods such as data-driven discovery (Langley and Zytkow, 1989;Langley et al, 1987).…”

Section: Generalmentioning

confidence: 99%

KOSI — An integrated system for discovering functional relations from databases

Zhong

Ohsuga

1995

J Intell Inf Syst

View full text Add to dashboard Cite

show abstract

“…FAHRENHEIT (Zytkow, 1987;Langley & Zytkow, 1989;Zytkow & Zhu, 1991) incorporates a problem-choosing facility that seeks to resolve uncertainty about range of applicability. After the program has produced a law, using its BACON-like module, its scope search facility is invoked to generate experiences outside the range of those previously considered in an attempt to find the boundaries of the region in which the law applies.…”

Section: The Need For a Curiosity Heuristicmentioning

confidence: 99%

Experience selection and problem choice in an exploratory learning system

Scott

Markovitch

1993

Mach Learn

View full text Add to dashboard Cite

Abstract.A fully autonomous exploratory learning system must perform two tasks that are not required of supervised learning systems: experience selection and problem choice. Experience selection is the process of choosing informative training examples from the space of all possible examples. Problem choice is the process of identifying defects in the domain theory and determining which should be remedied next. These processes are closely related because the degree to which a specific experience is informative depends on the particular defects in the domain theory that the system is attempting to remedy. In this article we propose a general control structure for exploratory learning in which problem choice by an information-theoretic "curiosity" heuristic: the problem chosen then guides the selection of training examples. An implementation of an exploratory learning system based on this control structure is described, and a series of experimental results are presented.

show abstract

“…An important problem area in virtually every area of science is finding the empirical relationship underlying observed values of the variables measuring a system (Langley and Zytkow 1989). In practice, the observed data may be noisy and there may be no known way to express the relationships involved in a precise way.…”

Section: Rediscovering the "Exchange Equation" From Empirical Time Sementioning

confidence: 99%

“…An important problem in economics and other areas of science is finding the mathematical relationship between the empirically observed variables measuring a system (Langley and Zytkow 1989). In many conventional modeling techniques, one necessarily begins by selecting the size and shape of the mathematical model.…”

Section: Introduction and Overviewmentioning

confidence: 99%