Control Representation in an EDA Assistant

Robert St. Amant, Paul R. Cohen
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:502-512, 1995.

Abstract

While research in statistics and artificial intelligence has addressed issues in the automation of later stages of analysis, such as theory generation, model selection, and experiment design [23], less attention has been given to initial exploration of data. We have developed a novel approach to exploration as search. This paper gives an overview of the design of AIDE, the Assistant for Intelligent Data Exploration, which assists humans in the early stages of data analysis [1]. The system adopts a script-based planning approach to automating EDA. Data-directed mechanisms extract simple observations and suggestive indications from the data. Scripted EDA operations are then applied in goal-directed fashion to generate deeper descriptions of the data. Control rules guide the EDA operations, relying on intermediate results for their decisions. The system is mixed-initiative, capable of autonomously pursuing high and low level goals while still allowing the user to guide or override its decisions. AIDE is currently a prototype under development. We emphasize that the work presented here is incomplete.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-amant95a, title = {Control Representation in an {EDA} Assistant}, author = {Amant, Robert St. and Cohen, Paul R.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {502--512}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/amant95a/amant95a.pdf}, url = {https://proceedings.mlr.press/r0/amant95a.html}, abstract = { While research in statistics and artificial intelligence has addressed issues in the automation of later stages of analysis, such as theory generation, model selection, and experiment design [23], less attention has been given to initial exploration of data. We have developed a novel approach to exploration as search. This paper gives an overview of the design of AIDE, the Assistant for Intelligent Data Exploration, which assists humans in the early stages of data analysis [1]. The system adopts a script-based planning approach to automating EDA. Data-directed mechanisms extract simple observations and suggestive indications from the data. Scripted EDA operations are then applied in goal-directed fashion to generate deeper descriptions of the data. Control rules guide the EDA operations, relying on intermediate results for their decisions. The system is mixed-initiative, capable of autonomously pursuing high and low level goals while still allowing the user to guide or override its decisions. AIDE is currently a prototype under development. We emphasize that the work presented here is incomplete.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Control Representation in an EDA Assistant %A Robert St. Amant %A Paul R. Cohen %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-amant95a %I PMLR %P 502--512 %U https://proceedings.mlr.press/r0/amant95a.html %V R0 %X While research in statistics and artificial intelligence has addressed issues in the automation of later stages of analysis, such as theory generation, model selection, and experiment design [23], less attention has been given to initial exploration of data. We have developed a novel approach to exploration as search. This paper gives an overview of the design of AIDE, the Assistant for Intelligent Data Exploration, which assists humans in the early stages of data analysis [1]. The system adopts a script-based planning approach to automating EDA. Data-directed mechanisms extract simple observations and suggestive indications from the data. Scripted EDA operations are then applied in goal-directed fashion to generate deeper descriptions of the data. Control rules guide the EDA operations, relying on intermediate results for their decisions. The system is mixed-initiative, capable of autonomously pursuing high and low level goals while still allowing the user to guide or override its decisions. AIDE is currently a prototype under development. We emphasize that the work presented here is incomplete. %Z Reissued by PMLR on 01 May 2022.
APA
Amant, R.S. & Cohen, P.R.. (1995). Control Representation in an EDA Assistant. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:502-512 Available from https://proceedings.mlr.press/r0/amant95a.html. Reissued by PMLR on 01 May 2022.

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