Inducing hierarchical process models in dynamic domains

Authors:
Ljupčo Todorovski;Will Bridewell;Oren Shiran;Pat Langley
Affiliations:
Computational Learning Laboratory, CSLI, Stanford University, Stanford and Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia;Computational Learning Laboratory, CSLI, Stanford University, Stanford;Computational Learning Laboratory, CSLI, Stanford University, Stanford;Computational Learning Laboratory, CSLI, Stanford University, Stanford
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Year:
2005

Citing 7
Cited 7

Qualitative process theory

Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Compositional modeling: finding the right model for the job

Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Algorithm 717: Subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models

ACM Transactions on Mathematical Software (TOMS)
Reasoning about nonlinear system identification

Artificial Intelligence
Explanation-Based Learning: An Alternative View

Machine Learning
A Divide and Conquer Approach to Learning from Prior Knowledge

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A learning algorithm for continually running fully recurrent neural networks

Neural Computation

Reducing overfitting in process model induction

ICML '05 Proceedings of the 22nd international conference on Machine learning
Extracting constraints for process modeling

Proceedings of the 4th international conference on Knowledge capture
Constructing explanatory process models from biological data and knowledge

Artificial Intelligence in Medicine
Learning declarative bias

ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Equation discovery for model identification in respiratory mechanics of the mechanically ventilated human lung

DS'10 Proceedings of the 13th international conference on Discovery science
Learning process models with missing data

ECML'06 Proceedings of the 17th European conference on Machine Learning
Development of a knowledge library for automated watershed modeling

Environmental Modelling & Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

Research on inductive process modeling combines background knowledge with time-series data to construct explanatory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchical manner, and we describe HIPM, a system that carries out constrained search for hierarchical process models. We report experiments that suggest this approach produces more accurate and plausible models with less effort. We conclude by discussing related research and directions for future work.