Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Experimental results on the application of satisfiability algorithms to scheduling problems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Heuristic Risk Assessment Using Cost Factors
IEEE Software
IEEE Software
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Empirical Investigation of Multiple Viewpoint Reasoning in Requirements Engineering
RE '99 Proceedings of the 4th IEEE International Symposium on Requirements Engineering
Converging on the Optimal Attainment of Requirements
RE '02 Proceedings of the 10th Anniversary IEEE Joint International Conference on Requirements Engineering
Combining the Best Attributes of Qualitative and Quantitative Risk Management Tool Support
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
Practical Large Scale What-if Queries: Case Studies with Software Risk Assessment
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
Testing Nondeterminate Systems
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Data Mining for Very Busy People
Computer
Backbone fragility and the local search cost peak
Journal of Artificial Intelligence Research
Model decomposition and simulation: a component based qualitative simulation algorithm
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Optimizing requirements decisions with keys
Proceedings of the 4th international workshop on Predictor models in software engineering
Symbolic execution with mixed concrete-symbolic solving
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Learning patterns of university student retention
Expert Systems with Applications: An International Journal
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An over-zealous machine learner can automatically generate large, intricate, theories which can be hard to understand. However, such intricate learning is not necessary in domains that lack complex relationships. A much simpler learner can suffice in domains with narrow funnels; i.e. where most domain variables are controlled by a very small subset. Such a learner is TAR2: a weighted-class minimal contrast-set association rule learner that utilizes confidence-based pruning, but not support-based pruning. TAR2 learns treatments; i.e. constraints that can change an agent's environment. Treatments take two forms. Controller treatments hold the smallest number of conjunctions that most improve the current state of the system. Monitor treatments hold the smallest number of conjunctions that best detect future faulty system behavior. Such treatments tell an agent what to do (apply the controller) and what to watch for (the monitor conditions) within the current environment. Because TAR2 generates very small theories, our experience has been that users prefer its tiny treatments. The success of such a simple learner suggests that many domains lack complex relationships.