Taxonomic syntax for first order inference
Journal of the ACM (JACM)
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning action strategies for planning domains
Artificial Intelligence
Model checking
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Machine Learning
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Programming by demonstration: a machine learning approach
Programming by demonstration: a machine learning approach
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning and joint deliberation through argumentation in multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
Search-based structured prediction
Machine Learning
MABLE: a framework for learning from natural instruction
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Gradient boosting for sequence alignment
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
HTN-MAKER: learning HTNs with minimal additional knowledge engineering required
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
POIROT: integrated learning of web service procedures
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Responsibility and blame: a structural-model approach
Journal of Artificial Intelligence Research
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Strategies for learning search control rules: an explanation-based approach
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Discriminative learning of beam-search heuristics for planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Goal-driven learning in the GILA integrated intelligence architecture
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Inductive policy selection for first-order MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
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We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.