SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
Machine Learning
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Learning procedural knowledge through observation
Proceedings of the 1st international conference on Knowledge capture
Investigating Explanation-Based Learning
Investigating Explanation-Based Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Inducing Models of human Control Skills
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Integrating Experimentation and Guidance in Relational Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
A Framework for Behavioural Cloning
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Learning task-performance knowledge through observation
Learning task-performance knowledge through observation
AI characters and directors for interactive computer games
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Synthetic adversaries for urban combat training
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Using theory completion to learn a robot navigation control program
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Compact representation of knowledge bases in ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Adversarial search with procedural knowledge heuristic
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A goal- and dependency-directed algorithm for learning hierarchical task networks
Proceedings of the fifth international conference on Knowledge capture
Behavior bounding: an efficient method for high-level behavior comparison
Journal of Artificial Intelligence Research
Inductive logic programming algorithm for estimating quality of partial plans
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Event Model Learning from Complex Videos using ILP
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Inductive generalization of analytically learned goal hierarchies
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
ACM Transactions on Intelligent Systems and Technology (TIST)
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We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in a "supervised concept learning'' setting. The acquired procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules.