A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Automatically generating abstractions for planning
Artificial Intelligence
Introspective multistrategy learning: on the construction of learning strategies
Artificial Intelligence
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Learning k-Reversible Context-Free Grammars from Positive Structural Examples
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Interacting Learning-Goals: Treating Learning as a Planning Task
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Bringing Semantics to Web Services with OWL-S
World Wide Web
Recognizing plan/goal abandonment
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Relational macros for transfer in reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Learning teleoreactive logic programs from problem solving
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Proceedings of the 14th international conference on Intelligent user interfaces
From geek to sleek: integrating task learning tools to support end users in real-world applications
Proceedings of the 14th international conference on Intelligent user interfaces
User-created forms as an effective method of human-agent communication
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
POIROT: acquiring workflows by combining models learned from interpreted traces
Proceedings of the fifth international conference on Knowledge capture
Lowering the barriers to website testing with CoTester
Proceedings of the 15th international conference on Intelligent user interfaces
Learning to ask the right questions to help a learner learn
Proceedings of the 16th international conference on Intelligent user interfaces
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
ACM Transactions on Intelligent Systems and Technology (TIST)
Detecting common scientific workflow fragments using templates and execution provenance
Proceedings of the seventh international conference on Knowledge capture
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POIROT is an integration framework for combining machine learning mechanisms to learn hierarchical models of web services procedures from a single or very small set of demonstration examples. The system is organized around a shared representation language for communications with a central hypothesis blackboard. Component learning systems share semantic representations of their hypotheses (generalizations) and inferences about demonstration traces. To further the process, components may generate learning goals for other learning components. POIROT's learners or hypothesis formers develop workflows that include order dependencies, subgoals, and decision criteria for selecting or prioritizing subtasks and service parameters. Hypothesis evaluators, guided by POIROT's meta-control component, plan experiments to confirm or disconfirm hypotheses extracted from these learning products. Collectively, they create methods that POIROT can use to reproduce the demonstration and solve similar problems. After its first phase of development, POIROT has demonstrated it can learn some moderately complex hierarchical task models from semantic traces of user-generated service transaction sequences at a level that is approaching human performance on the same learning task.