A formal theory of plan recognition and its implementation
Reasoning about plans
Automatically generating abstractions for planning
Artificial Intelligence
Semantics for hierarchical task-network planning
Semantics for hierarchical task-network planning
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Part-whole relations in object-centered systems: an overview
Data & Knowledge Engineering - Special issue on modeling parts and wholes
Understanding, building and using ontologies
International Journal of Human-Computer Studies
Control strategies in HTN planning: theory versus practice
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
A temporal description logic for reasoning about actions and plans
Journal of Artificial Intelligence Research
A formal theory for describing action concepts in terminological knowledge bases
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Active objects: actions for entity-centric search
Proceedings of the 21st international conference on World Wide Web
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Useful and suitable action representations, with accompanying planning algorithms are crucial for the task performance of many agent systems, and thus a core issue of research on intelligent agents. An efficient and expressive representation of actions and plans can allow planning systems to retrieve relevant knowledge faster and to access and use suitable actions more effectively [18]. Two general approaches have been pursued in the past; STRIPS-based planners, which construct plans from scratch, based on primitive action descriptions and planners using pre-defined Plan Decompositions Hierarchies, also known as Hierarchical Task Networks. In our research, we integrated both an inheritance hierarchy of actions, using STRIPS-like action descriptions, with a plan decomposition hierarchy, which consists of pre-defined plan schemata. This combination is suitable for a richer action and plan representation, and thus an improved planning algorithm. We implemented and tested this approach for a prototypical example application: the travel planning domain.