Domain-independent planning: representation and plan generation
Artificial Intelligence
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
gIBIS: a hypertext tool for exploratory policy discussion
ACM Transactions on Information Systems (TOIS)
SIBYL: A qualitative decision management system
Artificial intelligence at MIT expanding frontiers
O-Plan: the open planning architecture
Artificial Intelligence
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Knowledge-level analysis of planning systems
ACM SIGART Bulletin
Artificial Intelligence - Special volume on planning and scheduling
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Representing and Maintaining Process Knowledge for Large-Scale Systems Development
IEEE Expert: Intelligent Systems and Their Applications
The Trains 91 Dialogues
Roots of SPAR — Shared Planning and Activity Representation
The Knowledge Engineering Review
I-Ex: Intelligent Extreme Expedition Support
Proceedings of the 2006 conference on Rob Milne: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer
Coalition task support using I-X and
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
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Traditional approaches to plan representation have focused on the generation of a sequence of actions and orderings. Knowledge rich models, which incorporate plan rationale, provide benefits to the planning process in a number of ways. The use of rationale in planning is reviewed in terms of causality, dependencies, and decisions. Each dimension addresses practical issues in the planning process, and adds value to the resultant plan. The contribution of this paper is to explore this categorisation, and to motivate the need to explicitly record and represent rationale knowledge for situated, mixed-initiative planning systems.