SHOP2: an HTN planning system
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The SHOP2 planning system as discussed by the authors received one of the awards for distinguished performance in the 2002 International Planning Competition and described the features that enabled it to excel in the competition, especially those aspects of SHOP 2 that deal with temporal and metric planning domains.Abstract:
The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.read more
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DissertationDOI
Hierarchical landmarks - a means to reduce search effort in hybrid planning
TL;DR: A novel pre-processing technique to extract knowledge from a hierarchically structured planning domain and a current planning problem description which is used to significantly improve planning performance and novel domain independent strategies relying on the knowledge that is generated by pre- processing in order to guide the hierarchical planning processes more effectively towards a solution of a given planning problem.
Book ChapterDOI
Collaborative Human-Agent Planning for Resilience
TL;DR: In this article , the authors investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent's domain model.
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Collaborative Human-Agent Planning for Resilience.
TL;DR: In this article, the authors investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent's domain model.
Journal ArticleDOI
A development cycle for automated self-exploration of robot behaviors
Thomas M. Roehr,Daniel Harnack,Hendrik Wöhrle,Felix Wiebe,Moritz Schilling,Oscar Lima,Malte Langosz,Shivesh Kumar,Sirko Straube,Frank Kirchner +9 more
TL;DR: The Q-Rock development cycle as mentioned in this paper combines several machine learning and reasoning techniques to deal with the increasing complexity in the design of robotic systems, including self-exploration and qualification of robot behaviors.
Hierarchical methods for optimal long-term planning
Stuart Russell,Jason Wolfe +1 more
TL;DR: This thesis addresses the problem of generating goal-directed plans involving very many elementary actions, and introduces an "angelic" semantics for high-level actions that enables such inferences, and demonstrates how angelic bounds can be used to speed up search.
References
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Proceedings Article
HTN planning: complexity and expressivity
TL;DR: How the complexity of HTN planning varies with various conditions on the task networks is described.
Proceedings Article
Generating project networks
TL;DR: The planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain are described, which can aid in the generation of project networks.
Journal ArticleDOI
Using temporal logics to express search control knowledge for planning
Fahiem Bacchus,Froduald Kabanza +1 more
TL;DR: This work shows how domain dependent search control knowledge can be represented in a temporal logic, and then utilized to effectively control a forward-chaining planner.
Book
Practical Planning: Extending the Classical AI Planning Paradigm
TL;DR: In this paper, the authors present a hierarchical planning as a hierarchy of different abstraction levels for SIPE and compare it with other systems with different resources: Reusable, Consumable, Temporal, Search, and Reactivity.
Proceedings Article
The nonlinear nature of plans
TL;DR: A new information structure is described, called the procedural net, that represents a plan as a partial ordering of actions with respec to time, so that a problem-solving system using this representation can deal easily and directly with problems that are otherwise very difficult to solve.