Projects per year
In Automated Planning, learning and exploiting additional knowledge within a domain model, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising because they are to some extent domain model and planning engine independent. This paper aims to exploit recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate 'achievements' or 'requirements', for generating macro-operators.We discuss conditions which are necessary for generating such macro-operators and conditions that allow removing primitive operators without compromising solvability of a given (class of) problem(s). The effectiveness of our approach will be experimentally shown on a set of well-known benchmark domains using several highperforming planning engines.
|Title of host publication
|Proceedings of the 10th Symposium on Abstraction, Reformulation, and Approximation, SARA 2013
|Number of pages
|Published - 2013
|10th Symposium on Abstraction, Reformulation, and Approximation - Leavenworth, United States
Duration: 11 Jul 2013 → 12 Jul 2013
https://www.aaai.org/ocs/index.php/SARA/SARA13 (Link to Symposium Details )
|10th Symposium on Abstraction, Reformulation, and Approximation
|11/07/13 → 12/07/13
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- 1 Finished
Machine Learning and Adaptation of Domain Models to Support Real-Time Planning in Autonomous Systems
1/03/12 → 30/09/16