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Automated planning even in its simplest form, classical planning, is a computationally hard problem. With the increasing involvement of intelligent systems in everyday life there is a need for more and more advanced planning techniques able to solve planning problems in little (or real) time. However, planners designed to solve planning problems as fast as possible often provide solution plans of low quality. The quality of solution plans can be improved by their post-planning analysis by which redundant actions or optimizable sub plans can be identified. In this paper, we present techniques for determining redundancy of actions in plans. Especially, we present techniques for efficient redundancy checking of pairs of inverse actions. These techniques are accompanied with necessary theoretical foundations and are also empirically evaluated using existing planning systems and standard planning benchmarks.
|Title of host publication||Proceedings - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012|
|Number of pages||8|
|Publication status||Published - 2012|
|Event||IEEE 24th International Conference on Tools with Artificial Intelligence - Athens, Greece|
Duration: 7 Nov 2012 → 9 Nov 2012
Conference number: 24
|Conference||IEEE 24th International Conference on Tools with Artificial Intelligence|
|Abbreviated title||ICTAI 2012|
|Period||7/11/12 → 9/11/12|
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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