Planning in hybrid systems has been gaining research interest in the Artificial Intelligence community in recent years. Hybrid systems allow for a more accurate representation of real world problems, though solving them is very challenging due to complex system dynamics and a large model feature set. We developed DiNo, a new planner designed to tackle problems set in hybrid domains. DiNo is based on the discretise and validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic.

Piotrowski, W., Fox, M., Long, D., Magazzeni, D., Mercorio, F. (2016). Heuristic planning for hybrid systems. In Proceedings of 30th AAAI Conference on Artificial Intelligence, AAAI 2016; Phoenix Convention CenterPhoenix; United States; 12-17 February 2016 (pp.4254-4255). AAAI press.

Heuristic planning for hybrid systems

Mercorio, F.
2016

Abstract

Planning in hybrid systems has been gaining research interest in the Artificial Intelligence community in recent years. Hybrid systems allow for a more accurate representation of real world problems, though solving them is very challenging due to complex system dynamics and a large model feature set. We developed DiNo, a new planner designed to tackle problems set in hybrid domains. DiNo is based on the discretise and validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic.
paper
Automated Planning; PDDL+; Planning as Model Checking; Planning in Mixed Discrete/Continuous Domains
English
Conference on Artificial Intelligence, AAAI
2016
Proceedings of 30th AAAI Conference on Artificial Intelligence, AAAI 2016; Phoenix Convention CenterPhoenix; United States; 12-17 February 2016
9781577357605
2016
4254
4255
http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12394
open
Piotrowski, W., Fox, M., Long, D., Magazzeni, D., Mercorio, F. (2016). Heuristic planning for hybrid systems. In Proceedings of 30th AAAI Conference on Artificial Intelligence, AAAI 2016; Phoenix Convention CenterPhoenix; United States; 12-17 February 2016 (pp.4254-4255). AAAI press.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/111148
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