Learning Partially Observable Action Models: Efficient Algorithms Dafna Shahaf Allen Chang Eyal Amir Computer Science Department University of Illinois, Urbana-Champaign Urbana, IL 61801, USA {dshahaf2,achang6,eyal}@uiuc

First Page		Document Content
Date: 2010-08-28 23:41:19 Theoretical computer science Mathematical logic Mathematics Logic in computer science Boolean algebra Logic programming Automated planning and scheduling Formal methods Situation calculus Planning Domain Definition Language Boolean satisfiability problem Conjunctive normal form		Learning Partially Observable Action Models: Efficient Algorithms Dafna Shahaf Allen Chang Eyal Amir Computer Science Department University of Illinois, Urbana-Champaign Urbana, IL 61801, USA {dshahaf2,achang6,eyal}@uiuc Add to Reading List Source URL: www.cs.huji.ac.il Download Document from Source Website File Size: 479,09 KB Share Document on Facebook

	Automated Planning and Scheduling ΕΟ Constellations’ Operations with Ant Colony Optimization Evridiki Vasileia Ntagiou Surrey Space Centre, University of Surrey Abstract DocID: 1xToV - View Document
	27th International Conference on Automated Planning and Scheduling June 19-23, 2017, Pittsburgh, USA KEPS 2017 DocID: 1uq7E - View Document
	International Conference on Automated Planning and Scheduling (ICAPSTask and Motion Policy Synthesis as Liveness Games Yue Wang and Neil T. Dantam and Swarat Chaudhuri and Lydia E. Kavraki Department of Computer DocID: 1rgIf - View Document
	From FOND to Probabilistic Planning: Guiding search for quality policies Alberto Camacho† , Christian Muise∗ , Akshay Ganeshen† , Sheila A. McIlraith† † Department of Computer Science, University of Toronto Dep DocID: 1rboJ - View Document
	Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling Goal-Based Action Priors David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O’Farrell, James Mac DocID: 1ra2S - View Document