ANTONIO
GONZÁLEZ MUÑOZ
CATEDRÁTICO DE UNIVERSIDAD
LUIS
CASTILLO VIDAL
CATEDRÁTICO DE UNIVERSIDAD
Publikationen, an denen er mitarbeitet LUIS CASTILLO VIDAL (12)
2010
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A knowledge engineering methodology for rapid prototyping of planning applications
Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
2007
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Handling fuzzy temporal constraints in a planning environment
Annals of Operations Research, Vol. 155, Núm. 1, pp. 391-415
2005
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SIADEX: An interactive knowledge-based planner for decision support in forest fire fighting
AI Communications, Vol. 18, Núm. 4, pp. 257-268
2003
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Some issues about the representation and exploitation of imprecise temporal knowledge for an AI planner
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
2001
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Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm
Fuzzy Sets and Systems, Vol. 120, Núm. 2, pp. 309-321
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Mixing expressiveness and efficiency in a manufacturing planner
Journal of Experimental and Theoretical Artificial Intelligence, Vol. 13, Núm. 2, pp. 141-162
2000
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Automatic generation of control sequences for manufacturing systems based on partial order planning techniques
Artificial Intelligence in Engineering, Vol. 14, Núm. 1, pp. 15-30
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Intelligent planning of Grafcet charts
Robotics and Computer-Integrated Manufacturing, Vol. 16, Núm. 4, pp. 225-239
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Three-level knowledge-based system for the generation of live and safe Petri nets for manufacturing systems
Journal of Intelligent Manufacturing, Vol. 11, Núm. 6, pp. 559-572
1999
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Knowledge-based tool for the automated synthesis of Petri nets for manufacturing systems
IEEE Symposium on Emerging Technologies and Factory Automation, ETFA
1998
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A nonlinear planner for solving sequential control problems in manufacturing systems
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Distribution network optimization: Finding the most economic solution by using genetic algorithms
European Journal of Operational Research, Vol. 108, Núm. 3, pp. 527-537