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Efficient Representations and Conversions of Planning Problems
Toropila, Daniel
Title E cient Representations and Conversions of Planning Problems Author Daniel Toropila Department Department of Theoretical Computer Science and Mathematical Logic Supervisor prof. RNDr. Roman Barták, Ph.D. Abstract The e ciency of all types of planning systems is strongly dependent on the in- put formulation, the structure of which must be exploited in order to provide an improved e ciency. Hence, the state-variable representation (SAS+ ) has be- come the input of choice for many modern planners. As majority of planning problems is encoded using a classical representation, several techniques for trans- lation into SAS+ have been developed in the past. These techniques, however, ignore the instance-specific information of planning problems. Therefore, we in- troduce a novel algorithm for constructing SAS+ that fully utilizes the information from the goal and the initial state. By performing an exhaustive experimental evaluation we demonstrate that for many planning problems the novel approach generates a more e cient encoding, providing thus an improved solving time. Finally, we present an overview and performance evaluation of several constraint models based on SAS+ and finite-state automata, showing that they represent a competitive alternative in the category of constraint-based planners. Keywords...
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Properties of delta-matroids
Šíma, Lucien ; Kazda, Alexandr (advisor) ; Rolínek, Michal (referee)
We investigate delta-matroids which are formed by families of subsets of a finite ground set such that the exchange axiom is satisfied. We deal with some natural classes of delta-matroids. The main result of this thesis establishes sev- eral relations between even, linear, and matching-realizable delta-matroids. Fol- lowing up on the ideas due to Geelena, Iwatab, and Murota [2003], and apply- ing the properties of field extensions from algebra, we prove that the class of strictly matching-realizable delta-matroids, the subclass of matching-realizable delta-matroids, is included in the class of linear delta-matroids. We also show that not every linear delta-matroid is matching-realizable by giving a skew-symmetric matrix representation to the non matching-realizable delta-matroid constructed by Kazda, Kolmogorov, and Rol'ınek [2019].
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Efficient Representations and Conversions of Planning Problems
Toropila, Daniel ; Barták, Roman (advisor) ; McCluskey, Thomas Leo (referee) ; Pěchouček, Michal (referee)
Title E cient Representations and Conversions of Planning Problems Author Daniel Toropila Department Department of Theoretical Computer Science and Mathematical Logic Supervisor prof. RNDr. Roman Barták, Ph.D. Abstract The e ciency of all types of planning systems is strongly dependent on the in- put formulation, the structure of which must be exploited in order to provide an improved e ciency. Hence, the state-variable representation (SAS+ ) has be- come the input of choice for many modern planners. As majority of planning problems is encoded using a classical representation, several techniques for trans- lation into SAS+ have been developed in the past. These techniques, however, ignore the instance-specific information of planning problems. Therefore, we in- troduce a novel algorithm for constructing SAS+ that fully utilizes the information from the goal and the initial state. By performing an exhaustive experimental evaluation we demonstrate that for many planning problems the novel approach generates a more e cient encoding, providing thus an improved solving time. Finally, we present an overview and performance evaluation of several constraint models based on SAS+ and finite-state automata, showing that they represent a competitive alternative in the category of constraint-based planners. Keywords...
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Efficient Representations and Conversions of Planning Problems
Toropila, Daniel
Title E cient Representations and Conversions of Planning Problems Author Daniel Toropila Department Department of Theoretical Computer Science and Mathematical Logic Supervisor prof. RNDr. Roman Barták, Ph.D. Abstract The e ciency of all types of planning systems is strongly dependent on the in- put formulation, the structure of which must be exploited in order to provide an improved e ciency. Hence, the state-variable representation (SAS+ ) has be- come the input of choice for many modern planners. As majority of planning problems is encoded using a classical representation, several techniques for trans- lation into SAS+ have been developed in the past. These techniques, however, ignore the instance-specific information of planning problems. Therefore, we in- troduce a novel algorithm for constructing SAS+ that fully utilizes the information from the goal and the initial state. By performing an exhaustive experimental evaluation we demonstrate that for many planning problems the novel approach generates a more e cient encoding, providing thus an improved solving time. Finally, we present an overview and performance evaluation of several constraint models based on SAS+ and finite-state automata, showing that they represent a competitive alternative in the category of constraint-based planners. Keywords...
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