791 research outputs found
Symmetry Breaking for Answer Set Programming
In the context of answer set programming, this work investigates symmetry
detection and symmetry breaking to eliminate symmetric parts of the search
space and, thereby, simplify the solution process. We contribute a reduction of
symmetry detection to a graph automorphism problem which allows to extract
symmetries of a logic program from the symmetries of the constructed coloured
graph. We also propose an encoding of symmetry-breaking constraints in terms of
permutation cycles and use only generators in this process which implicitly
represent symmetries and always with exponential compression. These ideas are
formulated as preprocessing and implemented in a completely automated flow that
first detects symmetries from a given answer set program, adds
symmetry-breaking constraints, and can be applied to any existing answer set
solver. We demonstrate computational impact on benchmarks versus direct
application of the solver.
Furthermore, we explore symmetry breaking for answer set programming in two
domains: first, constraint answer set programming as a novel approach to
represent and solve constraint satisfaction problems, and second, distributed
nonmonotonic multi-context systems. In particular, we formulate a
translation-based approach to constraint answer set solving which allows for
the application of our symmetry detection and symmetry breaking methods. To
compare their performance with a-priori symmetry breaking techniques, we also
contribute a decomposition of the global value precedence constraint that
enforces domain consistency on the original constraint via the unit-propagation
of an answer set solver. We evaluate both options in an empirical analysis. In
the context of distributed nonmonotonic multi-context system, we develop an
algorithm for distributed symmetry detection and also carry over
symmetry-breaking constraints for distributed answer set programming.Comment: Diploma thesis. Vienna University of Technology, August 201
Decompositions of Grammar Constraints
A wide range of constraints can be compactly specified using automata or
formal languages. In a sequence of recent papers, we have shown that an
effective means to reason with such specifications is to decompose them into
primitive constraints. We can then, for instance, use state of the art SAT
solvers and profit from their advanced features like fast unit propagation,
clause learning, and conflict-based search heuristics. This approach holds
promise for solving combinatorial problems in scheduling, rostering, and
configuration, as well as problems in more diverse areas like bioinformatics,
software testing and natural language processing. In addition, decomposition
may be an effective method to propagate other global constraints.Comment: Proceedings of the Twenty-Third AAAI Conference on Artificial
Intelligenc
A Complete Solver for Constraint Games
Game Theory studies situations in which multiple agents having conflicting
objectives have to reach a collective decision. The question of a compact
representation language for agents utility function is of crucial importance
since the classical representation of a -players game is given by a
-dimensional matrix of exponential size for each player. In this paper we
use the framework of Constraint Games in which CSP are used to represent
utilities. Constraint Programming --including global constraints-- allows to
easily give a compact and elegant model to many useful games. Constraint Games
come in two flavors: Constraint Satisfaction Games and Constraint Optimization
Games, the first one using satisfaction to define boolean utilities. In
addition to multimatrix games, it is also possible to model more complex games
where hard constraints forbid certain situations. In this paper we study
complete search techniques and show that our solver using the compact
representation of Constraint Games is faster than the classical game solver
Gambit by one to two orders of magnitude.Comment: 17 page
A Mining-Based Compression Approach for Constraint Satisfaction Problems
In this paper, we propose an extension of our Mining for SAT framework to
Constraint satisfaction Problem (CSP). We consider n-ary extensional
constraints (table constraints). Our approach aims to reduce the size of the
CSP by exploiting the structure of the constraints graph and of its associated
microstructure. More precisely, we apply itemset mining techniques to search
for closed frequent itemsets on these two representation. Using Tseitin
extension, we rewrite the whole CSP to another compressed CSP equivalent with
respect to satisfiability. Our approach contrast with previous proposed
approach by Katsirelos and Walsh, as we do not change the structure of the
constraints.Comment: arXiv admin note: substantial text overlap with arXiv:1304.441
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