ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Local and temporal predicates in distributed systems
ACM Transactions on Programming Languages and Systems (TOPLAS)
Locomotion with unit-modular reconfigurable robot
Locomotion with unit-modular reconfigurable robot
Parameterizing above guaranteed values: MaxSat and MaxCut
Journal of Algorithms
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Partial Implicit Unfolding in the Davis-Putnam Procedure for Quantified Boolean Formulae
LPAR '01 Proceedings of the Artificial Intelligence on Logic for Programming
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Numeric State Variables in Constraint-Based Planning
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Applying the Davis-Putnam Procedure to Non-clausal Formulas
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Exploiting hierarchy and structure to efficiently solve graph coloring as SAT
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Reconfiguration of Cube-Style Modular Robots Using O(logn) Parallel Moves
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
Compact Representation of Sets of Binary Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Generation of hard non-clausal random satisfiability problems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
SAT-encodings, search space structure, and local search performance
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Planning for temporally extended goals as propositional satisfiability
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Solving non-Boolean satisfiability problems with stochastic local search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A stochastic non-CNF SAT solver
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Planning as satisfiability with relaxed ∃-step plans
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Heuristics for planning with SAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Algorithms for Fast Concurrent Reconfiguration of Hexagonal Metamorphic Robots
IEEE Transactions on Robotics
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Composed of multiple modular robotic units, self-reconfigurable modular robots are metamorphic systems that can autonomously rearrange the modules and form different configurations depending on dynamic environments and tasks. The goal of self-reconfiguration is to determine how to change connectivity of modules to transform the robot from the current configuration to the goal configuration subject to restrictions of physical implementation. The existing reconfiguration algorithms use different methods, such as divide-and-conquer, graph matching, and the like, to reduce the reconfiguration cost. However, an optimal solution with a minimal number of reconfiguration steps has not been found yet. The optimal reconfiguration planning problem consists in finding the least number of reconfiguration steps transforming the robot from one configuration to another. This is an NP-complete problem. In this paper, we describe an approach to solve this problem. The approach is based on constructing logical models of the problem under study.