Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Tableau-based model checking in the propositional mu-calculus
Acta Informatica
Artificial Intelligence
Efficient generation of counterexamples and witnesses in symbolic model checking
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Model checking
Modal logic
Tree-Like Counterexamples in Model Checking
LICS '02 Proceedings of the 17th Annual IEEE Symposium on Logic in Computer Science
Model Checking Knowledge and Time
Proceedings of the 9th International SPIN Workshop on Model Checking of Software
Concurrent dynamic epistemic logic for MAS
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
MCMAS: A Model Checker for the Verification of Multi-Agent Systems
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
A solution to the GHI problem for depth-first proof-number search
Information Sciences: an International Journal
What went wrong: explaining counterexamples
SPIN'03 Proceedings of the 10th international conference on Model checking software
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Most modal logics such as S5, LTL, or ATL are extensions of Modal Logic K. While the model checking problems for LTL and to a lesser extent ATL have been very active research areas for the past decades, the model checking problem for the more basic MMLK has important applications as a formal framework for perfect information multi-player games on its own. We present MPS, an effort number based algorithm solving the model checking problem for MMLK. We prove two important properties for MPS beyond its correctness. The (dis)proof exhibited by MPS is of minimal cost for a general definition of cost, and MPS is an optimal algorithm for finding (dis)proofs of minimal cost. Optimality means that any comparable algorithm either needs to explore a bigger or equal state space than MPS, or is not guaranteed to find a (dis)proof of minimal cost on every input. As such, our work relates to A* and AO* in heuristic search, to Proof Number Search and DFPN+ in two-player games, and to counterexample minimization in software model checking.