The complexity of probabilistic verification
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Automata logics, and infinite games: a guide to current research
Automata logics, and infinite games: a guide to current research
Planning Algorithms
Experiments with deterministic ω-automata for formulas of linear temporal logic
Theoretical Computer Science - Implementation and application of automata
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Dealing with Nondeterminism in Symbolic Control
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Automatic deployment of distributed teams of robots from temporal logic motion specifications
IEEE Transactions on Robotics
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Learning DFA from correction and equivalence queries
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Formal Approach to the Deployment of Distributed Robotic Teams
IEEE Transactions on Robotics
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We develop a technique to automatically generate a control policy for a robot moving in an environment that includes elements with unknown, randomly changing behavior. The robot is required to achieve a surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time inbetween consecutive services is minimized and additional temporal logic constraints are satisfied. We define a fragment of linear temporal logic to describe such a mission and formulate the problem as a temporal logic game. Our approach is based on two main ideas. First, we extend results in automata learning to detect patterns of the unknown behavior of the elements in the environment. Second, we employ an automata-theoretic method to generate the control policy. We show that the obtained control policy converges to an optimal one when the partially unknown behavior patterns are fully learned. In addition, we illustrate the method in an experimental setup, in which an unmanned ground vehicle, with the help of a cooperating unmanned aerial vehicle (UAV), satisfies a temporal logic requirement in a partitioned environment whose regions are controlled by barriers with unknown behavior.