Learning regular sets from queries and counterexamples
Information and Computation
Learning regular languages from counterexamples
Journal of Computer and System Sciences
Modern operating systems
Inference of finite automata using homing sequences
Information and Computation
An introduction to computational learning theory
An introduction to computational learning theory
Planning control rules for reactive agents
Artificial Intelligence
Automatic OBDD-based generation of universal plans in non-deterministic domains
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Extending Graphplan to handle uncertainty and sensing actions
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Learning Deterministic Finite Automata from Smallest Counterexamples
SIAM Journal on Discrete Mathematics
Alternating-time temporal logic
Journal of the ACM (JACM)
Planning for temporally extended goals
Annals of Mathematics and Artificial Intelligence
TALplanner: A temporal logic based forward chaining planner
Annals of Mathematics and Artificial Intelligence
Bounded Model Search in Linear Temporal Logic and Its Application to Planning
TABLEAUX '98 Proceedings of the International Conference on Automated Reasoning with Analytic Tableaux and Related Methods
Symbolic Controller Synthesis for Discrete and Timed Systems
Hybrid Systems II
MOCHA: Modularity in Model Checking
CAV '98 Proceedings of the 10th International Conference on Computer Aided Verification
NuSMV 2: An OpenSource Tool for Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Planning via Model Checking: A Decision Procedure for AR
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Automata-Theoretic Approach to Planning for Temporally Extended Goals
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Automatic symbolic compositional verification by learning assumptions
Formal Methods in System Design
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Conditional progressive planning under uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning as model checking for extended goals in non-deterministic domains
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Symbolic compositional verification by learning assumptions
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
Synthesis of a closed-loop combined plant and controller model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Control reconfiguration of discrete event systems controllers with partial observation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decentralized Learning in Markov Games
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Traditional planning assumes reachability goals and/or full observability. In this paper, we propose a novel solution for safety and reachability planning with partial observability. Given a planning domain, a safety property, and a reachability goal, we automatically learn a safe permissive plan to guide the planning domain so that the safety property is not violated and that can force the planning domain to eventually reach states that satisfy the reachability goal, regardless of how the planning domain behaves. Our technique is based on the active learning of regular languages and symbolic model checking. The planning method first learns a safe plan using the L* algorithm, which is an efficient active learning algorithm for regular languages. We then check whether the safe plan learned is also permissive by Alternating-time Temporal Logic (ATL) model checking. If the plan is permissive, it is indeed a safe permissive plan. Otherwise, we identify and add a safe string to converge a safe permissive plan. We describe an implementation of the proposed technique and demonstrate that our tool can efficiently construct safe permissive plans for four sets of examples.