Symbolic Verification and Strategy Synthesis for Turn-Based Stochastic Games

Kwiatkowska, Marta; Norman, Gethin; Parker, David; Santos, Gabriel

doi:10.1007/978-3-031-22337-2_19

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…In this regard, the most relevant work to us is [36] where an SDP algorithm is proposed for value iteration in FO-POMDP, nevertheless, states of FO-POMDP there only contain FOL formulas while ours include modality and program expressions as well, leading to special treatments in computing preimage and evaluating for case statements. Prior to [36], symbolic model-checking can be applied to transition systems [5], Markov chains [4], MDP [3], first-order MDP [35], or relational POMDP [41], stochastic game [24].…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Verifying Belief-Based Programs via Symbolic Dynamic Programming

Liu,

Huang,

Belle

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Belief-based programming is a probabilistic extension of the Golog programming language family, where every action and sensing could be noisy and every test refers to the subjective beliefs of the agent. Such characteristics make it rather suitable for robot control in a partial-observable uncertain environment. Recently, efforts have been made in providing formal semantics for belief programs and investigating the hardness of verifying belief programs. Nevertheless, a general algorithm that actually conducts the verification is missing. In this paper, we propose an algorithm based on symbolic dynamic programming to verify belief programs, an approach that generalizes the dynamic programming technique for solving (partially observable) Markov decision processes, i.e. (PO)MDP, by exploiting the symbolic structure in the solution of first-order (PO)MDPs induced by belief program execution.

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