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Using Counter-Examples in Controller Synthesis for POMDPs
Frejlach, Jakub ; Síč, Juraj (oponent) ; Češka, Milan (vedoucí práce)
This thesis examines partially observable Markov decision processes (POMDPs), a prominent stochastic model for decision-making under uncertainty and partial observability. POMDPs have diverse applications, from robot navigation to self-driving vehicles. The undecidable control problem of POMDPs has led to various approaches, including finite-state controllers (FSCs) based on observations and history. Identifying small and verifiable FSCs reduces the synthesis of Markov chains. This thesis focuses on counterexample-guided inductive synthesis (CEGIS) within the PAYNT program, exploring the use of Markov decision processes as counterexamples. A new greedy method for constructing counterexamples is outlined and implemented in PAYNT, showing improvements in some cases compared to the existing method.

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