Abstract
This article studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The goal is to generate a defender policy to maximize satisfaction of a given temporal logic specification under any adversary policy. The search for policies is limited to the space of finite-state controllers, which leads to a tractable approach to determine policies. We relate the satisfaction of the specification to reaching (a subset of) recurrent states of a Markov chain. We present an algorithm to determine a set of defender and adversary finite-state controllers of fixed sizes that will satisfy the temporal logic specification and prove that it is sound. We then propose a value-iteration algorithm to maximize the probability of satisfying the temporal logic specification under finite-state controllers of fixed sizes. Finally, we extend this setting to the scenario where the size of the finite-state controller of the defender can be increased to improve the satisfaction probability. We illustrate our approach with an example.
| Original language | English |
|---|---|
| Pages (from-to) | 5665-5679 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 66 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2021 |
Keywords
- Finite-state controllers (FSCs)
- Global Markov chain (GMC)
- Linear temporal logic (LTL)
- Partially observable stochastic games (POSGs)
- Policy iteration
- Stackelberg equilibrium
- Value iteration
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