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Arena-Independent Finite-Memory Determinacy in Stochastic Games
Second, we show that we can reduce the study of objectives for which pure AIFM strategies suffice in two-player stochastic games to the easier study of one-player stochastic games (i. e., Markov decision processes).
Decision Making
Computer Science and Game Theory
Formal Languages and Automata Theory
Logic in Computer Science
Games Where You Can Play Optimally with Arena-Independent Finite Memory
In 2005, Gimbert and Zielonka provided a complete characterization of preference relations (a formal framework to model specifications and game objectives) that admit memoryless optimal strategies for both players.
Computer Science and Game Theory Formal Languages and Automata Theory Logic in Computer Science
Simple Strategies in Multi-Objective MDPs (Technical Report)
That is, strategies that are pure (no randomization) and have bounded memory.
Life is Random, Time is Not: Markov Decision Processes with Window Objectives
The window mechanism was introduced by Chatterjee et al. to strengthen classical game objectives with time bounds.
Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes
We establish that, for all variants of this problem, deciding the existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity class as classical parity games.
