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Strategy Complexity of Point Payoff, Mean Payoff and Total Payoff Objectives in Countable MDPs

Richard Mayr ; Eric Munday.

We study countably infinite Markov decision processes (MDPs) with real-valued transition rewards. Every infinite run induces the following sequences of payoffs: 1. Point payoff (the sequence of directly seen transition rewards), 2. Mean payoff (the sequence of the sums of all rewards so far, divided&nbsp;[&hellip;]

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Model Checking Probabilistic Pushdown Automata

Javier Esparza ; Antonin Kucera ; Richard Mayr.

We consider the model checking problem for probabilistic pushdown automata (pPDA) and properties expressible in various probabilistic logics. We start with properties that can be formulated as instances of a generalized random walk problem. We prove that both qualitative and quantitative model&nbsp;[&hellip;]

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Stochastic Parity Games on Lossy Channel Systems

Parosh Aziz Abdulla ; Lorenzo Clemente ; Richard Mayr ; Sven Sandberg.

We give an algorithm for solving stochastic parity games with almost-sure winning conditions on {\it lossy channel systems}, under the constraint that both players are restricted to finite-memory strategies. First, we describe a general framework, where we consider the class of 2 1/2-player games&nbsp;[&hellip;]

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Model Checking Flat Freeze LTL on One-Counter Automata

Antonia Lechner ; Richard Mayr ; Joël Ouaknine ; Amaury Pouly ; James Worrell.

Freeze LTL is a temporal logic with registers that is suitable for specifying properties of data words. In this paper we study the model checking problem for Freeze LTL on one-counter automata. This problem is known to be undecidable in general and PSPACE-complete for the special case of&nbsp;[&hellip;]

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