How much memory is needed to win infinite games?
LICS '97 Proceedings of the 12th Annual IEEE Symposium on Logic in Computer Science
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Quantitative multi-objective verification for probabilistic systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Energy games in multiweighted automata
ICTAC'11 Proceedings of the 8th international conference on Theoretical aspects of computing
Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes
LICS '11 Proceedings of the 2011 IEEE 26th Annual Symposium on Logic in Computer Science
Markov decision processes with multiple objectives
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Strategy synthesis for multi-dimensional quantitative objectives
CONCUR'12 Proceedings of the 23rd international conference on Concurrency Theory
Playing stochastic games precisely
CONCUR'12 Proceedings of the 23rd international conference on Concurrency Theory
PRISM-games: a model checker for stochastic multi-player games
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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We study strategy synthesis for stochastic two-player games with multiple objectives expressed as a conjunction of LTL and expected total reward goals. For stopping games, the strategies are constructed from the Pareto frontiers that we compute via value iteration. Since, in general, infinite memory is required for deterministic winning strategies in such games, our construction takes advantage of randomised memory updates in order to provide compact strategies. We implement our methods in PRISM-games, a model checker for stochastic multi-player games, and present a case study motivated by the DARPA Urban Challenge, illustrating how our methods can be used to synthesise strategies for high-level control of autonomous vehicles.