Percentile Queries in Multi-Dimensional Markov Decision Processes
Mickaël Randour, Jean-François Raskin, Ocan Sankur · arXiv (Cornell University) · 2014
Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with multiple objectives that may be conflicting and require the analysis of trade-offs. We study the complexity of percentile queries in such MDPs and give algorithms to synthesize strategies that enforce such constraints. Given a multi-dimensional weighted MDP and a quantitative payoff function $f$, thresholds $v_i$ (one per dimension), and probability thresholds $α_i$, we show how to compute a single strategy to enforce that for all dimensions $i$, the probability of outcomes $ρ$ satisfying $f_i(ρ) \geq v_i$ is at least $α_i$. We consider classical quantitative payoffs from the literature (sup, inf, lim sup, lim inf, mean-payoff, truncated sum, discounted sum). Our work extends to the quantitative case the multi-objective model checking problem studied by Etessami et al. in unweighted MDPs.