Abstract
Markov Decision Processes (MDPs) model problems where a decision-maker makes sequential decisions and the effect of decisions is probabilistic. A particular formulation of MDPs is the Shortest Stochastic Path (SSP), in which the agent seeks to accomplish a goal while reducing the cost of the path to it. Literature introduces some optimality criteria; most of them consider a priority of maximizing probability to accomplish the goal while minimizing some cost measure; such criteria allow a unique trade-off between probability-to-goal and path cost for a decision-maker. Here, we present algorithms to make a trade-off between probability-to-goal and expected cost; based on the Minimum Cost given Maximum Probability (MCMP) criterion, we propose to treat such a trade-off under three different methods: (i) additional constraints for probability-to-goal or expected cost; (ii) a Pareto’s optimality by finding non-dominated policies; and (iii) an efficient preference elicitation process based on non-dominated policies. We report experiments on a toy problem, where probability-to-goal and expected cost trade-off can be observed.
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Kuo, I., Freire, V. (2021). Probability-to-Goal and Expected Cost Trade-Off in Stochastic Shortest Path. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_9
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DOI: https://doi.org/10.1007/978-3-030-86970-0_9
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