We propose a new model for formalizing reward collection problems on graphs with dynamically generated rewards which may appear and disappear based on a stochastic model. The robot routing problem is modeled as a graph whose nodes are stochastic processes generating potential rewards over discrete time. The rewards are generated according to the stochastic process, but at each step, an existing reward disappears with a given probability. The edges in the graph encode the (unit-distance) paths between the rewards' locations. On visiting a node, the robot collects the accumulated reward at the node at that time, but traveling between the nodes takes time. The optimization question asks to compute an optimal (or epsilon-optimal) path that maximizes the expected collected rewards. We consider the finite and infinite-horizon robot routing problems. For finite-horizon, the goal is to maximize the total expected reward, while for infinite horizon we consider limit-average objectives. We study the computational and strategy complexity of these problems, establish NP-lower bounds and show that optimal strategies require memory in general. We also provide an algorithm for computing epsilon-optimal infinite paths for arbitrary epsilon > 0.
@InProceedings{dimitrova_et_al:LIPIcs.CONCUR.2017.13, author = {Dimitrova, Rayna and Gavran, Ivan and Majumdar, Rupak and Prabhu, Vinayak S. and Soudjani, Sadegh Esmaeil Zadeh}, title = {{The Robot Routing Problem for Collecting Aggregate Stochastic Rewards}}, booktitle = {28th International Conference on Concurrency Theory (CONCUR 2017)}, pages = {13:1--13:17}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-048-4}, ISSN = {1868-8969}, year = {2017}, volume = {85}, editor = {Meyer, Roland and Nestmann, Uwe}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2017.13}, URN = {urn:nbn:de:0030-drops-77920}, doi = {10.4230/LIPIcs.CONCUR.2017.13}, annote = {Keywords: Path Planning, Graph Games, Quantitative Objectives, Discounting} }
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