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<titleInfo><title>Optimal cost almost-sure reachability in POMDPs</title></titleInfo>

  
  
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  <title>Artifical Intelligence</title>
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<name type="personal">
  <namePart type="given">Krishnendu</namePart>
  <namePart type="family">Chatterjee</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">2E5DCA20-F248-11E8-B48F-1D18A9856A87</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-4561-241X</description></name>
<name type="personal">
  <namePart type="given">Martin</namePart>
  <namePart type="family">Chmelik</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">3624234E-F248-11E8-B48F-1D18A9856A87</identifier></name>
<name type="personal">
  <namePart type="given">Raghav</namePart>
  <namePart type="family">Gupta</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Ayush</namePart>
  <namePart type="family">Kanodia</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>







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<name type="conference">
  <namePart>IAAI: Innovative Applications of Artificial Intelligence</namePart>
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  <namePart>Modern Graph Algorithmic Techniques in Formal Verification</namePart>
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  <namePart>Rigorous Systems Engineering</namePart>
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  <namePart>Quantitative Graph Games: Theory and Applications</namePart>
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<abstract lang="eng">We consider partially observable Markov decision processes (POMDPs) with a set of target states and every transition is associated with an integer cost. The optimization objec- tive we study asks to minimize the expected total cost till the target set is reached, while ensuring that the target set is reached almost-surely (with probability 1). We show that for integer costs approximating the optimal cost is undecidable. For positive costs, our results are as follows: (i) we establish matching lower and upper bounds for the optimal cost and the bound is double exponential; (ii) we show that the problem of approximating the optimal cost is decidable and present ap- proximation algorithms developing on the existing algorithms for POMDPs with finite-horizon objectives. While the worst- case running time of our algorithm is double exponential, we present efficient stopping criteria for the algorithm and show experimentally that it performs well in many examples.</abstract>

<originInfo><publisher>AAAI Press</publisher><dateIssued encoding="w3cdtf">2015</dateIssued><place><placeTerm type="text">Austin, TX, USA</placeTerm></place>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence </title></titleInfo>
  <identifier type="arXiv">1411.3880</identifier>
  <identifier type="ISI">000372683700002</identifier>
<part><detail type="volume"><number>5</number></detail><extent unit="pages">3496-3502</extent>
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  <location>     <url>https://research-explorer.ista.ac.at/record/1529</url>  </location>
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<bibliographicCitation>
<ista>Chatterjee K, Chmelik M, Gupta R, Kanodia A. 2015. Optimal cost almost-sure reachability in POMDPs. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence . IAAI: Innovative Applications of Artificial Intelligence, Artifical Intelligence, vol. 5, 3496–3502.</ista>
<chicago>Chatterjee, Krishnendu, Martin Chmelik, Raghav Gupta, and Ayush Kanodia. “Optimal Cost Almost-Sure Reachability in POMDPs.” In &lt;i&gt;Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence &lt;/i&gt;, 5:3496–3502. AAAI Press, 2015.</chicago>
<short>K. Chatterjee, M. Chmelik, R. Gupta, A. Kanodia, in:, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence , AAAI Press, 2015, pp. 3496–3502.</short>
<ieee>K. Chatterjee, M. Chmelik, R. Gupta, and A. Kanodia, “Optimal cost almost-sure reachability in POMDPs,” in &lt;i&gt;Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence &lt;/i&gt;, Austin, TX, USA, 2015, vol. 5, pp. 3496–3502.</ieee>
<apa>Chatterjee, K., Chmelik, M., Gupta, R., &amp;#38; Kanodia, A. (2015). Optimal cost almost-sure reachability in POMDPs. In &lt;i&gt;Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence &lt;/i&gt; (Vol. 5, pp. 3496–3502). Austin, TX, USA: AAAI Press.</apa>
<ama>Chatterjee K, Chmelik M, Gupta R, Kanodia A. Optimal cost almost-sure reachability in POMDPs. In: &lt;i&gt;Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence &lt;/i&gt;. Vol 5. AAAI Press; 2015:3496-3502.</ama>
<mla>Chatterjee, Krishnendu, et al. “Optimal Cost Almost-Sure Reachability in POMDPs.” &lt;i&gt;Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence &lt;/i&gt;, vol. 5, AAAI Press, 2015, pp. 3496–502.</mla>
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