[{"status":"public","month":"12","conference":{"end_date":"2016-02-17","name":"AAAI: Conference on Artificial Intelligence","location":"Phoenix, AZ, USA","start_date":"2016-02-12"},"date_created":"2018-12-11T11:50:30Z","publist_id":"6191","intvolume":"      2016","publisher":"AAAI Press","page":"3225 - 3232","volume":2016,"date_published":"2016-12-02T00:00:00Z","abstract":[{"lang":"eng","text":"POMDPs are standard models for probabilistic planning problems, where an agent interacts with an uncertain environment. We study the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a policy to ensure that the target set is reached with probability 1 (almost-surely). While in general the problem is EXPTIMEcomplete, in many practical cases policies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. In this work, we first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. We report experimental results demonstrating the scalability of our symbolic (SAT-based) approach. © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved."}],"publication_status":"published","project":[{"call_identifier":"FWF","_id":"2584A770-B435-11E9-9278-68D0E5697425","name":"Modern Graph Algorithmic Techniques in Formal Verification","grant_number":"P 23499-N23"},{"grant_number":"S 11407_N23","name":"Rigorous Systems Engineering","_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"call_identifier":"FP7","_id":"2581B60A-B435-11E9-9278-68D0E5697425","grant_number":"279307","name":"Quantitative Graph Games: Theory and Applications"}],"department":[{"_id":"KrCh"},{"_id":"ToHe"}],"_id":"1166","related_material":{"link":[{"relation":"table_of_contents","url":"https://dl.acm.org/citation.cfm?id=3016355"}],"record":[{"id":"5443","status":"public","relation":"earlier_version"}]},"language":[{"iso":"eng"}],"publication":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","ec_funded":1,"title":"A symbolic SAT based algorithm for almost sure reachability with small strategies in pomdps","year":"2016","day":"02","oa_version":"None","type":"conference","date_updated":"2023-02-23T12:26:41Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","author":[{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","last_name":"Chatterjee","first_name":"Krishnendu"},{"last_name":"Chmelik","first_name":"Martin","id":"3624234E-F248-11E8-B48F-1D18A9856A87","full_name":"Chmelik, Martin"},{"first_name":"Jessica","last_name":"Davies","id":"378E0060-F248-11E8-B48F-1D18A9856A87","full_name":"Davies, Jessica"}],"quality_controlled":"1","citation":{"ama":"Chatterjee K, Chmelik M, Davies J. A symbolic SAT based algorithm for almost sure reachability with small strategies in pomdps. In: <i>Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence</i>. Vol 2016. AAAI Press; 2016:3225-3232.","ieee":"K. Chatterjee, M. Chmelik, and J. Davies, “A symbolic SAT based algorithm for almost sure reachability with small strategies in pomdps,” in <i>Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence</i>, Phoenix, AZ, USA, 2016, vol. 2016, pp. 3225–3232.","short":"K. Chatterjee, M. Chmelik, J. Davies, in:, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI Press, 2016, pp. 3225–3232.","chicago":"Chatterjee, Krishnendu, Martin Chmelik, and Jessica Davies. “A Symbolic SAT Based Algorithm for Almost Sure Reachability with Small Strategies in Pomdps.” In <i>Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence</i>, 2016:3225–32. AAAI Press, 2016.","ista":"Chatterjee K, Chmelik M, Davies J. 2016. A symbolic SAT based algorithm for almost sure reachability with small strategies in pomdps. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 2016, 3225–3232.","mla":"Chatterjee, Krishnendu, et al. “A Symbolic SAT Based Algorithm for Almost Sure Reachability with Small Strategies in Pomdps.” <i>Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence</i>, vol. 2016, AAAI Press, 2016, pp. 3225–32.","apa":"Chatterjee, K., Chmelik, M., &#38; Davies, J. (2016). A symbolic SAT based algorithm for almost sure reachability with small strategies in pomdps. In <i>Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence</i> (Vol. 2016, pp. 3225–3232). Phoenix, AZ, USA: AAAI Press."}},{"citation":{"apa":"Chatterjee, K., Chmelik, M., &#38; Davies, J. (2015). <i>A symbolic SAT-based algorithm for almost-sure reachability with small strategies in POMDPs</i>. IST Austria. <a href=\"https://doi.org/10.15479/AT:IST-2015-325-v2-1\">https://doi.org/10.15479/AT:IST-2015-325-v2-1</a>","mla":"Chatterjee, Krishnendu, et al. <i>A Symbolic SAT-Based Algorithm for Almost-Sure Reachability with Small Strategies in POMDPs</i>. IST Austria, 2015, doi:<a href=\"https://doi.org/10.15479/AT:IST-2015-325-v2-1\">10.15479/AT:IST-2015-325-v2-1</a>.","short":"K. Chatterjee, M. Chmelik, J. Davies, A Symbolic SAT-Based Algorithm for Almost-Sure Reachability with Small Strategies in POMDPs, IST Austria, 2015.","chicago":"Chatterjee, Krishnendu, Martin Chmelik, and Jessica Davies. <i>A Symbolic SAT-Based Algorithm for Almost-Sure Reachability with Small Strategies in POMDPs</i>. IST Austria, 2015. <a href=\"https://doi.org/10.15479/AT:IST-2015-325-v2-1\">https://doi.org/10.15479/AT:IST-2015-325-v2-1</a>.","ista":"Chatterjee K, Chmelik M, Davies J. 2015. 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We study the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a policy to ensure that the target set is reached with probability 1 (almost-surely). While in general the problem is EXPTIME-complete, in many practical cases policies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. In this work, we first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. 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