--- _id: '5434' abstract: - lang: eng text: DEC-POMDPs extend POMDPs to a multi-agent setting, where several agents operate in an uncertain environment independently to achieve a joint objective. DEC-POMDPs have been studied with finite-horizon and infinite-horizon discounted-sum objectives, and there exist solvers both for exact and approximate solutions. In this work we consider Goal-DEC-POMDPs, where given a set of target states, the objective is to ensure that the target set is reached with minimal cost. We consider the indefinite-horizon (infinite-horizon with either discounted-sum, or undiscounted-sum, where absorbing goal states have zero-cost) problem. We present a new method to solve the problem that extends methods for finite-horizon DEC- POMDPs and the RTDP-Bel approach for POMDPs. We present experimental results on several examples, and show our approach presents promising results. alternative_title: - IST Austria Technical Report author: - first_name: '1' full_name: Anonymous, 1 last_name: Anonymous - first_name: '2' full_name: Anonymous, 2 last_name: Anonymous citation: ama: Anonymous 1, Anonymous 2. Optimal Cost Indefinite-Horizon Reachability in Goal DEC-POMDPs. IST Austria; 2015. apa: Anonymous, 1, & Anonymous, 2. (2015). Optimal cost indefinite-horizon reachability in goal DEC-POMDPs. IST Austria. chicago: Anonymous, 1, and 2 Anonymous. Optimal Cost Indefinite-Horizon Reachability in Goal DEC-POMDPs. IST Austria, 2015. ieee: 1 Anonymous and 2 Anonymous, Optimal cost indefinite-horizon reachability in goal DEC-POMDPs. IST Austria, 2015. ista: Anonymous 1, Anonymous 2. 2015. Optimal cost indefinite-horizon reachability in goal DEC-POMDPs, IST Austria, 16p. mla: Anonymous, 1, and 2 Anonymous. Optimal Cost Indefinite-Horizon Reachability in Goal DEC-POMDPs. IST Austria, 2015. short: 1 Anonymous, 2 Anonymous, Optimal Cost Indefinite-Horizon Reachability in Goal DEC-POMDPs, IST Austria, 2015. date_created: 2018-12-12T11:39:18Z date_published: 2015-02-19T00:00:00Z date_updated: 2020-07-14T23:04:59Z day: '19' ddc: - '000' file: - access_level: open_access checksum: 8542fd0b10aed7811cd41077b8ccb632 content_type: application/pdf creator: system date_created: 2018-12-12T11:53:14Z date_updated: 2020-07-14T12:46:53Z file_id: '5475' file_name: IST-2015-326-v1+1_main.pdf file_size: 378162 relation: main_file - access_level: closed checksum: 84c31c537bdaf7a91909f18d25d640ab content_type: text/plain creator: dernst date_created: 2019-04-16T13:00:33Z date_updated: 2020-07-14T12:46:53Z file_id: '6317' file_name: IST-2015-326-v1+2_authors.txt file_size: 64 relation: main_file file_date_updated: 2020-07-14T12:46:53Z has_accepted_license: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '16' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '326' status: public title: Optimal cost indefinite-horizon reachability in goal DEC-POMDPs type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '5429' abstract: - lang: eng text: "We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives. \r\nThere have been two different views: (i) the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii) the satisfaction semantics, where the goal is to maximize the probability of runs such that the mean-payoff value stays above a given vector. \ \r\nWe consider the problem where the goal is to optimize the expectation under the constraint that the satisfaction semantics is ensured, and thus consider a generalization that unifies the existing semantics.\r\nOur problem captures the notion of optimization with respect to strategies that are risk-averse (i.e., ensures certain probabilistic guarantee).\r\nOur main results are algorithms for the decision problem which are always polynomial in the size of the MDP. We also show that an approximation of the Pareto-curve can be computed in time polynomial in the size of the MDP, and the approximation factor, but exponential in the number of dimensions.\r\nFinally, we present a complete characterization of the strategy complexity (in terms of memory bounds and randomization) required to solve our problem." alternative_title: - IST Austria Technical Report author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Zuzana full_name: Komarkova, Zuzana last_name: Komarkova - first_name: Jan full_name: Kretinsky, Jan id: 44CEF464-F248-11E8-B48F-1D18A9856A87 last_name: Kretinsky orcid: 0000-0002-8122-2881 citation: ama: Chatterjee K, Komarkova Z, Kretinsky J. Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes. IST Austria; 2015. doi:10.15479/AT:IST-2015-318-v1-1 apa: Chatterjee, K., Komarkova, Z., & Kretinsky, J. (2015). Unifying two views on multiple mean-payoff objectives in Markov decision processes. IST Austria. https://doi.org/10.15479/AT:IST-2015-318-v1-1 chicago: Chatterjee, Krishnendu, Zuzana Komarkova, and Jan Kretinsky. Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes. IST Austria, 2015. https://doi.org/10.15479/AT:IST-2015-318-v1-1. ieee: K. Chatterjee, Z. Komarkova, and J. Kretinsky, Unifying two views on multiple mean-payoff objectives in Markov decision processes. IST Austria, 2015. ista: Chatterjee K, Komarkova Z, Kretinsky J. 2015. Unifying two views on multiple mean-payoff objectives in Markov decision processes, IST Austria, 41p. mla: Chatterjee, Krishnendu, et al. Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes. IST Austria, 2015, doi:10.15479/AT:IST-2015-318-v1-1. short: K. Chatterjee, Z. Komarkova, J. Kretinsky, Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes, IST Austria, 2015. date_created: 2018-12-12T11:39:17Z date_published: 2015-01-12T00:00:00Z date_updated: 2023-02-23T12:26:16Z day: '12' ddc: - '004' department: - _id: KrCh doi: 10.15479/AT:IST-2015-318-v1-1 file: - access_level: open_access checksum: e4869a584567c506349abda9c8ec7db3 content_type: application/pdf creator: system date_created: 2018-12-12T11:54:11Z date_updated: 2020-07-14T12:46:52Z file_id: '5533' file_name: IST-2015-318-v1+1_main.pdf file_size: 689863 relation: main_file file_date_updated: 2020-07-14T12:46:52Z has_accepted_license: '1' language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: '41' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '318' related_material: record: - id: '1657' relation: later_version status: public - id: '466' relation: later_version status: public - id: '5435' relation: later_version status: public status: public title: Unifying two views on multiple mean-payoff objectives in Markov decision processes type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '5435' abstract: - lang: eng text: "We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives. \r\nThere have been two different views: (i) the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii) the satisfaction semantics, where the goal is to maximize the probability of runs such that the mean-payoff value stays above a given vector. \ \r\nWe consider the problem where the goal is to optimize the expectation under the constraint that the satisfaction semantics is ensured, and thus consider a generalization that unifies the existing semantics. Our problem captures the notion of optimization with respect to strategies that are risk-averse (i.e., ensures certain probabilistic guarantee).\r\nOur main results are algorithms for the decision problem which are always polynomial in the size of the MDP.\r\nWe also show that an approximation of the Pareto-curve can be computed in time polynomial in the size of the MDP, and the approximation factor, but exponential in the number of dimensions. Finally, we present a complete characterization of the strategy complexity (in terms of memory bounds and randomization) required to solve our problem." alternative_title: - IST Austria Technical Report author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Zuzana full_name: Komarkova, Zuzana last_name: Komarkova - first_name: Jan full_name: Kretinsky, Jan id: 44CEF464-F248-11E8-B48F-1D18A9856A87 last_name: Kretinsky orcid: 0000-0002-8122-2881 citation: ama: Chatterjee K, Komarkova Z, Kretinsky J. Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes. IST Austria; 2015. doi:10.15479/AT:IST-2015-318-v2-1 apa: Chatterjee, K., Komarkova, Z., & Kretinsky, J. (2015). Unifying two views on multiple mean-payoff objectives in Markov decision processes. IST Austria. https://doi.org/10.15479/AT:IST-2015-318-v2-1 chicago: Chatterjee, Krishnendu, Zuzana Komarkova, and Jan Kretinsky. Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes. IST Austria, 2015. https://doi.org/10.15479/AT:IST-2015-318-v2-1. ieee: K. Chatterjee, Z. Komarkova, and J. Kretinsky, Unifying two views on multiple mean-payoff objectives in Markov decision processes. IST Austria, 2015. ista: Chatterjee K, Komarkova Z, Kretinsky J. 2015. Unifying two views on multiple mean-payoff objectives in Markov decision processes, IST Austria, 51p. mla: Chatterjee, Krishnendu, et al. Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes. IST Austria, 2015, doi:10.15479/AT:IST-2015-318-v2-1. short: K. Chatterjee, Z. Komarkova, J. Kretinsky, Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes, IST Austria, 2015. date_created: 2018-12-12T11:39:19Z date_published: 2015-02-23T00:00:00Z date_updated: 2023-02-23T12:26:00Z day: '23' ddc: - '004' department: - _id: KrCh doi: 10.15479/AT:IST-2015-318-v2-1 file: - access_level: open_access checksum: 75284adec80baabdfe71ff9ebbc27445 content_type: application/pdf creator: system date_created: 2018-12-12T11:54:03Z date_updated: 2020-07-14T12:46:53Z file_id: '5525' file_name: IST-2015-318-v2+1_main.pdf file_size: 717630 relation: main_file file_date_updated: 2020-07-14T12:46:53Z has_accepted_license: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '51' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '327' related_material: record: - id: '1657' relation: later_version status: public - id: '466' relation: later_version status: public - id: '5429' relation: earlier_version status: public status: public title: Unifying two views on multiple mean-payoff objectives in Markov decision processes type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '5436' abstract: - lang: eng text: "Recently there has been a significant effort to handle quantitative properties in formal verification and synthesis. While weighted automata over finite and infinite words provide a natural and flexible framework to express quantitative properties, perhaps surprisingly, some basic system properties such as average response time cannot be expressed using weighted automata, nor in any other know decidable formalism. In this work, we introduce nested weighted automata as a natural extension of weighted automata which makes it possible to express important quantitative properties such as average response time.\r\nIn nested weighted automata, a master automaton spins off and collects results from weighted slave automata, each of which computes a quantity along a finite portion of an infinite word. Nested weighted automata can be viewed as the quantitative analogue of monitor automata, which are used in run-time verification. We establish an almost complete decidability picture for the basic decision problems about nested weighted automata, and illustrate their applicability in several domains. In particular, nested weighted automata can be used to decide average response time properties." alternative_title: - IST Austria Technical Report author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000−0002−2985−7724 - first_name: Jan full_name: Otop, Jan id: 2FC5DA74-F248-11E8-B48F-1D18A9856A87 last_name: Otop citation: ama: Chatterjee K, Henzinger TA, Otop J. Nested Weighted Automata. IST Austria; 2015. doi:10.15479/AT:IST-2015-170-v2-2 apa: Chatterjee, K., Henzinger, T. A., & Otop, J. (2015). Nested weighted automata. IST Austria. https://doi.org/10.15479/AT:IST-2015-170-v2-2 chicago: Chatterjee, Krishnendu, Thomas A Henzinger, and Jan Otop. Nested Weighted Automata. IST Austria, 2015. https://doi.org/10.15479/AT:IST-2015-170-v2-2. ieee: K. Chatterjee, T. A. Henzinger, and J. Otop, Nested weighted automata. IST Austria, 2015. ista: Chatterjee K, Henzinger TA, Otop J. 2015. Nested weighted automata, IST Austria, 29p. mla: Chatterjee, Krishnendu, et al. Nested Weighted Automata. IST Austria, 2015, doi:10.15479/AT:IST-2015-170-v2-2. short: K. Chatterjee, T.A. Henzinger, J. Otop, Nested Weighted Automata, IST Austria, 2015. date_created: 2018-12-12T11:39:19Z date_published: 2015-04-24T00:00:00Z date_updated: 2023-02-23T12:25:21Z day: '24' ddc: - '000' department: - _id: KrCh - _id: ToHe doi: 10.15479/AT:IST-2015-170-v2-2 file: - access_level: open_access checksum: 3c402f47d3669c28d04d1af405a08e3f content_type: application/pdf creator: system date_created: 2018-12-12T11:54:19Z date_updated: 2020-07-14T12:46:54Z file_id: '5541' file_name: IST-2015-170-v2+2_report.pdf file_size: 569991 relation: main_file file_date_updated: 2020-07-14T12:46:54Z has_accepted_license: '1' language: - iso: eng month: '04' oa: 1 oa_version: Published Version page: '29' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '331' related_material: record: - id: '1656' relation: later_version status: public - id: '467' relation: later_version status: public - id: '5415' relation: earlier_version status: public status: public title: Nested weighted automata type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ... --- _id: '5437' abstract: - lang: eng text: "We consider the core algorithmic problems related to verification of systems with respect to three classical quantitative properties, namely, the mean-payoff property, the ratio property, and the minimum initial credit for energy property. \r\nThe algorithmic problem given a graph and a quantitative property asks to compute the optimal value (the infimum value over all traces) from every node of the graph. We consider graphs with constant treewidth, and it is well-known that the control-flow graphs of most programs have constant treewidth. Let $n$ denote the number of nodes of a graph, $m$ the number of edges (for constant treewidth graphs $m=O(n)$) and $W$ the largest absolute value of the weights.\r\nOur main theoretical results are as follows.\r\nFirst, for constant treewidth graphs we present an algorithm that approximates the mean-payoff value within a multiplicative factor of $\\epsilon$ in time $O(n \\cdot \\log (n/\\epsilon))$ and linear space, as compared to the classical algorithms that require quadratic time. Second, for the ratio property we present an algorithm that for constant treewidth graphs works in time $O(n \\cdot \\log (|a\\cdot b|))=O(n\\cdot\\log (n\\cdot W))$, when the output is $\\frac{a}{b}$, as compared to the previously best known algorithm with running time $O(n^2 \\cdot \\log (n\\cdot W))$. Third, for the minimum initial credit problem we show that (i)~for general graphs the problem can be solved in $O(n^2\\cdot m)$ time and the associated decision problem can be solved in $O(n\\cdot m)$ time, improving the previous known $O(n^3\\cdot m\\cdot \\log (n\\cdot W))$ and $O(n^2 \\cdot m)$ bounds, respectively; and (ii)~for constant treewidth graphs we present an algorithm that requires $O(n\\cdot \\log n)$ time, improving the previous known $O(n^4 \\cdot \\log (n \\cdot W))$ bound.\r\nWe have implemented some of our algorithms and show that they present a significant speedup on standard benchmarks. " alternative_title: - IST Austria Technical Report author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Rasmus full_name: Ibsen-Jensen, Rasmus id: 3B699956-F248-11E8-B48F-1D18A9856A87 last_name: Ibsen-Jensen orcid: 0000-0003-4783-0389 - first_name: Andreas full_name: Pavlogiannis, Andreas id: 49704004-F248-11E8-B48F-1D18A9856A87 last_name: Pavlogiannis orcid: 0000-0002-8943-0722 citation: ama: Chatterjee K, Ibsen-Jensen R, Pavlogiannis A. Faster Algorithms for Quantitative Verification in Constant Treewidth Graphs. IST Austria; 2015. doi:10.15479/AT:IST-2015-330-v2-1 apa: Chatterjee, K., Ibsen-Jensen, R., & Pavlogiannis, A. (2015). Faster algorithms for quantitative verification in constant treewidth graphs. IST Austria. https://doi.org/10.15479/AT:IST-2015-330-v2-1 chicago: Chatterjee, Krishnendu, Rasmus Ibsen-Jensen, and Andreas Pavlogiannis. Faster Algorithms for Quantitative Verification in Constant Treewidth Graphs. IST Austria, 2015. https://doi.org/10.15479/AT:IST-2015-330-v2-1. ieee: K. Chatterjee, R. Ibsen-Jensen, and A. Pavlogiannis, Faster algorithms for quantitative verification in constant treewidth graphs. IST Austria, 2015. ista: Chatterjee K, Ibsen-Jensen R, Pavlogiannis A. 2015. Faster algorithms for quantitative verification in constant treewidth graphs, IST Austria, 27p. mla: Chatterjee, Krishnendu, et al. Faster Algorithms for Quantitative Verification in Constant Treewidth Graphs. IST Austria, 2015, doi:10.15479/AT:IST-2015-330-v2-1. short: K. Chatterjee, R. Ibsen-Jensen, A. Pavlogiannis, Faster Algorithms for Quantitative Verification in Constant Treewidth Graphs, IST Austria, 2015. date_created: 2018-12-12T11:39:19Z date_published: 2015-04-27T00:00:00Z date_updated: 2023-02-23T12:26:05Z day: '27' ddc: - '000' department: - _id: KrCh doi: 10.15479/AT:IST-2015-330-v2-1 file: - access_level: open_access checksum: f5917c20f84018b362d385c000a2e123 content_type: application/pdf creator: system date_created: 2018-12-12T11:53:12Z date_updated: 2020-07-14T12:46:54Z file_id: '5473' file_name: IST-2015-330-v2+1_main.pdf file_size: 1072137 relation: main_file file_date_updated: 2020-07-14T12:46:54Z has_accepted_license: '1' language: - iso: eng month: '04' oa: 1 oa_version: Published Version page: '27' publication_identifier: issn: - 2664-1690 publication_status: published publisher: IST Austria pubrep_id: '333' related_material: record: - id: '1607' relation: later_version status: public - id: '5430' relation: earlier_version status: public status: public title: Faster algorithms for quantitative verification in constant treewidth graphs type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2015' ...