Privacy preserving planning in multi-agent stochastic environments

Collaborative privacy preserving planning ( cppp ) gained much attention in the past decade. cppp aims to create solutions for multi agent planning problems where cooperation is required to achieve an efficient solution, without exposing information that the agent considers private in the process. T...

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Vydáno v:	Autonomous agents and multi-agent systems Ročník 36; číslo 1
Hlavní autoři:	Hefner, Tommy, Shani, Guy, Stern, Roni
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2022 Springer Nature B.V
Témata:	Adaptation Algorithms Artificial Intelligence Computer Science Computer Systems Organization and Communication Networks Domains Dynamic programming Iterative algorithms Iterative methods Markov processes Message passing Multiagent systems Privacy Real-time programming Run time (computers) Software Engineering/Programming and Operating Systems Synchronism User Interfaces and Human Computer Interaction Privacy preserving planning Multi-agent planning
ISSN:	1387-2532, 1573-7454
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Abstract	Collaborative privacy preserving planning ( cppp ) gained much attention in the past decade. cppp aims to create solutions for multi agent planning problems where cooperation is required to achieve an efficient solution, without exposing information that the agent considers private in the process. To date, cppp has focused on domains with deterministic action effects. However, in real-world problems action effects are often non-deterministic, and actions can have multiple possible effects with varying probabilities. In this paper, we introduce Stochastic cppp ( scppp ), which is an extension of cppp to domains with stochastic action effects. We show how scppp can be modeled as a Markov decision process ( mdp ) and how the value-iteration algorithm can be adapted to solve it. This adaptation requires extending value-iteration to support multiple agents and privacy. Then, we present two adaptions of the real-time dynamic programming ( rtdp ) algorithm, a popular algorithm for solving mdp s, designed to solve scppp problems. The first rtdp adaptation, called distributed rtdp ( drtdp ), yields identical behavior to applying rtdp in a centralized manner on the joint problem. To preserve privacy, drtdp uses a message passing mechanism adopted from the mafs algorithm. The second rtdp adaptation is an approximation of drtdp called public synchronization rtdp ( ps - rtdp ). ps - rtdp differs from drtdp mainly in its message passing mechanism, where ps - rtdp sends significantly fewer messages than drtdp . We experimented on domains adapted from the deterministic cppp literature by adding different stochastic effects to different actions. The results show that ps - rtdp can reduce the amount of messages compared to drtdp by orders of magnitude thus improving run-time, while producing policies with similar expected costs.
AbstractList	Collaborative privacy preserving planning ( cppp ) gained much attention in the past decade. cppp aims to create solutions for multi agent planning problems where cooperation is required to achieve an efficient solution, without exposing information that the agent considers private in the process. To date, cppp has focused on domains with deterministic action effects. However, in real-world problems action effects are often non-deterministic, and actions can have multiple possible effects with varying probabilities. In this paper, we introduce Stochastic cppp ( scppp ), which is an extension of cppp to domains with stochastic action effects. We show how scppp can be modeled as a Markov decision process ( mdp ) and how the value-iteration algorithm can be adapted to solve it. This adaptation requires extending value-iteration to support multiple agents and privacy. Then, we present two adaptions of the real-time dynamic programming ( rtdp ) algorithm, a popular algorithm for solving mdp s, designed to solve scppp problems. The first rtdp adaptation, called distributed rtdp ( drtdp ), yields identical behavior to applying rtdp in a centralized manner on the joint problem. To preserve privacy, drtdp uses a message passing mechanism adopted from the mafs algorithm. The second rtdp adaptation is an approximation of drtdp called public synchronization rtdp ( ps - rtdp ). ps - rtdp differs from drtdp mainly in its message passing mechanism, where ps - rtdp sends significantly fewer messages than drtdp . We experimented on domains adapted from the deterministic cppp literature by adding different stochastic effects to different actions. The results show that ps - rtdp can reduce the amount of messages compared to drtdp by orders of magnitude thus improving run-time, while producing policies with similar expected costs. Collaborative privacy preserving planning (cppp) gained much attention in the past decade. cppp aims to create solutions for multi agent planning problems where cooperation is required to achieve an efficient solution, without exposing information that the agent considers private in the process. To date, cppp has focused on domains with deterministic action effects. However, in real-world problems action effects are often non-deterministic, and actions can have multiple possible effects with varying probabilities. In this paper, we introduce Stochastic cppp (scppp), which is an extension of cppp to domains with stochastic action effects. We show how scppp can be modeled as a Markov decision process (mdp) and how the value-iteration algorithm can be adapted to solve it. This adaptation requires extending value-iteration to support multiple agents and privacy. Then, we present two adaptions of the real-time dynamic programming (rtdp) algorithm, a popular algorithm for solving mdps, designed to solve scppp problems. The first rtdp adaptation, called distributed rtdp (drtdp), yields identical behavior to applying rtdp in a centralized manner on the joint problem. To preserve privacy, drtdp uses a message passing mechanism adopted from the mafs algorithm. The second rtdp adaptation is an approximation of drtdp called public synchronization rtdp (ps-rtdp). ps-rtdp differs from drtdp mainly in its message passing mechanism, where ps-rtdp sends significantly fewer messages than drtdp. We experimented on domains adapted from the deterministic cppp literature by adding different stochastic effects to different actions. The results show that ps-rtdp can reduce the amount of messages compared to drtdp by orders of magnitude thus improving run-time, while producing policies with similar expected costs.
ArticleNumber	22
Author	Shani, Guy Hefner, Tommy Stern, Roni
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References	BrafmanRIDomshlakCOn the complexity of planning for agent teams and its implications for single agent planningArtificial Intelligence20131985271304882710.1016/j.artint.2012.08.005 MaliahSShaniGSternRAction dependencies in privacy-preserving multi-agent planningAutonomous Agents and Multi-Agent Systems201832677982110.1007/s10458-018-9394-z Du, Wenliang., & Zhan, Zhijun. (2002). A practical approach to solve secure multi-party computation problems. In Proceedings of the 2002 workshop on New security paradigms, pp 127–135. Trevizan, F. W., Teichteil-Königsbuch, F., & Thiébaux, S. (2017). Efficient solutions for stochastic shortest path problems with dead ends. In UAI. PutermanMLMarkov decision processes: Discrete stochastic dynamic programming2014New YorkWiley0829.90134 Gohari, P., Hale, M., & Topcu, U. (2020). Privacy-preserving policy synthesis in markov decision processes. In 2020 59th IEEE Conference on Decision and Control (CDC) (pp. 6266–6271). IEEE. Kocsis, L., Szepesvári, C. (2006). Bandit based monte-carlo planning. In European conference on machine learning (pp. 282–293). Springer. BellmanRA Markovian decision processJournal of Mathematics and Mechanics19576679684918590078.34101 Ameloot, T. J., & Van den Bussche, J. (2015). On the convergence of cycle detection for navigational reinforcement learning. http://arxiv.org/1511.08724 WitzigJBeckenbachIEiflerLFackeldeyKGleixnerAGreverAWeberMMixed-integer programming for cycle detection in nonreversible Markov processesMultiscale Modeling & Simulation2018161248265376391210.1137/16M1091162 Hauskrecht, M., Meuleau, N., Kaelbling, L. P, Dean, T. L., & Boutilier, C. (2013). Hierarchical solution of markov decision processes using macro-actions. http://arxiv.org/1301.7381 Brafman, R. I., & Domshlak, C. (2008). From one to many: Planning for loosely coupled multi-agent systems. In ICAPS (pp. 28–35). Štolba, M., Fišer, D., & Komenda, A. (2019). Privacy leakage of search-based multi-agent planning algorithms. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 29, pp. 482–490). Maliah, S., Shani, G., & Brafman, R. I. (2016a). Online macro generation for privacy preserving planning. In Twenty-Sixth International Conference on Automated Planning and Scheduling. Gerevini, A. E., Lipovetzky, N., Percassi, F., Saetti, A., & Serina, I. (2019). Best-first width search for multi agent privacy-preserving planning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 29, pp. 163–171). ŠtolbaMKomendaAThe Madla planner: Multi-agent planning by combination of distributed and local heuristic searchArtificial Intelligence2017252175210370666110.1016/j.artint.2017.08.007 ŠtolbaMTožičkaJKomendaAQuantifying privacy leakage in multi-agent planningTOIT20181832810.1145/3133326 Brafman, R. I. (2015). A privacy preserving algorithm for multi-agent planning and search. In IJCAI (pp. 1530–1536). Maliah, S., Shani, G., & Stern, R. (2016b). Stronger privacy preserving projections for multi-agent planning. In the International Conference on Automated Planning and Scheduling (ICAPS) (pp. 221–229). KolobovA.Planning with Markov decision processes: An AI perspectiveSynthesis Lectures on Artificial Intelligence and Machine Learning20126112101270.68014 Bonet, B., & Geffner, H. (2006). Learning depth-first search: A unified approach to heuristic search in deterministic and non-deterministic settings, and its application to mdps. In In ICAPS (Vol. 6, pp. 142–151). Hefner, T., Stern, R., & Shani, G. (2020). Privacy preserving planning in stochastic environments. In J. C. Beck, O. Buffet, J. Hoffmann, E. Karpas, & S. Sohrabi, (Eds.). Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, Nancy, France, October 26–30 (pp. 258–262). AAAI Press. https://aaai.org/ojs/index.php/ICAPS/article/view/6669 Brendan, M. H., Likhachev, M., & Gordon, G. J. (2005). Bounded real-time dynamic programming: Rtdp with monotone upper bounds and performance guarantees. In Proceedings of the 22nd international conference on Machine learning (pp. 569–576). ACM. Tožička, J., Štolba, M., & Komenda, A. (2017). The limits of strong privacy preserving multi-agent planning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 27). BartoAGBradtkeSJSinghSPLearning to act using real-time dynamic programmingArtificial Intelligence1995721–28113810.1016/0004-3702(94)00011-O Amato, C., Chowdhary, G., Geramifard, A., Kemal Üre, N., & Kochenderfer, M. J. (2013). Decentralized control of partially observable markov decision processes. In 52nd IEEE Conference on Decision and Control (pp. 2398–2405). IEEE. Stolba, M., Fiser, D., & Komenda, A. (2019). Privacy leakage of search-based multi-agent planning algorithms. In J. Benton, N. Lipovetzky, E. Onaindia, D. E. Smith, & S. Srivastava (Eds.). Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, ICAPS 2018, Berkeley, CA, USA, July 11–15, 2019 (pp. 482–490). AAAI Press. Boutilier, C. (1999). Sequential optimality and coordination in multiagent systems. In In IJCAI (Vol. 99, pp. 478–485). Maliah, S., Shani, G., & Stern, R. (2014). Privacy preserving landmark detection. In the European Conference on Artificial Intelligence (ECAI) (pp. 597–602). Wu, F., Zilberstein, S., & Chen, X. (2018). Privacy-preserving policy iteration for decentralized pomdps. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA (pp. 4759–4766). BertsekasDDistributed dynamic programmingIEEE Transactions on Automatic Control198227361061668031910.1109/TAC.1982.1102980 PapadimitriouCHTsitsiklisJNThe complexity of Markov decision processesMathematics of Operations Research198712344145090641610.1287/moor.12.3.441 Keller, T., & Eyerich, P. (2012). Prost: Probabilistic planning based on uct. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 22). Maliah, S., Shani, G., & Stern, R. (2016c). Stronger privacy preserving projections for multi-agent planning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 26). Guestrin, C., Koller, D., & Parr, R. (2001). Max-norm projections for factored mdps. InIn IJCAI (Vol. 17, pp. 673–680). BertsekasDPTsitsiklisJNAn analysis of stochastic shortest path problemsMathematics of Operations Research1991163580595112047110.1287/moor.16.3.580 Venkitasubramaniam, P. (2013). Privacy in stochastic control: A markov decision process perspective. In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 381–388). IEEE. NissimRBrafmanRIDistributed heuristic forward search for multi-agent planningJAIR201451293332327007510.1613/jair.4295 Littman, M. L., Dean, T. L., & Kaelbling, L. P. (2013). On the complexity of solving markov decision problems. http://arxiv.org/1302.4971 StolbaMTozickaJKomendaAQuantifying privacy leakage in multi-agent planningACM Transactions on Internet Technology201818328:128:2110.1145/3133326 BellmanRDynamic programmingScience19661533731343710.1126/science.153.3731.34 Štolba, M., Komenda, A., & Kovacs, D. L. (2015). Competition of distributed and multiagent planners (codmap). In The International Planning Competition (WIPC-15), (p. 24). Smith, T., & Simmons, R. (2006). Focused real-time dynamic programming for mdps: Squeezing more out of a heuristic. In AAAI (pp. 1227–1232). BonetBGeffnerHPlanning as heuristic searchArtificial Intelligence20011291533183577110.1016/S0004-3702(01)00108-4 9554_CR19 9554_CR39 AG Barto (9554_CR5) 1995; 72 9554_CR16 9554_CR38 9554_CR33 9554_CR10 9554_CR32 9554_CR31 9554_CR30 9554_CR15 9554_CR37 9554_CR36 9554_CR13 9554_CR35 9554_CR12 9554_CR34 R Nissim (9554_CR6) 2014; 51 D Bertsekas (9554_CR21) 1982; 27 CH Papadimitriou (9554_CR24) 1987; 12 9554_CR2 R Bellman (9554_CR17) 1957; 6 9554_CR1 R Bellman (9554_CR3) 1966; 153 M Stolba (9554_CR43) 2018; 18 9554_CR9 9554_CR29 9554_CR28 9554_CR7 DP Bertsekas (9554_CR25) 1991; 16 A. Kolobov (9554_CR4) 2012; 6 9554_CR44 9554_CR20 9554_CR42 9554_CR41 9554_CR26 M Štolba (9554_CR14) 2018; 18 M Štolba (9554_CR27) 2017; 252 9554_CR23 RI Brafman (9554_CR8) 2013; 198 J Witzig (9554_CR22) 2018; 16 B Bonet (9554_CR40) 2001; 129 S Maliah (9554_CR11) 2018; 32 ML Puterman (9554_CR18) 2014
References_xml	– reference: Brafman, R. I. (2015). A privacy preserving algorithm for multi-agent planning and search. In IJCAI (pp. 1530–1536). – reference: BertsekasDPTsitsiklisJNAn analysis of stochastic shortest path problemsMathematics of Operations Research1991163580595112047110.1287/moor.16.3.580 – reference: BonetBGeffnerHPlanning as heuristic searchArtificial Intelligence20011291533183577110.1016/S0004-3702(01)00108-4 – reference: Stolba, M., Fiser, D., & Komenda, A. (2019). Privacy leakage of search-based multi-agent planning algorithms. In J. Benton, N. Lipovetzky, E. Onaindia, D. E. Smith, & S. Srivastava (Eds.). Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, ICAPS 2018, Berkeley, CA, USA, July 11–15, 2019 (pp. 482–490). AAAI Press. – reference: Du, Wenliang., & Zhan, Zhijun. (2002). A practical approach to solve secure multi-party computation problems. In Proceedings of the 2002 workshop on New security paradigms, pp 127–135. – reference: Venkitasubramaniam, P. (2013). Privacy in stochastic control: A markov decision process perspective. In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 381–388). IEEE. – reference: Tožička, J., Štolba, M., & Komenda, A. (2017). The limits of strong privacy preserving multi-agent planning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 27). – reference: MaliahSShaniGSternRAction dependencies in privacy-preserving multi-agent planningAutonomous Agents and Multi-Agent Systems201832677982110.1007/s10458-018-9394-z – reference: Maliah, S., Shani, G., & Stern, R. (2016c). Stronger privacy preserving projections for multi-agent planning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 26). – reference: BellmanRDynamic programmingScience19661533731343710.1126/science.153.3731.34 – reference: BrafmanRIDomshlakCOn the complexity of planning for agent teams and its implications for single agent planningArtificial Intelligence20131985271304882710.1016/j.artint.2012.08.005 – reference: KolobovA.Planning with Markov decision processes: An AI perspectiveSynthesis Lectures on Artificial Intelligence and Machine Learning20126112101270.68014 – reference: ŠtolbaMTožičkaJKomendaAQuantifying privacy leakage in multi-agent planningTOIT20181832810.1145/3133326 – reference: Hefner, T., Stern, R., & Shani, G. (2020). Privacy preserving planning in stochastic environments. In J. C. Beck, O. Buffet, J. Hoffmann, E. Karpas, & S. Sohrabi, (Eds.). Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, Nancy, France, October 26–30 (pp. 258–262). AAAI Press. https://aaai.org/ojs/index.php/ICAPS/article/view/6669 – reference: Guestrin, C., Koller, D., & Parr, R. (2001). Max-norm projections for factored mdps. InIn IJCAI (Vol. 17, pp. 673–680). – reference: Maliah, S., Shani, G., & Stern, R. (2016b). Stronger privacy preserving projections for multi-agent planning. In the International Conference on Automated Planning and Scheduling (ICAPS) (pp. 221–229). – reference: StolbaMTozickaJKomendaAQuantifying privacy leakage in multi-agent planningACM Transactions on Internet Technology201818328:128:2110.1145/3133326 – reference: Bonet, B., & Geffner, H. (2006). Learning depth-first search: A unified approach to heuristic search in deterministic and non-deterministic settings, and its application to mdps. In In ICAPS (Vol. 6, pp. 142–151). – reference: BellmanRA Markovian decision processJournal of Mathematics and Mechanics19576679684918590078.34101 – reference: Gohari, P., Hale, M., & Topcu, U. (2020). Privacy-preserving policy synthesis in markov decision processes. In 2020 59th IEEE Conference on Decision and Control (CDC) (pp. 6266–6271). IEEE. – reference: Štolba, M., Fišer, D., & Komenda, A. (2019). Privacy leakage of search-based multi-agent planning algorithms. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 29, pp. 482–490). – reference: Brendan, M. H., Likhachev, M., & Gordon, G. J. (2005). Bounded real-time dynamic programming: Rtdp with monotone upper bounds and performance guarantees. In Proceedings of the 22nd international conference on Machine learning (pp. 569–576). ACM. – reference: WitzigJBeckenbachIEiflerLFackeldeyKGleixnerAGreverAWeberMMixed-integer programming for cycle detection in nonreversible Markov processesMultiscale Modeling & Simulation2018161248265376391210.1137/16M1091162 – reference: Wu, F., Zilberstein, S., & Chen, X. (2018). Privacy-preserving policy iteration for decentralized pomdps. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA (pp. 4759–4766). – reference: Trevizan, F. W., Teichteil-Königsbuch, F., & Thiébaux, S. (2017). Efficient solutions for stochastic shortest path problems with dead ends. In UAI. – reference: Littman, M. L., Dean, T. L., & Kaelbling, L. P. (2013). On the complexity of solving markov decision problems. http://arxiv.org/1302.4971 – reference: Brafman, R. I., & Domshlak, C. (2008). From one to many: Planning for loosely coupled multi-agent systems. In ICAPS (pp. 28–35). – reference: Ameloot, T. J., & Van den Bussche, J. (2015). On the convergence of cycle detection for navigational reinforcement learning. http://arxiv.org/1511.08724 – reference: Keller, T., & Eyerich, P. (2012). Prost: Probabilistic planning based on uct. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 22). – reference: ŠtolbaMKomendaAThe Madla planner: Multi-agent planning by combination of distributed and local heuristic searchArtificial Intelligence2017252175210370666110.1016/j.artint.2017.08.007 – reference: Boutilier, C. (1999). Sequential optimality and coordination in multiagent systems. In In IJCAI (Vol. 99, pp. 478–485). – reference: Hauskrecht, M., Meuleau, N., Kaelbling, L. P, Dean, T. L., & Boutilier, C. (2013). Hierarchical solution of markov decision processes using macro-actions. http://arxiv.org/1301.7381 – reference: PutermanMLMarkov decision processes: Discrete stochastic dynamic programming2014New YorkWiley0829.90134 – reference: BartoAGBradtkeSJSinghSPLearning to act using real-time dynamic programmingArtificial Intelligence1995721–28113810.1016/0004-3702(94)00011-O – reference: Štolba, M., Komenda, A., & Kovacs, D. L. (2015). Competition of distributed and multiagent planners (codmap). In The International Planning Competition (WIPC-15), (p. 24). – reference: Gerevini, A. E., Lipovetzky, N., Percassi, F., Saetti, A., & Serina, I. (2019). Best-first width search for multi agent privacy-preserving planning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 29, pp. 163–171). – reference: Maliah, S., Shani, G., & Stern, R. (2014). Privacy preserving landmark detection. In the European Conference on Artificial Intelligence (ECAI) (pp. 597–602). – reference: Kocsis, L., Szepesvári, C. (2006). Bandit based monte-carlo planning. In European conference on machine learning (pp. 282–293). Springer. – reference: BertsekasDDistributed dynamic programmingIEEE Transactions on Automatic Control198227361061668031910.1109/TAC.1982.1102980 – reference: Smith, T., & Simmons, R. (2006). Focused real-time dynamic programming for mdps: Squeezing more out of a heuristic. In AAAI (pp. 1227–1232). – reference: NissimRBrafmanRIDistributed heuristic forward search for multi-agent planningJAIR201451293332327007510.1613/jair.4295 – reference: Maliah, S., Shani, G., & Brafman, R. I. (2016a). Online macro generation for privacy preserving planning. In Twenty-Sixth International Conference on Automated Planning and Scheduling. – reference: Amato, C., Chowdhary, G., Geramifard, A., Kemal Üre, N., & Kochenderfer, M. J. (2013). Decentralized control of partially observable markov decision processes. In 52nd IEEE Conference on Decision and Control (pp. 2398–2405). IEEE. – reference: PapadimitriouCHTsitsiklisJNThe complexity of Markov decision processesMathematics of Operations Research198712344145090641610.1287/moor.12.3.441 – volume-title: Markov decision processes: Discrete stochastic dynamic programming year: 2014 ident: 9554_CR18 – ident: 9554_CR26 – volume: 27 start-page: 610 issue: 3 year: 1982 ident: 9554_CR21 publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.1982.1102980 – volume: 6 start-page: 1 issue: 1 year: 2012 ident: 9554_CR4 publication-title: Synthesis Lectures on Artificial Intelligence and Machine Learning – ident: 9554_CR32 – volume: 12 start-page: 441 issue: 3 year: 1987 ident: 9554_CR24 publication-title: Mathematics of Operations Research doi: 10.1287/moor.12.3.441 – ident: 9554_CR12 doi: 10.1609/icaps.v26i1.13741 – ident: 9554_CR36 doi: 10.1109/CDC.2013.6760239 – ident: 9554_CR33 doi: 10.1109/CDC42340.2020.9304015 – ident: 9554_CR16 – ident: 9554_CR30 – ident: 9554_CR39 – ident: 9554_CR20 doi: 10.1145/844102.844125 – volume: 153 start-page: 34 issue: 3731 year: 1966 ident: 9554_CR3 publication-title: Science doi: 10.1126/science.153.3731.34 – ident: 9554_CR13 doi: 10.1609/aaai.v32i1.11584 – volume: 6 start-page: 679 year: 1957 ident: 9554_CR17 publication-title: Journal of Mathematics and Mechanics – ident: 9554_CR37 doi: 10.1007/11871842_29 – ident: 9554_CR44 – volume: 16 start-page: 580 issue: 3 year: 1991 ident: 9554_CR25 publication-title: Mathematics of Operations Research doi: 10.1287/moor.16.3.580 – volume: 18 start-page: 28 issue: 3 year: 2018 ident: 9554_CR14 publication-title: TOIT doi: 10.1145/3133326 – volume: 129 start-page: 5 issue: 1 year: 2001 ident: 9554_CR40 publication-title: Artificial Intelligence doi: 10.1016/S0004-3702(01)00108-4 – ident: 9554_CR2 – volume: 51 start-page: 293 year: 2014 ident: 9554_CR6 publication-title: JAIR doi: 10.1613/jair.4295 – ident: 9554_CR23 – ident: 9554_CR42 – ident: 9554_CR28 doi: 10.1609/icaps.v26i1.13753 – ident: 9554_CR29 doi: 10.1609/icaps.v26i1.13753 – volume: 32 start-page: 779 issue: 6 year: 2018 ident: 9554_CR11 publication-title: Autonomous Agents and Multi-Agent Systems doi: 10.1007/s10458-018-9394-z – ident: 9554_CR34 doi: 10.1109/Allerton.2013.6736549 – ident: 9554_CR10 doi: 10.1145/2970030.2970042 – volume: 72 start-page: 81 issue: 1–2 year: 1995 ident: 9554_CR5 publication-title: Artificial Intelligence doi: 10.1016/0004-3702(94)00011-O – ident: 9554_CR19 – volume: 16 start-page: 248 issue: 1 year: 2018 ident: 9554_CR22 publication-title: Multiscale Modeling & Simulation doi: 10.1137/16M1091162 – ident: 9554_CR41 doi: 10.1609/icaps.v22i1.13518 – volume: 198 start-page: 52 year: 2013 ident: 9554_CR8 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2012.08.005 – ident: 9554_CR35 – ident: 9554_CR15 – ident: 9554_CR31 – ident: 9554_CR9 – ident: 9554_CR38 – volume: 252 start-page: 175 year: 2017 ident: 9554_CR27 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2017.08.007 – volume: 18 start-page: 28:1 issue: 3 year: 2018 ident: 9554_CR43 publication-title: ACM Transactions on Internet Technology doi: 10.1145/3133326 – ident: 9554_CR7 – ident: 9554_CR1
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Snippet	Collaborative privacy preserving planning ( cppp ) gained much attention in the past decade. cppp aims to create solutions for multi agent planning problems... Collaborative privacy preserving planning (cppp) gained much attention in the past decade. cppp aims to create solutions for multi agent planning problems...
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SubjectTerms	Adaptation Algorithms Artificial Intelligence Computer Science Computer Systems Organization and Communication Networks Domains Dynamic programming Iterative algorithms Iterative methods Markov processes Message passing Multiagent systems Privacy Real-time programming Run time (computers) Software Engineering/Programming and Operating Systems Synchronism User Interfaces and Human Computer Interaction
Title	Privacy preserving planning in multi-agent stochastic environments
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