Abstract
In multi-agent planning, preserving the agents’ privacy has become an increasingly popular research topic. In multi-agent privacy-preserving planning, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, preserving the privacy of such information can severely restrict the accuracy of the heuristic functions used while searching for solutions. Recently, it has been shown that centralized planning based on Width-based search is a very effective approach over several benchmark domains, even when the search is driven by uninformed heuristics. In this paper, we investigate the usage of Width-based search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Private facts that are false in the search state are omitted from the shared description of the state.
- 2.
Our code and experimental data will be available on request.
References
Brafman, R.I., Domshlak, C.: From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling, ICAPS, pp. 28–35 (2008)
Nissim, R., Brafman, R.I.: Distributed heuristic forward search for multi-agent planning. J. Artif. Intell. Res. 51(1), 293–332 (2014)
Torreño, A., Onaindia, E., Sapena, Ó.: An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European Conference on Artificial Intelligence, ECAI, pp. 762–767 (2012)
Lipovetzky, N., Geffner, H.: Width and serialization of classical planning problems. In: 20th European Conference on Artificial Intelligence, ECAI 2012, pp. 540–545 (2012)
Brafman, R.I.: A privacy preserving algorithm for multi-agent planning and search. In: International Joint Conference on Artificial Intelligence, IJCAI, pp. 1530–1536 (2015)
Maliah, S., Shani, G., Stern, R.: Privacy preserving landmark detection. In: Proceedings of European Conference on Artificial Intelligence, ECAI, vol. 14 (2014)
Maliah, S., Stern, R., Shani, G.: Privacy preserving LAMA. In: Proceedings of the Fourth Workshop on Distributed and Multi-agent Planning, ICAPS, pp. 100–108 (2016)
Nissim, R., Apsel, U., Brafman, R.I.: Tunneling and decomposition-based state reduction for optimal planning. In: 20th European Conference on Artificial Intelligence, ECAI, pp. 624–629 (2012)
Nissim, R., Brafman, R.I.: Multi-agent A* for parallel and distributed systems. In: Proceedings of the Workshop on Heuristics and Search for Domain-Independent Planning, ICAPS, pp. 42–51 (2012)
Bonisoli, A., Gerevini, A.E., Saetti, A., Serina, I.: A privacy-preserving model for multi-agent propositional planning. J. Exp. Theor. Artif. Intell. (2018, in press)
Fišer, D., Štolba, M., Komenda, A.: MAPlan. In: Proceedings of the Competition of Distributed and Multi-agent Planners, ICAPS, pp. 8–10 (2015)
Bonet, B., Geffner, H.: Planning as heuristic search. Artif. Intell. 129(1–2), 5–33 (2001)
Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90(1–2), 281–300 (1997)
Bandres, W., Bonet, B., Geffner, H.: Planning with pixels in (almost) real time. In: AAAI (2018)
Katz, M., Lipovetzky, N., Moshkovich, D., Tuisov, A.: Adapting novelty to classical planning as heuristic search. In: ICAPS, pp. 172–180 (2017)
Lipovetzky, N., Geffner, H.: Best-first width search: exploration and exploitation in classical planning. In: Singh, S.P., Markovitch, S. (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 4–9 February 2017, San Francisco, California, USA, pp. 3590–3596. AAAI Press (2017)
Sustrik, M.: nanomsg (2016). http://nanomsg.org/
Štolba, M., Komenda, A., Kovacs, D.L.: Competition of distributed and multiagent planners (2015). http://agents.fel.cvut.cz/codmap/
Štolba, M., Komenda, A., Kovacs, D.L.: Competition of distributed and multiagent planners (codmap). In: The International Planning Competition (WIPC-15), vol. 24 (2015)
Olaya, A., G., López C., L., Jiménez, S., C.: Deterministic part of the 7th International Planning Competition IPC7. In: ICAPS (2011). http://www.plg.inf.uc3m.es/ipc2011-deterministic
Štolba, M., Fišer, D., Komenda, A.: Admissible landmark heuristic for multi-agent planning. In: Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, ICAPS (2015)
Bonet, B., Helmert, M.: Strengthening landmark heuristics via hitting sets. In: ECAI 2010–19th European Conference on Artificial Intelligence, pp. 329–334 (2010)
Štolba, M., Komenda, A.: Relaxation heuristics for multiagent planning. In: Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS, pp. 298–306 (2014)
Bonisoli, A., Gerevini, A.E., Saetti, A., Serina, I.: A privacy-preserving model for the multi-agent propositional planning problem. In: Proceedings of the Twenty-First European Conference on Artificial Intelligence, ECAI, pp. 973–974 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Bazzotti, G., Gerevini, A.E., Lipovetzky, N., Percassi, F., Saetti, A., Serina, I. (2018). Iterative Width Search for Multi Agent Privacy-Preserving Planning. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-03840-3_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03839-7
Online ISBN: 978-3-030-03840-3
eBook Packages: Computer ScienceComputer Science (R0)