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Abstrakty
Contingent planning models a robot that must achieve a goal in a partially observable environment with non-deterministic actions. A solution for this problem is generated by searching in the space of belief states, where a belief state is a set of possible world states. However, if there is an unavoidable dead-end state, the robot will fail to accomplish his task. In this work, rather than limiting a contingent planning task to the agent's actions and observations, we model a planning agent that is able to proactively resort to humans for help in order to complete tasks that would be unsolvable otherwise. Our aim is to develop a symbiotic autonomous agent, that is, an agent that, proactively and autonomously, asks for human help when needed. We formalize this problem and propose an extension of a translation technique to convert the contingent planning problem with human help into a non-deterministic fully observable planning problem that can be solved by an off-the-shelf efficient FOND planner.
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Czasopismo
Rocznik
Tom
Strony
63--81
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
- Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo (USP), São Paulo, Brazil
autor
- Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo (USP), São Paulo, Brazil
autor
- Escola de Artes, Ciências e Humanidades, University of São Paulo (USP), São Paulo, Brazil
Bibliografia
- [1] Vincent J. The robotic farm of the future isnt what youd expect. [Online; accessed 15-October-2018]. URL https://www:theverge:com/2018/10/9/17950502/robot-farm-future-iron-ox-agriculture-automation.
- [2] Barnett D. The robots are coming: but will they really take all our jobs? [Online; accessed 15-October-2018]; 2017. URL https://www:independent:co:uk/news/science/robots-are-coming-but-will-they-take-our-jobs-uk-artificial-intelligence-doctor-who-a8080501:html.
- [3] Home Assistant Robot AR. [Online; accessed 03-August-2018]; 2012. URL http://www:jsk:t:u-tokyo:ac:jp/research/irt/ar:html.
- [4] Robots in the home: Will older adults roll out the welcome mat? 2012. [Online; accessed 19-July-2018]. https://www:sciencedaily:com/releases/2012/10/121025161518:htm.
- [5] Hoffmann J, Brafman RI. Contingent Planning via Heuristic Forward Search with Implicit Belief States. In: Proceedings of the Fifteenth International Conference on International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press, 2005 pp. 71-88.
- [6] Bryce D, Kambhampati S, Smith DE. Planning Graph Heuristics for Belief Space Search. Journal of Artificial Intelligence Research (JAIR), 2006. 26:35-99. doi: 10.1613/jair.1869.
- [7] Albore A, Palacios H, Geffner H. A Translation-Based Approach to Contingent Planning. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI). 2009 pp. 1623-1628.
- [8] Muise C, Belle V, McIlraith SA. Computing Contingent Plans via Fully Observable Non-deterministic Planning. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI Press, 2014 pp. 2322-2329.
- [9] Rintanen J. Complexity of Planning with Partial Observability. In: Proceedings of the Fourteenth International Conference on International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press, 2004 pp. 345-354.
- [10] Schmidt-Rohr SR, Knoop S, Lösch M, Dillmann R. Reasoning for a multi-modal service robot considering uncertainty in human-robot interaction. In: Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction. IEEE, 2008 pp. 249-254. doi:10.1145/1349822.1349855.
- [11] Karami AB, Jeanpierre L, Mouaddib AI. Partially Observable Markov Decision Process for managing Robot Collaboration with Human. In: Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence. IEEE, 2009 pp. 518-521. doi:10.1109/ICTAI.2009.61.
- [12] Armstrong-Crews N, Veloso M. Oracular Partially Observable Markov Decision Processes: A very special case. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, 2007 pp. 2477-2482. doi:10.1109/ROBOT.2007.363691.
- [13] Cai C, Liao X, Carin L. Learning to explore and exploit in POMDPs. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems. 2009 pp. 198-206. URL http://papers:nips:cc/paper/3814-learning-to-explore-and-exploit-in-pomdps:pdf.
- [14] Doshi F, Pineau J, Roy N. Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs. In: Proceedings of the 25th International Conference on Machine learning. ACM, 2008 pp. 256-263. doi:10.1901/jaba.2008.301-256.
- [15] Jaulmes R, Pineau J, Precup D. Active learning in partially observable Markov Decision Processes. In: Proceedings of the 16th European conference on Machine Learning. Springer, 2005 pp. 601-608. doi:10.1007/11564096_59.
- [16] Kearns M, Singh S. Near-optimal reinforcement learning in polynomial time. Machine Learning, 2002. 49(2-3):209-232. doi:10.1023/A:1017984413808.
- [17] Rosenthal S, Veloso M, Dey AK. Learning accuracy and availability of humans who help mobile robots. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. AAAI Press, 2011 pp. 1501-1506.
- [18] Veloso M, Biswas J, Coltin B, Rosenthal S. Cobots: Robust Symbiotic Autonomous Mobile Service Robots. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI). 2015 pp. 4423-4429. URL https://www:ijcai:org/Abstract/15/656.
- [19] Rosenthal S, Biswas J, Veloso M. An Effective Personal Mobile Robot Agent Through Symbiotic Human-robot Interaction. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems, 2010 pp. 915-922. doi:10.1145/1838206.1838329.
- [20] Côté N, Canu A, Bouzid M, Mouaddib AI. Humans-robots sliding collaboration control in complex environments with adjustable autonomy. In: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 02. IEEE Computer Society, 2012 pp. 146-153. doi:10.1109/WI-IAT.2012.215.
- [21] Göbelbecker M, Keller T, Eyerich P, Brenner M, Nebel B. Coming up with good excuses: what to do when no plan can be found. In: Proceedings of the Twentieth International Conference on International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press, 2010 pp. 81-88.
- [22] Bonet B, Geffner H. Planning under partial observability by classical replanning: Theory and experiments. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, volume 22. 2011 p. 1936. doi:10.5591/978-1-57735-516-8/IJCAI11-324.
- [23] Hoffmann J, Nebel B. The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 2001. 14:253-302. doi:10.1613/jair.855.
- [24] Muise C, McIlraith SA, Beck JC. Improved non-deterministic planning by exploiting state relevance. In: Proceedings of the Twenty-Second International Conference on International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press, 2012 pp. 172-180. URL http://www:cs:toronto:edu/~sheila/publications/mui-mci-bec-icaps12:pdf.
- [25] Bonet B, Geffner H. Flexible and scalable partially observable planning with linear translations. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI Press, 2014 pp. 2235-2241.
- [26] Helmert M. The Fast Downward Planning System. Journal of Artificial Intelligence Research (JAIR), 2006. 26(1):191-246. URL https://doi:org/10:1613/jair:1705.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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