Abstract
We consider the problem of deploying robots to observe the evolution of a stochastic process in order to output a sequence of observations that fit some given specification. This problem often arises in contexts such as event reporting, situation depiction, and automated narrative generation. The paper extends our prior work by formulating and examining the multi-robot case: a team of robots move about, each recording what they observe, and, if they manage to capture some event, communicating that fact with the group. In the end, all events from all the robots are collated to provide a cumulative output. A plan is used to decide what each robot will attempt to capture next, based on the state of the world and the events that have been captured (collectively) so far. This paper focuses on the question of how to compute effective multi-robot plans. A monolithic treatment, involving the optimal selection of joint choices, i.e., choosing the next elements to attempt to capture by all robots, is formulated where costs are minimized in an expected sense. Since such plans are prohibitive to compute, variants based on an approximation scheme based on solving a sequence of individual planning problems are then introduced. This scheme sacrifices some solution quality but requires far less computational expense; we show this permits one to scale to greater numbers of robots.
This material is based upon work supported by the National Science Foundation under Grants 1849249 & 1849291.
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.
Being consistent with that above, we use \(a^{(j)}\) to denote the \(j^{\text {th}}\) element of \(\mathbf {a}\).
References
Rahmani, H., Shell, D.A., O’Kane, J.M.: Planning to chronicle. In: Workshop on the Algorithmic Foundations of Robotics (WAFR XIV) (2020)
Shell, D.A., Huang, L., Becker, A.T., O’Kane, J.M.: Planning coordinated event observation for structured narratives. In: IEEE ICRA (2019)
Yu, J., LaValle, S.M.: Story validation and approximate path inference with a sparse network of heterogeneous sensors. In: IEEE ICRA (2011)
Lee, Y.J., Ghosh, J., Grauman, K.: Discovering important people and objects for egocentric video summarization. In: IEEE CVPR (2012)
Hong, R., Tang, J., Tan, H.-K., Ngo, C.-W., Yan, S., Chua, T.-S.: Beyond search: event-driven summarization for web videos. ACM Trans. Multimed. Comput. Commun. Appl. 7(4), 35 (2011)
Potapov, D., Douze, M., Harchaoui, Z., Schmid, C.: Category-specific video summarization. In: ECCV (2014)
Feng, L., Li, Z., Kuang, Z., Zhang, W.: Extractive video summarizer with memory augmented neural networks. In: ACM Multimedia (2018)
Chang, P., Han, M., Gong, Y.: Extract highlights from baseball game video with hidden Markov models. In: IEEE ICIP (2002)
Kolekar, M.H., Sengupta, S.: Event-importance based customized and automatic cricket highlight generation. In: IEEE ICME (2006)
Girdhar, Y., Giguere, P., Dudek, G.: Autonomous adaptive exploration using realtime online spatiotemporal topic modeling. Int. J. Robot. Res. 33(4), 645–657 (2014)
H. Hajishirzi, J. Hockenmaier, E. T. Mueller, and E. Amir, “Reasoning in Robocup Soccer Narratives,” in UAI, 2011
Rosenthal, S., Selvaraj, S.P., Veloso, M.: Verbalization: narration of autonomous robot experience. In: IJCAI (2016)
Riedl, M.O., Young, R.M.: Narrative planning: balancing plot and character. J. Artif. Intell. Res. 39, 217–268 (2010)
Barot, C., et al.: Bardic: generating multimedia narrative reports for game logs. In: Working Notes of the AIIDE Workshop on Intelligent Narrative Technologies (2017)
Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory. Languages and Computation. Adison-Wesley. Reading, Mass (1979)
Puterman, M.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Hoboken (1994)
Ephrati, E., Rosenschein, J.S.: Divide and conquer in multi-agent planning. In: AAAI-94 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chaudhuri, D., Rahmani, H., Shell, D.A., O’Kane, J.M. (2022). Tractable Planning for Coordinated Story Capture: Sequential Stochastic Decoupling. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-92790-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92789-9
Online ISBN: 978-3-030-92790-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)