[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Risk-aware Decision-making in Human-multi-robot Collaborative Search: A Regret Theory Approach

  • Short Paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

The expected value (EV) based optimization principle often used in engineering ignores risk-related human characteristics which are however important to human-robot interaction (HRI). The characteristics include risk-perception and risk-attitudes which can be called risk-awareness collectively. In this work, we study the effects of risk-awareness in a human-multi-robot collaborative search task. In such a task, the correctness of robotic visual detection is uncertain, but the robots can request human assistance. Assume there is only one human in the team, the requesting robots must be ordered into a sequence. To optimize the ordering, we propose to construct a risk-aware cost function with an extended version of regret theory (RTx). RTx is a decision theory modeling risk-awareness and is backed by neuroscientific and psychological evidences. We cast the optimal ordering into multi-option choice problems and use RTx to make human-like risk-aware decisions. This optimal ordering is combinatorial optimization with a nonlinear cost function which is generally difficult to solve. However, we prove the properties of RTx enable simplification of the optimal ordering to a sorting problem which has fast off-the-shelf solvers. The simplification has two parts. One part concerns with ordering a fixed number of robots optimally. The other concerns with selecting a not-yet-ordered sequence of robots with the optimal length. We examine the RTx-based ordering in simulation and show risk-aware decision-making is more advantageous than EV-based decision-making. The results indicate that risk-awareness renders improved performance of robotic decision-making for HRI and RTx is a tractable embodiment of risk-awareness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

Data were generated by simulation and are available online at https://gitlab.com/I2RCLEMSON/rtx_hrc_search/.

Code Availability

Simulation code is available online at https://gitlab.com/I2RCLEMSON/rtx_hrc_search/.

References

  1. Abdellaoui, M., Bleichrodt, H., l’Haridon, O.: A tractable method to measure utility and loss aversion under prospect theory. Journal of Risk and Uncertainty 36(3), 245–266 (2008)

    Article  Google Scholar 

  2. Bechara, A., Damasio, A.R.: The somatic marker hypothesis: a neural theory of economic decision. Games and Economic Behavior 52(2), 336–372 (2005)

    Article  Google Scholar 

  3. Bell, D.E.: Regret in decision making under uncertainty. Operations Tesearch 30(5), 961–981 (1982)

    Article  Google Scholar 

  4. Dilokthanakul, N., Shanahan, M.: Deep reinforcement learning with risk-seeking exploration. In: International Conference on Simulation of Adaptive Behavior, pp. 201–211. Springer (2018)

  5. Dodge, S., Karam, L.: Human and DNN classification performance on images with quality distortions: a comparative study. ACM Transactions on Applied Perception (TAP) 16(2), 1–17 (2019)

    Article  Google Scholar 

  6. Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research 23(9), 939–954 (2004)

    Article  Google Scholar 

  7. Hakobyan, A., Kim, G.C., Yang, I.: Risk-aware motion planning and control using cvar-constrained optimization. IEEE Robotics and Automation Letters 4(4), 3924–3931 (2019)

    Article  Google Scholar 

  8. Huang, Z., Miyauchi, G., Gomez, A.S., Bird, R., Kalsi, A.S., Jansen, C., Liu, Z., Parsons, S., Sklar, E.: An experiment on human-robot interaction in a simulated agricultural task. In: Annual Conference Towards Autonomous Robotic Systems, pp. 221–233. Springer (2020)

  9. Jammernegg, W., Kischka, P.: Risk-averse and risk-taking newsvendors: A conditional expected value approach. Rev. Manag. Sci. 1(1), 93–110 (2007)

    Article  Google Scholar 

  10. Jessup, R.K., Bishara, A.J., Busemeyer, J.R.: Feedback produces divergence from prospect theory in descriptive choice. Psychol. Sci. 19(10), 1015–1022 (2008)

    Article  Google Scholar 

  11. Jiang, L., Wang, Y.: A human-computer interface design for quantitative measure of regret theory. IFAC-PapersOnLine 51(34), 15–20 (2019)

    Article  Google Scholar 

  12. Jiang, L., Wang, Y.: A personalized computational model for human-like automated decision-making. IEEE Transactions on Automation Science and Engineering (2021)

  13. Jin, I.G., Schürmann, B., Murray, R.M., Althoff, M.: Risk-aware motion planning for automated vehicle among human-driven cars. In: 2019 American Control Conference (ACC), pp. 3987–3993. IEEE (2019)

  14. Kahneman, D., Lovallo, D.: Timid choices and bold forecasts: a cognitive perspective on risk taking. Management Science 39(1), 17–31 (1993)

    Article  Google Scholar 

  15. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–292 (1979)

    Article  MathSciNet  Google Scholar 

  16. Kontek, K., Lewandowski, M.: Range-dependent utility. Manag. Sci. 64(6), 2812–2832 (2017)

    Article  Google Scholar 

  17. Korte, B., Vygen, J., Korte, B., Vygen, J.: Combinatorial optimization, vol. 2 Springer (2012)

  18. Kwon, M., Biyik, E., Talati, A., Bhasin, K., Losey, D.P., Sadigh, D.: When humans aren’t optimal: robots that collaborate with risk-aware humans. In: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 43–52 (2020)

  19. Lebiere, C., Jentsch, F., Ososky, S.: Cognitive models of decision making processes for human-robot interaction. In: International Conference on Virtual, Augmented and Mixed Reality, pp. 285–294. Springer (2013)

  20. Levy, I.: Neuroanatomical substrates for risk behavior. Neuroscientist 23(3), 275–286 (2017)

    Article  Google Scholar 

  21. Liao, Z., Jiang, L., Wang, Y.: A quantitative measure of regret in decision-making for human-robot collaborative search tasks (2017)

  22. Liu, Y., Nejat, G.: Robotic urban search and rescue: a survey from the control perspective. J. Intell. Robot. Syst. 72(2), 147–165 (2013)

    Article  Google Scholar 

  23. Loomes, G., Sugden, R.: Regret theory: an alternative theory of rational choice under uncertainty. The economic journal 92(368), 805–824 (1982)

    Article  Google Scholar 

  24. Majumdar, A., Pavone, M.: How should a robot assess risk? towards an axiomatic theory of risk in robotics. In: Robotics Research, pp. 75–84. Springer (2020)

  25. Markowitz, H.M.: Foundations of portfolio theory. The journal of finance 46(2), 469–477 (1991)

    Article  Google Scholar 

  26. Moerland, T.M., Broekens, J., Jonker, C.M.: Model-based reinforcement learning: a survey. arXiv:2006.16712 (2020)

  27. Mokhtari, K., Lang, K.A., Wagner, A.R.: Don’t go that way! risk-aware decision making for autonomous vehicles (2020)

  28. Monga, A., Zor, O.: Time versus money. Current Opinion in Psychology 26, 28–31 (2019)

    Article  Google Scholar 

  29. Nishimura, H., Ivanovic, B., Gaidon, A., Pavone, M., Schwager, M.: Risk-sensitive sequential action control with multi-modal human trajectory forecasting for safe crowd-robot interaction. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11205–11212. IEEE (2020)

  30. Novin, R.S., Yazdani, A., Merryweather, A., Hermans, T.: Risk-aware decision making in service robots to minimize risk of patient falls in hospitals. arXiv:2010.08124 (2020)

  31. Nyberg, T., Pek, C., Dal Col, L., Norén, C., Tumova, J.: Risk-aware motion planning for autonomous vehicles with safety specifications. In: IEEE Intelligent Vehicles Symposium (2021)

  32. Park, H., Hutchinson, S.: Robust rendezvous for multi-robot system with random node failures: an optimization approach. Auton. Robot. 42(8), 1807–1818 (2018)

    Article  Google Scholar 

  33. Prelec, D.: The probability weighting function. Econometrica pp. 497–527 (1998)

  34. Queralta, J.P., Taipalmaa, J., Pullinen, B.C., Sarker, V.K., Gia, T.N., Tenhunen, H., Gabbouj, M., Raitoharju, J., Westerlund, T.: Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access 8, 191617–191643 (2020)

    Article  Google Scholar 

  35. Quiggin, J.: A theory of anticipated utility. J. Econ. Behav. Organ. 3(4), 323–343 (1982)

    Article  Google Scholar 

  36. Quiggin, J.: Regret theory with general choice sets. J. Risk Uncertain. 8(2), 153–165 (1994)

    Article  MathSciNet  Google Scholar 

  37. Rudolph, M., Chernova, S., Ravichandar, H.: Desperate times call for desperate measures: towards risk-adaptive task allocation. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2592–2597. IEEE (2021)

  38. Singh, S., Lacotte, J., Majumdar, A., Pavone, M.: Risk-sensitive inverse reinforcement learning via semi-and non-parametric methods. The International Journal of Robotics Research 37(13-14), 1713–1740 (2018)

    Article  Google Scholar 

  39. Somasundaram, J., Diecidue, E.: Regret theory and risk attitudes. Journal of Risk and Uncertainty 55(2-3), 147–175 (2017)

    Article  Google Scholar 

  40. de Souza, P.E., Chanel, C.P., Mailliez, M., Dehais, F.: Predicting human operator’s decisions based on prospect theory. Interact. Comput. 32(3), 221–232 (2020)

    Article  Google Scholar 

  41. Starmer, C.: Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk. Journal of Economic Literature 38(2), 332–382 (2000)

    Article  Google Scholar 

  42. Stearns, S.C.: Daniel bernoulli (1738): evolution and economics under risk. Journal of Biosciences 25(3), 221–228 (2000)

    Article  Google Scholar 

  43. Tversky, A., Fox, C.R.: Weighing risk and uncertainty. Psychological review 102(2), 269 (1995)

    Article  Google Scholar 

  44. Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4), 297–323 (1992)

    Article  Google Scholar 

  45. Wang, Y.: Regret-based automated decision-making aids for domain search tasks using human-agent collaborative teams. IEEE Transactions on Control Systems Technology 24(5), 1680–1695 (2016)

    Article  Google Scholar 

  46. Weber, E.U., Milliman, R.A.: Perceived risk attitudes: relating risk perception to risky choice. Management Science 43(2), 123–144 (1997)

    Article  Google Scholar 

  47. Yang, F., Chakraborty, N.: Algorithm for optimal chance constrained linear assignment. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 801–808. IEEE (2017)

  48. You, H., Zhang, M., Wang, D.H.: Neural mechanism underlying risk attitude and probability distortion: one two-stage model of valuation and choice. Neurocomputing 375, 32–42 (2020)

    Article  Google Scholar 

  49. Zhou, L., Tokekar, P.: Risk-aware submodular optimization for multi-robot coordination. arXiv:2003.10492 (2020)

Download references

Funding

This research is supported by the National Science Foundation of USA under Grant No. CMMI-1454139.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Clemson University, Clemson, SC, 29634, USA

    Longsheng Jiang & Yue Wang

Authors
  1. Longsheng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Longsheng Jiang contributed to the study conception and design under the supervision and advising of Yue Wang. Mathematical derivation, simulation programming, data collection and analysis were performed by Longsheng Jiang. The first draft of the manuscript was written by Longsheng Jiang and both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Yue Wang.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose. Regarding employment, Longsheng Jiang is a graduate student at Clemson University and Yue Wang is a faculty member in Department of Mechanical Engineering at Clemson University.

Additional information

Ethical Approval

This study reports human subject data collected from a previous study [12] which was approved by the Institutional Review Board of Clemson University (No. IRB 2013-289). The procedures used in the study adhere to the tenets of the Declaration of Helsinki.

Consent to Participate

This study reports data of 3 human subjects in our previous study [12] in which informed consent was obtained from all individual participants.

Consent to Publish

The authors affirm that human participants provided informed consent for publication of the data in Fig. ??.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, L., Wang, Y. Risk-aware Decision-making in Human-multi-robot Collaborative Search: A Regret Theory Approach. J Intell Robot Syst 105, 40 (2022). https://doi.org/10.1007/s10846-022-01642-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01642-z

Keywords

Mathematics Subject Classification 2010

Search

Navigation