Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Eastwood, Tai Chi Minh Ralph; Alexander, Robert David; Kelly, Timothy Patrick (2016)
Languages: English
Types: Unknown
Autonomous planning in safety critical systems is a difficult task where decisions must carefully balance optimisation for performance goals of the system while also keeping the system away from safety hazards. These tasks often conflict, and hence present a challenging multi-objective planning problem where at least one of the objectives relates to safety risk. Recasting safety risk into an objective introduces additional requirements on planning algorithms: safety risk cannot be "averaged out" nor can it be combined with other objectives without loss of information and losing its intended purpose as a tool in risk reduction. Thus, existing algorithms for multi-objective planning cannot be used directly as they do not provide any facility to accurately track and update safety risk. A common work around is to restrict available decisions to those guaranteed safe a priori, but this can be overly conservative and hamper performance significantly. In this paper, we propose a planning algorithm based on multiobjective Monte-Carlo Tree Search to resolve these problems by recognising safety risk as a first class objective. Our algorithm explicitly models the safety of the system separately from the performance of the system, uses safety risk to both optimise and provide constraints for safety in the planning process, and uses an ALARP-based preference selection method to choose an appropriate safe plan from its output. The preference selection method chooses from the set of multiple safe plans to weigh risk against performance. We demonstrate the behaviour of the algorithm using an example representative of safety critical decision-making.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] D. M. Roijers, P. Vamplew, S. Whiteson, and R. Dazeley, “A survey of multi-objective sequential decision-making,” Journal of Artificial Intelligence Research, vol. 48, pp. 67-113, 2013.
    • [2] W. Wang, M. Sebag et al., “Multi-objective monte-carlo tree search,” in Asian conference on machine learning, 2012.
    • [3] Health and S. Executive, Reducing Risks, Protecting People: HSE's Decision-Making Process. HSE, 2001.
    • [4] E. A. Hansen, “Indefinite-horizon pomdps with action-based termination,” in Proceedings of the 22nd AAAI Conference on Artificial Intelligence, ser. AAAI'07, vol. 2. AAAI Press, 2007, pp. 1237- 1242.
    • [5] C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A survey of monte carlo tree search methods,” Computational Intelligence and AI in Games, IEEE Transactions on, vol. 4, no. 1, pp. 1-43, 2012.
    • [6] G. Chaslot, S. Bakkes, I. Szita, and P. Spronck, “Monte-Carlo Tree Search: A New Framework for Game AI,” in AIIDEC-08, 2008.
    • [7] D. Silver and J. Veness, “Monte-Carlo planning in large POMDPs,” in Advances in Neural Information Processing Systems, 2010, pp. 2164-2172.
    • [8] W. Wang, “Multi-objective sequential decision making,” Ph.D. dissertation, Universit Paris-Sud, 2014.
    • [9] International Electrotechnical Commission (IEC), Functional safety of electrical/electronic/programmable electronic safety related systems (IEC 61508). IEC, 2000.
    • [10] L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, “Planning and acting in partially observable stochastic domains,” Artificial intelligence, vol. 101, no. 1, pp. 99-134, 1998.
    • [11] V. B. Zubek and T. Dietterich, “A pomdp approximation algorithm that anticipates the need to observe,” in Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, ser. PRICAI'00. Berlin, Heidelberg: Springer-Verlag, 2000, pp. 521-532.
    • [12] H. Ren, A. A. Bitaghsir, and M. Barley, “Safe stochastic planning: Planning to avoid fatal states,” in Safety and Security in Multiagent Systems. Springer, 2009, pp. 101-115.
    • [13] E. A. Hansen and Z. Feng, “Dynamic programming for pomdps using a factored state representation,” in AIPS, 2000, pp. 130-139.
    • [14] A. Kolobov, “Scalable methods and expressive models for planning under uncertainty,” Ph.D. dissertation, University of Washington, 2013.
    • [15] D. Bryce, W. Cushing, and S. Kambhampati, “Probabilistic planning is multi-objective,” Arizona State University, Tech. Rep. ASU-CSE07-006, 2007.
    • [16] E. Zitzler and S. Ku¨nzli, “Indicator-based selection in multiobjective search,” in Parallel Problem Solving from Nature-PPSN VIII. Springer, 2004, pp. 832-842.
    • [17] D. Seward, C. Pace, and R. Agate, “Safe and effective navigation of autonomous robots in hazardous environments,” Autonomous Robots, vol. 22, no. 3, pp. 223-242, 2007.
  • No related research data.
  • No similar publications.
  • BioEntity Site Name

Share - Bookmark

Cite this article