Towards explainable multi-objective probabilistic planning
Abstract
Use of multi-objective probabilistic planning to synthesize behavior of CPSs can play an important role in engineering systems that must self-optimize for multiple quality objectives and operate under uncertainty. However, the reasoning behind automated planning is opaque to end-users. They may not understand why a particular behavior is generated, and therefore not be able to calibrate their confidence in the systems working properly. To address this problem, we propose a method to automatically generate verbal explanation of multi-objective probabilistic planning, that explains why a particular behavior is generated on the basis of the optimization objectives. Our explanation method involves describing objective values of a generated behavior and explaining any tradeoff made to reconcile competing objectives. We contribute: (i) an explainable planning representation that facilitates explanation generation, and (ii) an algorithm for generating contrastive justification as explanation for why a generated behavior is best with respect to the planning objectives. We demonstrate our approach on a mobile robot case study.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The present survey was undertaken as a systematic literature review following the guidelines by Kitchenham and Charters, Kitchenham et al., and Wohlin.
“The present survey has been undertaken as a systematic literature review following the guidelines by Kitchenham and Charters [58], Kitchenham et al. [59], and Wohlin [60].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [60]
Cites
Decisions with Multiple Objectives: Preferences and Value Tradeoffs.
Tsoukias, A., Keeney, R. L., & Raiffa, H. (1994) · Journal of the Operational Research Society · doi:10.2307/2584151 · Cited by 8,414 · see citing works
Decisions with Multiple Objectives
Keeney, R. L., & Raiffa, H. (1993) · Cambridge University Press eBooks · doi:10.1017/cbo9781139174084 · Cited by 4,449 · see citing works
Decisions with Multiple Objectives: Preferences and Value Trade-Offs
Keeney, R. L., Raiffa, H., & Rajala, D. W. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310245 · Cited by 1,789 · see citing works
<i>Why and why not</i> explanations improve the intelligibility of context-aware intelligent systems
Lim, B. Y., Dey, A. K., & Avrahami, D. (2009) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1519023 · Cited by 552 · see citing works
An Analysis of Stochastic Shortest Path Problems
Bertsekas, D. P., & Tsitsiklis, J. N. (1991) · Mathematics of Operations Research · doi:10.1287/moor.16.3.580 · Cited by 502 · see citing works
Too much, too little, or just right? Ways explanations impact end users' mental models
Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., & Wong, W.-K. (2013) · 2013 IEEE Symposium on Visual Languages and Human Centric Computing · doi:10.1109/vlhcc.2013.6645235 · Cited by 388 · see citing works
Improving Robot Controller Transparency Through Autonomous Policy Explanation
Hayes, B., & Shah, J. A. (2017) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/2909824.3020233 · Cited by 359 · see citing works
Assessing demand for intelligibility in context-aware applications
Lim, B. Y., & Dey, A. K. (2009) · Proceedings of the 11th international conference on Ubiquitous computing · doi:10.1145/1620545.1620576 · Cited by 246 · see citing works
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Kulesza, T., Stumpf, S., Burnett, M., & Kwan, I. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207678 · Cited by 225 · see citing works
Toolkit to support intelligibility in context-aware applications
Lim, B. Y., & Dey, A. K. (2010) · Proceedings of the 12th ACM international conference on Ubiquitous computing · doi:10.1145/1864349.1864353 · Cited by 143 · see citing works
Minimal Sufficient Explanations for Factored Markov Decision Processes
Khan, O., Poupart, P., & Black, J. P. (2009) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v19i1.13365 · Cited by 77 · see citing works
Making Hybrid Plans More Clear to Human Users - A Formal Approach for Generating Sound Explanations
Seegebarth, B., Müller, F., Schattenberg, B., & Biundo, S. (2012) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v22i1.13503 · Cited by 77 · see citing works
An MDP Approach for Explanation Generation.
Elizalde, F., Sucar, L. E., Reyes, A., et al. (2007) · Cited by 17 · see citing works
Explanations in Context-Aware Systems.
Dey, A. K. (2009) · Explanation-aware Computing · Cited by 16 · see citing works
Verbal Plan Explanations for Hybrid Planning
Bidot, J., Biundo, S., Heinroth, T., et al. (2010) · Multikonferenz Wirtschaftsinformatik · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works
“In contrast, multiple-sentence explanations provide end-users with specific details for the given decision [153], [156], [157], [161] or explain multiple decisions at once [146].”
BackgroundInfluentialExplainable goal-driven agents and robots-a comprehensive review
Sado, F., Loo, C. K., Liew, W. S., Kerzel, M., & Wermter, S. (2023) · ACM Computing Surveys · doi:10.1145/3564240 · Cited by 79 · see citing works
Interactive explanation for planning-based systems: WIP abstract
Zhao, E., & Sukkerd, R. (2019) · Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems · doi:10.1145/3302509.3313322 · Cited by 7 · see citing works
Self-citation
- Authors
- Sukkerd, R., Simmons, R., & Garlan, D.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 4th International Workshop on Software Engineering for Smart Cyber-Physical Systems
- Publisher
- ACM
- Published
- 2018-05-27
- Pages
- 19-25
- OpenAlex
- W2884533761
- Cited by
- 27
- References
- 15
- Citations by year
- 2024: 2 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 6 · 2019: 3
- FWCI
- 1.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- explainable planning, multi-objective planning, probabilistic planning, automated planning, planning under uncertainty, cyber-physical systems, mobile robot navigation, planning representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
