Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-25T15:46:58.275Z Has data issue: false hasContentIssue false
  • English
  • Français

Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

Published online by Cambridge University Press:  20 September 2019

YI WANG ,
SHIQI ZHANG  and
JOOHYUNG LEE Open the ORCID record for JOOHYUNG LEE [Opens in a new window]
YI WANG
Affiliation:
Arizona State University, USA
SHIQI ZHANG
Affiliation:
SUNY Binghamton, USA
JOOHYUNG LEE
Affiliation:
Arizona State University, USA (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called interleaved commonsense reasoning and probabilistic planning (icorpp), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of icorpp is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate icorpp’s reasoning and planning components. In particular, we extend probabilistic action language pBC+ to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system pbcplus2pomdp, which compiles a pBC+ action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the pBC+ action description. Our experiments show that it retains the advantages of icorpp while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.

Keywords

Action LanguagePOMDPProbabilistic Logic ProgrammingCommonsense ReasoningProbabilistic Planning
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 1090 - 1106
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amiri, S., Wei, S., Zhang, S., Sinapov, J., Thomason, J., and Stone, P. 2018. Multi-modal predicate identification using dynamically learned robot controllers. In Proceedings of the 27th International Joint Conference on Artificial Intelligence.Google Scholar
Babb, J. and Lee, J. 2015. Action language BC+. Journal of Logic and Computation, exv062.Google Scholar
Baral, C., Gelfond, M., and Rushton, J. N. 2009. Probabilistic reasoning with answer sets. Theory and Practice of Logic Programming 9, 1, 57–144.Google Scholar
Baral, C., Tran, N., and Tuan, L.-C. 2002. Reasoning about actions in a probabilistic setting. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 507–512.Google Scholar
Chitnis, R., Kaelbling, L. P., and Lozano-Pérez, T. 2018. Integrating human-provided information into belief state representation using dynamic factorization. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3551–3558. IEEE.Google Scholar
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., and Turner, H. 2004. Nonmonotonic causal theories. Artificial Intelligence 153(1–2), 49–104.Google Scholar
Hanheide, M., Göbelbecker, M., Horn, G. S., Pronobis, A., Sjöö, K., Aydemir, A., Jensfelt, P., Gretton, C., Dearden, R., Janicek, M., et al. 2017. Robot task planning and explanation in open and uncertain worlds. Artificial Intelligence 247, 119–150.Google Scholar
Jiang, Y., Yang, F., Zhang, S., and Stone, P. 2018. Integrating task-motion planning with reinforcement learning for robust decision making in mobile robots. CoRR abs/1811.08955.Google Scholar
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R. 1998. Planning and acting in partially observable stochastic domains. Artificial intelligence 101, 1-2, 99–134.Google Scholar
Kim, B., Kaelbling, L. P., and Lozano-Perez, T. 2019. Adversarial actor-critic method for task and motion planning problems using planning experience. In AAAI Conference on Artificial Intelligence (AAAI).Google Scholar
Lee, J., Lifschitz, V., and Yang, F. 2013. Action language BC: Preliminary report. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI).Google Scholar
Lee, J., Talsania, S., and Wang, Y. 2017. Computing LPMLN using ASP and MLN solvers. Theory and Practice of Logic Programming.Google Scholar
Lee, J. and Wang, Y. 2016. Weighted rules under the stable model semantics. In Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 145–154.Google Scholar
Lee, J. and Wang, Y. 2018. A probabilistic extension of action language BC+. Theory and Practice of Logic Programming 18(3–4), 607–622.Google Scholar
Leonetti, M., Iocchi, L., and Stone, P. 2016. A synthesis of automated planning and reinforcement learning for efficient, robust decision-making. Artificial Intelligence 241, 103–130.Google Scholar
Lu, K., Zhang, S., Stone, P., and Chen, X. 2018. Robot representing and reasoning with knowledge from reinforcement learning. CoRR abs/1809.11074.Google Scholar
Lyu, D., Yang, F., Liu, B., and Gustafson, S. 2019. Sdrl: Interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. In AAAI.Google Scholar
Puterman, M. L. 2014. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.Google Scholar
Sridharan, M., Gelfond, M., Zhang, S., and Wyatt, J. 2019. REBA: A refinement-based architecture for knowledge representation and reasoning in robotics. Journal of Artificial Intelligence Research 65, 87–180.Google Scholar
Sutton, R. S. and Barto, A. G. 2018. Reinforcement learning: An introduction. MIT press.Google Scholar
Tran, N. and Baral, C. 2004. Encoding probabilistic causal model in probabilistic action language. In Proceedings of the National Conference on Artificial Intelligence.Google Scholar
Veiga, T. S., Silva, M., Ventura, R., and Lima, P. U. 2019. A hierarchical approach to active semantic mapping using probabilistic logic and information reward pomdps. In Proceedings of the International Conference on Automated Planning and Scheduling.Google Scholar
Wang, Y. and Lee, J. 2019. Elaboration tolerant representation of markov decision process via decision theoretic extension of action language pbc+. In LPNMR. To appear.Google Scholar
Yang, F., Lyu, D., Liu, B., and Gustafson, S. 2018. Peorl: integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 4860–4866.Google Scholar
Zhang, S., Khandelwal, P., and Stone, P. 2017. Dynamically constructed (PO)MDPs for adaptive robot planning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.Google Scholar
Zhang, S., Sridharan, M., and Wyatt, J. L. 2015. Mixed logical inference and probabilistic planning for robots in unreliable worlds. IEEE Transactions on Robotics 31, 3, 699–713.CrossRefGoogle Scholar
Zhang, S. and Stone, P. 2015. CORPP: Commonsense reasoning and probabilistic planning, as applied to dialog with a mobile robot. In Twenty-Ninth AAAI Conference on Artificial Intelligence.Google Scholar
Supplementary material: PDF

Wang et al. supplementary material

Online Appendix

Download Wang et al. supplementary material(PDF)
PDF 327.5 KB