Abstract
Plan reconstruction is a task that appears in plan recognition and learning by practice. The document introduces a method of building a goal graph, which holds a reconstructed plan structure, based on analysis of data provided by observation of an agent. The approach is STRIPS-free and uses only a little knowledge about an environment. The process is automatic, thus, it does not require manual annotation of observations. The paper provides details of the developed algorithm. In the experimental study properties of a goal graph were evaluated. Possible application areas of the method are described.
Chapter PDF
References
Gu, W., Ren, H., Li, B., Liu, Y., Liu, S.: Adversarial Plan Recognition and Opposition Based Tactical Plan Recognition. In: International Conference on Machine Learning and Cybernetics, pp. 499–504 (2006)
Gu, W., Yin, J.: The Recognition and Opposition to Multiagent Adversarial Planning. In: International Conference on Machine Learning and Cybernetics, pp. 2759–2764 (2006)
Camilleri, G.: A generic formal plan recognition theory. In: International Conference on Information Intelligence and Systems, pp. 540–547 (1999)
Takahashi, T., Takeuchi, I., Matsuno, F., Tadokoro, S.: Rescue simulation project and comprehensive disaster simulator architecture. In: International Conference on Intelligent Robots and Systems, vol. 3, pp. 1894–1899 (2000)
Takahashi, T., Tadokoro, S.: Working with robots in disasters. IEEE Robotics and Automation Magazine 9(3), 34–39 (2002)
Chernova, S., Orkin, J., Breazel, C.: Crowdsourcing HRI through Online Multiplayer Games. In: Proceedings of AAAI Fall Symposium on Dialog with Robots, pp. 14–19 (2010)
Wang, X.: Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition. In: Proceedings of the 12th International Conference on Machine Learning, pp. 549–557 (1995)
Ehsaei, M., Heydarzadeh, Y., Aslani, S., Haghighat, A.: Pattern-Based Planning System (PBPS): A novel approach for uncertain dynamic multi-agent environments. In: 3rd International Symposium on Wireless Pervasive Computing, pp. 524–528 (2008)
Nejati, N., Langley, P., Konik, T.: Learning Hierarchical Task Networks by Observation. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 665–672 (2006)
Sacerdoti, E.D.: A Structure For Plans and Behavior. In: Artificial Intelligence Center. Elsevier North-Holland, Technical Note 109 (1977)
Fikes, R., Nilsson, N.: STRIPS: a new approach to the application of theorem proving to problem solving. In: IJCAI, pp. 608–620 (1971)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 IFIP International Federation for Information Processing
About this paper
Cite this paper
Dzieńkowski, B.J., Markowska-Kaczmar, U. (2012). Plan and Goal Structure Reconstruction: An Automated and Incremental Method Based on Observation of a Single Agent. In: Cortesi, A., Chaki, N., Saeed, K., Wierzchoń, S. (eds) Computer Information Systems and Industrial Management. CISIM 2012. Lecture Notes in Computer Science, vol 7564. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33260-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-33260-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33259-3
Online ISBN: 978-3-642-33260-9
eBook Packages: Computer ScienceComputer Science (R0)