Abstract
Open environments require dynamic execution of plans where agents must engage in settings that include, for example, re-planning, plan reusing, plan repair, etc. Hence, real-life Plan Recognition (PR) approaches are required to deal with different classes of observations (e.g., exogenous actions, switching between activities, and missing observations). Many approaches to PR consider these classes of observations, but none have dealt with them as deliberated events. Actually, using existing PR methods to explain such classes of observations may generate only so-called imperfect explanations (plans that partially explain a sequence of observations). Our overall approach is to leverage (in the sense of plan editing) imperfect explanations by exploiting new classes of observations. We use the notation of capabilities in the well-known Belief-Desire-Intention (BDI) agents programming as an ideal platform to discuss our work. To validate our approach, we show the implementation of our approach using practical examples from the Monroe Plan Corpus.
A. Ghose—Passed away prior to the submission of the manuscript. This is one of the last contributions by Aditya Ghose.
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Notes
- 1.
The source code for XPlaM Toolkit (including the code for the approach presented here) has been published online at https://github.com/dsl-uow/xplam.
- 2.
We published the datasets supporting the conclusions of this work online at https://www.kaggle.com/datasets/alelaimat/xplam.
- 3.
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Alelaimat, A., Ghose, A., Dam, H.K. (2023). Leveraging Imperfect Explanations for Plan Recognition Problems. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023. Lecture Notes in Computer Science(), vol 14127. Springer, Cham. https://doi.org/10.1007/978-3-031-40878-6_13
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