67,587 research outputs found
Landmark-based approaches for goal recognition as planning
This article is a revised and extended version of two papers published at AAAI 2017 (Pereira et al., 2017b) and ECAI 2016 (Pereira and Meneguzzi, 2016). We thank the anonymous reviewers that helped improve the research in this article. The authors thank Shirin Sohrabi for discussing the way in which the algorithms of Sohrabi et al. (2016) should be configured, and Yolanda Escudero-Martın for providing code for the approach of E-Martın et al. (2015) and engaging with us. We also thank Miquel Ramırez and Mor Vered for various discussions, and Andre Grahl Pereira for a discussion of properties of our algorithm. Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1.Peer reviewedPostprin
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?
Goal recognition is an important problem in many application domains (e.g.,
pervasive computing, intrusion detection, computer games, etc.). In many
application scenarios, it is important that goal recognition algorithms can
recognize goals of an observed agent as fast as possible. However, many early
approaches in the area of Plan Recognition As Planning, require quite large
amounts of computation time to calculate a solution. Mainly to address this
issue, recently, Pereira et al. developed an approach that is based on planning
landmarks and is much more computationally efficient than previous approaches.
However, the approach, as proposed by Pereira et al., also uses trivial
landmarks (i.e., facts that are part of the initial state and goal description
are landmarks by definition). In this paper, we show that it does not provide
any benefit to use landmarks that are part of the initial state in a planning
landmark based goal recognition approach. The empirical results show that
omitting initial state landmarks for goal recognition improves goal recognition
performance.Comment: Will be presented at KI 202
An LP-Based Approach for Goal Recognition as Planning
Goal recognition aims to recognize the set of candidate goals that are
compatible with the observed behavior of an agent. In this paper, we develop a
method based on the operator-counting framework that efficiently computes
solutions that satisfy the observations and uses the information generated to
solve goal recognition tasks. Our method reasons explicitly about both partial
and noisy observations: estimating uncertainty for the former, and satisfying
observations given the unreliability of the sensor for the latter. We evaluate
our approach empirically over a large data set, analyzing its components on how
each can impact the quality of the solutions. In general, our approach is
superior to previous methods in terms of agreement ratio, accuracy, and spread.
Finally, our approach paves the way for new research on combinatorial
optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final
authorship and tex
- âŠ