333 research outputs found
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
Landmark-Based Heuristics for Goal Recognition
6 volumes http://www.aaai.org/Press/Proceedings/aaai17.phpPublisher PD
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
Iterative Depth-First Search for Fully Observable Non-Deterministic Planning
Fully Observable Non-Deterministic (FOND) planning models uncertainty through
actions with non-deterministic effects. Existing FOND planning algorithms are
effective and employ a wide range of techniques. However, most of the existing
algorithms are not robust for dealing with both non-determinism and task size.
In this paper, we develop a novel iterative depth-first search algorithm that
solves FOND planning tasks and produces strong cyclic policies. Our algorithm
is explicitly designed for FOND planning, addressing more directly the
non-deterministic aspect of FOND planning, and it also exploits the benefits of
heuristic functions to make the algorithm more effective during the iterative
searching process. We compare our proposed algorithm to well-known FOND
planners, and show that it has robust performance over several distinct types
of FOND domains considering different metrics
Editorial : Advances in Goal, Plan and Activity Recognition
Funding Information: The editors would like to thank the authors and reviewers for their time and effort and also for providing new insights and reflections into the growing field of goal recognition research. We are indebted to Dr. Marta Compigotto, Senior Journal Specialist, and her editorial team for their editorial assistance.Peer reviewedPublisher PD
Uncertain Machine Ethical Decisions Using Hypothetical Retrospection
We propose the use of the hypothetical retrospection argumentation procedure,
developed by Sven Hansson, to improve existing approaches to machine ethical
reasoning by accounting for probability and uncertainty from a position of
Philosophy that resonates with humans. Actions are represented with a branching
set of potential outcomes, each with a state, utility, and either a numeric or
poetic probability estimate. Actions are chosen based on comparisons between
sets of arguments favouring actions from the perspective of their branches,
even those branches that led to an undesirable outcome. This use of arguments
allows a variety of philosophical theories for ethical reasoning to be used,
potentially in flexible combination with each other. We implement the
procedure, applying consequentialist and deontological ethical theories,
independently and concurrently, to an autonomous library system use case. We
introduce a a preliminary framework that seems to meet the varied requirements
of a machine ethics system: versatility under multiple theories and a resonance
with humans that enables transparency and explainability
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