Publication
Online action recognition
Conference Article
Conference
AAAI Conference on Artificial Intelligence (AAAI)
Edition
35th
Pages
11981-11989
Doc link
https://ojs.aaai.org/index.php/AAAI/article/view/17423
File
Authors
Projects associated
Abstract
Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e.g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consists in recognizing, in an open world, the planning action that best explains a partially observable state transition from a knowledge library of first-order STRIPS actions, which is initially empty. We frame this as an optimization problem, and propose two algorithms to address it: Action Unification (AU) and Online Action Recognition through Unification (OARU). The former builds on logic unification and generalizes two input actions using weighted partial MaxSAT. The latter looks for an action within the library that explains an observed transition. If there is such action, it generalizes it making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts a Trivial Grounded Action (TGA) in the library that explains just that transition. We report results on benchmarks from the International Planning Competition and PDDLGym, where OARU recognizes actions accurately with respect to expert knowledge, and shows real-time performance.
Categories
knowledge engineering, learning (artificial intelligence), planning (artificial intelligence).
Author keywords
Activity and Plan Recognition, Constraint Satisfaction and Optimization, Knowledge Acquisition
Scientific reference
A. Suárez, J. Segovia, C. Torras and G. Alenyà. Online action recognition, 35th AAAI Conference on Artificial Intelligence, 2021, (Virtual), pp. 11981-11989.
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