Online Action Recognition
| Autor: | Suárez Hernández, Alejandro, Segovia Aguas, Javier, Torras, Carme|||0000-0002-2933-398X, Alenyà Ribas, Guillem|||0000-0002-6018-154X |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI, European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Ministerio de Economía y Competitividad (España), European Research Council |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Activity and Plan Recognition Artificial intelligence Computer Science - Artificial Intelligence Adquisició del coneixement (Sistemes experts) Intel·ligència artificial General Medicine Processament òptic de dades Artificial Intelligence (cs.AI) Applications Knowledge acquisition (Expert systems) Deterministic Planning Knowledge acquisition Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC] Constraint satisfaction and optimization Optical data processing Knowledge Acquisition Activity and plan Recognition |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Digital.CSIC. Repositorio Institucional del CSIC instname |
| ISSN: | 2374-3468 2159-5399 |
| Popis: | Trabajo presentado en 35th AAAI Conference on Artificial Intelligence, celebrada de forma virtual del 2 al 9 de febrero de 2021 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. The research leading to these results has received funding from the EU H2020 research and innovation programme under grant agreement no.731761, IMAGINE; the Hu- MoUR project TIN2017-90086-R (AEI/FEDER, UE); and AEI through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Javier Segovia-Aguas was also partially supported by TAILOR, a project funded by EU H2020 research and innovation programme no. 952215, an ERC Advanced Grant no. 885107, and grant TIN-2015-67959-P from MINECO, Spain. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|