{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:39:03Z","timestamp":1781523543725,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103420"],"award-info":[{"award-number":["62103420"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The problem of goal recognition involves inferring the high-level task goals of an agent based on observations of its behavior in an environment. Current methods for achieving this task rely on offline comparison inference of observed behavior in discrete environments, which presents several challenges. First, accurately modeling the behavior of the observed agent requires significant computational resources. Second, continuous simulation environments cannot be accurately recognized using existing methods. Finally, real-time computing power is required to infer the likelihood of each potential goal. In this paper, we propose an advanced and efficient real-time online goal recognition algorithm based on deep reinforcement learning in continuous domains. By leveraging the offline modeling of the observed agent\u2019s behavior with deep reinforcement learning, our algorithm achieves real-time goal recognition. We evaluate the algorithm\u2019s online goal recognition accuracy and stability in continuous simulation environments under communication constraints.<\/jats:p>","DOI":"10.3390\/e25101415","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T11:58:57Z","timestamp":1696420737000},"page":"1415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Online Goal Recognition in Continuous Domains via Deep Reinforcement Learning"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6690-333X","authenticated-orcid":false,"given":"Zihao","family":"Fang","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8161-6916","authenticated-orcid":false,"given":"Dejun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunxiu","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7044-4377","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"ref_1","unstructured":"Sukthankar, G., Goldman, R., Geib, C., Pynadath, D., and Bui, H. (2014). Plan, Activity, and Intent Recognition: Theory and Practice, Elsevier."},{"key":"ref_2","unstructured":"Geib, C.W. (2002, January 29). Problems with Intent Recognition for Elder Care. Proceedings of the AAAI-02 Workshop \u201cAutomation as Caregiver\u201d, Edmonton, AB, Canada."},{"key":"ref_3","unstructured":"Granada, R., Pereira, R.F., Monteiro, J., Barros, R., Ruiz, D., and Meneguzzi, F. (2017, January 4\u20139). Hybrid Activity and Plan Recognition for Video Streams. Proceedings of the 31st AAAI Conference: Plan, Activity and Intent Recognition Workshop, San Francisco, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brewitt, C., Gyevnar, B., Garcin, S., and Albrecht, S.V. (October, January 27). GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636279"},{"key":"ref_5","unstructured":"Brewitt, C., Tamborski, M., and Albrecht, S.V. (2022). Verifiable Goal Recognition for Autonomous Driving with Occlusions. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, K., and Yin, Q. (2019). Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric. Entropy, 21.","DOI":"10.3390\/e21030299"},{"key":"ref_7","unstructured":"Sohrabi, S., Riabov, A.V., and Udrea, O. (2016, January 9\u201315). Plan Recognition as Planning Revisited. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"717003","DOI":"10.3389\/frai.2021.717003","article-title":"Behaviour Recognition with Kinodynamic Planning Over Continuous Domains","volume":"4","author":"Fitzpatrick","year":"2021","journal-title":"Front. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wayllace, C., Ha, S., Han, Y., Hu, J., Monadjemi, S., Yeoh, W., and Ottley, A. (2020, January 7\u201312). DRAGON-V: Detection and Recognition of Airplane Goals with Navigational Visualization. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i09.7108"},{"key":"ref_10","unstructured":"Ramirez, M., and Geffner, H. (2009, January 11\u201317). Plan Recognition as Planning. Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Amado, L., Mirsky, R., and Meneguzzi, F. (2022). Goal Recognition as Reinforcement Learning. arXiv.","DOI":"10.1609\/aaai.v36i9.21198"},{"key":"ref_12","unstructured":"Silver, T., and Chitnis, R. (2020). PDDLGym: Gym Environments from PDDL Problems. arXiv."},{"key":"ref_13","unstructured":"Vered, M., and Kaminka, G.A. (2017, January 8\u201312). Online Recognition of Navigation Goals Through Goal Mirroring (Extended Abstract). Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems, S\u00e3o Paulo, Brazil."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vered, M., and Kaminka, G.A. (2017). Heuristic Online Goal Recognition in Continuous Domains. arXiv.","DOI":"10.24963\/ijcai.2017\/621"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Meneguzzi, F., and Fraga Pereira, R. (2021, January 19\u201326). A Survey on Goal Recognition as Planning. Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/616"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"643010","DOI":"10.3389\/frobt.2021.643010","article-title":"Activity, Plan, and Goal Recognition: A Review","volume":"8","author":"Peer","year":"2021","journal-title":"Front. Robot. AI"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mirsky, R., Keren, S., and Geib, C. (2021). Introduction to Symbolic Plan and Goal Recognition, Morgan & Claypool Publishers.","DOI":"10.1007\/978-3-031-01589-2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103217","DOI":"10.1016\/j.artint.2019.103217","article-title":"Landmark-Based Approaches for Goal Recognition as Planning","volume":"279","author":"Pereira","year":"2020","journal-title":"Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amado, L., Paludo Licks, G., Marcon, M., Fraga Pereira, R., and Meneguzzi, F. (2020, January 19\u201324). Using Self-Attention LSTMs to Enhance Observations in Goal Recognition, Glasgow, United Kingdom. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207597"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Amado, L., Pereira, R.F., Aires, J., Magnaguagno, M., Granada, R., and Meneguzzi, F. (2018, January 8\u201313). Goal Recognition in Latent Space. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489653"},{"key":"ref_21","unstructured":"Zhi-Xuan, T., Mann, J., Silver, T., Tenenbaum, J., and Mansinghka, V. (2020). Online Bayesian Goal Inference for Boundedly Rational Planning Agents, Curran Associates, Inc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Masters, P., and Sardina, S. (2017, January 8\u201312). Cost-Based Goal Recognition for Path-Planning. Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems, S\u00e3o Paulo, Brazil.","DOI":"10.24963\/ijcai.2018\/747"},{"key":"ref_23","unstructured":"Oh, J., Meneguzzi, F., Sycara, K., and Norman, T.J. (2011, January 16\u201322). An Agent Architecture for Prognostic Normative Reasoning. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Avrahami-Zilberbrand, D., and Kaminka, G.A. (2014). Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior, Elsevier.","DOI":"10.1016\/B978-0-12-398532-3.00004-X"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, K., Zeng, Y., Qin, L., and Yin, Q. (2020). Single Real Goal, Magnitude-Based Deceptive Path-Planning. Entropy, 22.","DOI":"10.3390\/e22010088"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kaminka, G., Vered, M., and Agmon, N. (2018, January 2\u20137). Plan Recognition in Continuous Domains. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12097"},{"key":"ref_27","unstructured":"Borrajo, D., Gopalakrishnan, S., and Potluru, V.K. (2020). Goal Recognition via Model-Based and Model-Free Techniques. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Masters, P., and Vered, M. (2021, January 19\u201327). What\u2019s the Context? Implicit and Explicit Assumptions in Model-Based Goal Recognition, Montreal, Canada. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/615"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1613\/jair.2575","article-title":"Learning Partially Observable Deterministic Action Models","volume":"33","author":"Amir","year":"2008","journal-title":"J. Artif. Intell. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Asai, M., and Muise, C. (2020). Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS). arXiv.","DOI":"10.24963\/ijcai.2020\/371"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Juba, B., Le, H.S., and Stern, R. (2021). Safe Learning of Lifted Action Models. arXiv.","DOI":"10.24963\/kr.2021\/36"},{"key":"ref_32","unstructured":"Zeng, Y., Xu, K., Yin, Q., Qin, L., Zha, Y., and Yeoh, W. (2018, January 2\u20137). Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction. Proceedings of the AAAI Workshops, New Orleans, LA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Durga, K.M.L., Jyotsna, P., and Kumar, G.K. (2022, January 7\u20139). A Deep Learning based Human Activity Recognition Model using Long Short Term Memory Networks. Proceedings of the 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India.","DOI":"10.1109\/ICSCDS53736.2022.9760794"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chiari, M., Gerevini, A.E., Putelli, L., Percassi, F., and Serina, I. (2022). Goal Recognition as a Deep Learning Task: The GRNet Approach. arXiv.","DOI":"10.1609\/icaps.v33i1.27237"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/10\/1415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:05:22Z","timestamp":1760130322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/10\/1415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["e25101415"],"URL":"https:\/\/doi.org\/10.3390\/e25101415","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,4]]}}}