{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:25:20Z","timestamp":1760243120076,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,7,30]],"date-time":"2015-07-30T00:00:00Z","timestamp":1438214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N000141210860"],"award-info":[{"award-number":["N000141210860"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>In this paper, we propose a new approach for recognizing intentions of humans by observing their activities with a color plus depth (RGB-D) camera. Activities and goals are modeled as a distributed network of inter-connected nodes in an Activation Spreading Network (ASN). Inspired by a formalism in hierarchical task networks, the structure of the network captures the hierarchical relationship between high-level goals and low-level activities that realize these goals. Our approach can detect intentions before they are realized and it can work in real-time. We also extend the formalism of ASNs to incorporate contextual information into intent recognition. We further augment the ASN formalism with special nodes and synaptic connections to model ordering constraints between actions, in order to represent and handle partial-order plans in our ASN. A fully functioning system is developed for experimental evaluation. We implemented a robotic system that uses our intent recognition to naturally interact with the user. Our ASN based intent recognizer is tested against three different scenarios involving everyday activities performed by a subject, and our results show that the proposed approach is able to detect low-level activities and recognize high-level intentions effectively in real-time. Further analysis shows that contextual and partial-order ASNs are able to discriminate between otherwise ambiguous goals.<\/jats:p>","DOI":"10.3390\/robotics4030284","type":"journal-article","created":{"date-parts":[[2015,7,30]],"date-time":"2015-07-30T11:21:59Z","timestamp":1438255319000},"page":"284-315","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intent Understanding Using an Activation  Spreading Architecture"],"prefix":"10.3390","volume":"4","author":[{"given":"Mohammad","family":"Saffar","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mircea","family":"Nicolescu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Monica","family":"Nicolescu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Banafsheh","family":"Rekabdar","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, University of Nevada Reno, Reno, NV 89557, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,30]]},"reference":[{"key":"ref_1","unstructured":"Sukthankar, G., Geib, C., Bui, H.H., Pynadath, D., and Goldman, R.P. (2014). Plan, Activity, and Intent Recognition: Theory and Practice, Newnes."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10462-009-9095-8","article-title":"Plan recognition for interface agents","volume":"28","author":"Armentano","year":"2007","journal-title":"Artif. Intell. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3233\/AIC-130559","article-title":"State-of-the-art of intention recognition and its use in decision making","volume":"26","author":"Han","year":"2013","journal-title":"AI Commun."},{"key":"ref_4","first-page":"32","article-title":"Generalized Plan Recognition","volume":"86","author":"Kautz","year":"1986","journal-title":"AAAI"},{"key":"ref_5","first-page":"103","article-title":"UCPOP: A Sound, Complete, Partial Order Planner for ADL","volume":"92","author":"Penberthy","year":"1992","journal-title":"KR"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s10458-014-9248-2","article-title":"The complexity of multi-agent plan recognition","volume":"29","author":"Banerjee","year":"2015","journal-title":"Auton. Agents Multi Agent Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lehrmann, A.M., Gehler, P.V., and Nowozin, S. (,  2014). Efficient  Nonlinear Markov Models for Human Motion. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.171"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.patrec.2014.04.011","article-title":"Human activity recognition from 3D data: A review","volume":"48","author":"Aggarwal","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Magnani, L. (2001). Abduction, Reason, and Science: Processes of Discovery and Explanation, Springer Science and Business Media.","DOI":"10.1007\/978-1-4419-8562-0"},{"key":"ref_10","unstructured":"Sindlar, M., Dastani, M., and Meyer, J.-J. (,  2011). Programming Mental State Abduction. Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, Ann Arbor, MI, USA."},{"key":"ref_11","unstructured":"Georgeff, M., Pell, B., Pollack, M., Tambe, M., and Wooldridge, M. (1999). Intelligent Agents V: Agents Theories, Architectures, and Languages, Springer."},{"key":"ref_12","unstructured":"Bonatti, P., Calimeri, F., Leone, N., and Ricca, F. (2010). A 25-Year Perspective on Logic Programming, Springer."},{"key":"ref_13","unstructured":"Sindlar, M.P., Dastani, M.M., Dignum, F., and Meyer, J.-J.C. (2009). Declarative Agent Languages and Technologies VI, Springer."},{"key":"ref_14","unstructured":"Pereira, L.M. (2011). Applications of Declarative Programming and Knowledge Management, Springer."},{"key":"ref_15","unstructured":"Meadows, B.L., Langley, P., and Emery, M.J. (2013, January 14\u201315). Seeing Beyond Shadows: Incremental Abductive Reasoning for Plan Understanding. Proceedings of AAAI Workshop: Plan, Activity, and Intent Recognition, Bellevue, WA, USA."},{"key":"ref_16","first-page":"377","article-title":"Complexity of model checking and bounded predicate arities for non-ground answer set programming","volume":"04","author":"Eiter","year":"2004","journal-title":"KR"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/978-1-4471-0765-1_30","article-title":"Automated Robot Behavior Recognition","volume":"9","author":"Han","year":"2000","journal-title":"Inter. Symp. Robot. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1142\/S0219843608001418","article-title":"An architecture for understanding intent using a novel hidden markov formulation","volume":"5","author":"Kelley","year":"2008","journal-title":"Int. J. Humanoid Robot."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kelley, R., Tavakkoli, A., King, C., Nicolescu, M., Nicolescu, M., and Bebis, G. (2008, January 12\u201315). Understanding human intentions via Hidden Markov Models in Autonomous Mobile Robots. Proceedings of the 3rd ACM\/IEEE International Conference on Human Robot Interaction, Amsterdam, the Netherlands.","DOI":"10.1145\/1349822.1349870"},{"key":"ref_20","unstructured":"Wilson, A.D., and Bobick, A.F. (,  1998). Recognition and Interpretation of Parametric Gesture. Proceedings of the Sixth International Conference on Computer Vision, Washington, DC, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/34.868685","article-title":"Discovery and segmentation of activities in video","volume":"22","author":"Brand","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1006\/cviu.2000.0894","article-title":"Learning variable-length Markov models of behavior","volume":"81","author":"Galata","year":"2001","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_23","unstructured":"Brand, M., Oliver, N., and Pentland, A. (1997, January 17\u201319). Coupled Hidden Markov Models for Complex Action Recognition. Proceedings of the Computer Vision and Pattern Recognition, San Juan, Argentina."},{"key":"ref_24","unstructured":"Oliver, N., Horvitz, E., and Garg, A. (,  2002). Layered Representations for Human Activity Recognition. Proceedings of the Fourth IEEE International Conference on Multimodal Interfaces, Redmond, WA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/34.868686","article-title":"Recognition of visual activities and interactions by stochastic parsing","volume":"22","author":"Ivanov","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0004-3702(93)90060-O","article-title":"A Bayesian model of plan recognition","volume":"64","author":"Charniak","year":"1993","journal-title":"Artif. Intell."},{"key":"ref_27","unstructured":"Nazerfard, E., and Cook, D.J. (2013, January 14\u201315). Using Bayesian Networks for Daily Activity Prediction. Proceedings of AAAI Workshop: Plan, Activity, and Intent Recognition, Bellevue, WA, USA."},{"key":"ref_28","unstructured":"Madabhushi, A., and Aggarwal, J. (, January July). A Bayesian Approach to Human Activity Recognition. Proceedings of the Second IEEE Workshop on Visual Surveillance, Fort Collins, CO, USA."},{"key":"ref_29","unstructured":"Hoey, J. (2001, January 8). Hierarchical Unsupervised Learning of Facial Expression Categories. Proceedings of the IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, Canada."},{"key":"ref_30","unstructured":"Fernyhough, J., Cohn, A.G., and Hogg, D. (1998, January 4\u20137). Building Qualitative Event Models Automatically from Visual Input. Proceedings of the Sixth International Conference on Computer Vision, Bombay, India."},{"key":"ref_31","unstructured":"Intille, S.S., and Bobick, A.F. (1999, January 18\u201322). A Framework for Recognizing Multi-Agent Action from Visual Evidence. Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence (AAAI\/IAAI), Menlo Park, CA, USA."},{"key":"ref_32","unstructured":"Forbes, J., Huang, T., Kanazawa, K., and Russell, S. (1995, January 20\u201325). The batmobile: Towards a Bayesian Automated Taxi. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cao, Y., Barrett, D., Barbu, A., Narayanaswamy, S., Yu, H., Michaux, A., Lin, Y., Dickinson, S., Siskind, J.M., and Wang, S. (2013, January 23\u201328). Recognize Human Activities from Partially Observed Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.343"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Koppula, H.S., and Saxena, A. (2013). Anticipating Human Activities using Object Affordances for Reactive Robotic Response. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.15607\/RSS.2013.IX.006"},{"key":"ref_35","unstructured":"Kinect for Windows. Available online: http:\/\/www.microsoft.com\/en-us\/kinectforwindows\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rti.2004.12.004","article-title":"Real-Time Foreground-Background Segmentation Using Codebook Model","volume":"11","author":"Kim","year":"2005","journal-title":"Real-Time Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Harville, M. (2002, January 28\u201331). A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models. Proceedings of the 7th European Conference on Computer Vision\u2014ECCV, Copenhagen, Denmark.","DOI":"10.1007\/3-540-47977-5_36"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object Recognition from Local Scale-Invariant Features. Proceedings of the IEEE International Conference on Computer Vision, Kerkyra, Greek.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_40","first-page":"401","article-title":"On a measure of divergence between two multinomial populations","volume":"7","author":"Bhattacharyya","year":"1946","journal-title":"Sankhy\u0101: Indian J. Stat."},{"key":"ref_41","unstructured":"Bradski, G.R. (1998, January 19\u201321). Computer Vision Face Tracking for Use in a Perceptual User Interface. Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, Princeton, NJ, USA."},{"key":"ref_42","unstructured":"Erol, K., Hendler, J.A., and Nau, D.S. (1994, January 13\u201315). UMCP: A Sound and Complete Procedure for Hierarchical Task-Network Planning. Proceedings of the International Conference on AI Planning & Scheduling (AIPS), Menlo Park, CA, USA."},{"key":"ref_43","unstructured":"Erol, K., Hendler, J., and Nau, D.S. (August, January 31). HTN Planning: Complexity and Expressivity. Proceedings of the AAAI, Seattle, WA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1613\/jair.1141","article-title":"SHOP2: An HTN planning system","volume":"20","author":"Nau","year":"2003","journal-title":"J. Artif. Intell. Res."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/4\/3\/284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:49:58Z","timestamp":1760215798000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/4\/3\/284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,30]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2015,9]]}},"alternative-id":["robotics4030284"],"URL":"https:\/\/doi.org\/10.3390\/robotics4030284","relation":{},"ISSN":["2218-6581"],"issn-type":[{"type":"electronic","value":"2218-6581"}],"subject":[],"published":{"date-parts":[[2015,7,30]]}}}