{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:57:33Z","timestamp":1743123453629,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031287183"},{"type":"electronic","value":"9783031287190"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-28719-0_23","type":"book-chapter","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T18:03:51Z","timestamp":1679421831000},"page":"328-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Search of Active Inference Policy Spaces Using k-Means"],"prefix":"10.1007","author":[{"given":"Alex B.","family":"Kiefer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahault","family":"Albarracin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"23_CR1","unstructured":"Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: Optimizing the latent space of generative networks. arXiv preprint arXiv:1707.05776 (2017)"},{"key":"23_CR2","unstructured":"Bottou, L., Bengio, Y.: Convergence properties of the k-means algorithms. Adv. Neural Inf. Process. Syst. 7 (1994)"},{"key":"23_CR3","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.neunet.2022.05.010","volume":"152","author":"T Champion","year":"2022","unstructured":"Champion, T., Bowman, H., Grze\u015b, M.: Branching time active inference: empirical study and complexity class analysis. Neural Netw. 152, 450\u2013466 (2022)","journal-title":"Neural Netw."},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neunet.2022.03.036","volume":"151","author":"T Champion","year":"2022","unstructured":"Champion, T., Da Costa, L., Bowman, H., Grze\u015b, M.: Branching time active inference: the theory and its generality. Neural Netw. 151, 295\u2013316 (2022). https:\/\/doi.org\/10.1016\/j.neunet.2022.03.036","journal-title":"Neural Netw."},{"key":"23_CR5","unstructured":"Da Costa, L., Sajid, N., Parr, T., Friston, K., Smith, R.: The relationship between dynamic programming and active inference: the discrete, finite-horizon case. arXiv preprint arXiv:2009.08111 (2020)"},{"key":"23_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2020.102447","volume":"99","author":"L Da Costa","year":"2020","unstructured":"Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., Friston, K.: Active inference on discrete state-spaces: a synthesis. J. Math. Psychol. 99, 102447 (2020)","journal-title":"J. Math. Psychol."},{"key":"23_CR7","unstructured":"Fountas, Z., Sajid, N., Mediano, P., Friston, K.: Deep active inference agents using Monte-Carlo methods. In: Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS 2020), Red Hook, NY, USA (2020). Curran Associates Inc. ISBN 9781713829546"},{"issue":"1","key":"23_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/NECO\\_a_00912","volume":"29","author":"K Friston","year":"2017","unstructured":"Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G.: Active inference: a process theory. Neural Comput. 29(1), 1\u201349 (2017). https:\/\/doi.org\/10.1162\/NECO_a_00912","journal-title":"Neural Comput."},{"issue":"4","key":"23_CR9","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s40031-014-0106-z","volume":"95","author":"M Goyal","year":"2014","unstructured":"Goyal, M., Kumar, S.: Improving the initial centroids of k-means clustering algorithm to generalize its applicability. J. Inst. Eng. (India): Ser. B 95(4), 345\u2013350 (2014). https:\/\/doi.org\/10.1007\/s40031-014-0106-z","journal-title":"J. Inst. Eng. (India): Ser. B"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Heins, C., et al.: pymdp: a Python library for active inference in discrete state spaces. arXiv preprint arXiv:2201.03904 (2022)","DOI":"10.21105\/joss.04098"},{"key":"23_CR11","unstructured":"Kulkarni, T.D., Saeedi, A., Gautam, S., Gershman, S.J.: Deep successor reinforcement learning. arXiv preprint arXiv:1606.02396 (2016)"},{"key":"23_CR12","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-319-53817-4_4","volume-title":"Guide to Big Data Applications","author":"Y Li","year":"2018","unstructured":"Li, Y., Yang, T.: Word embedding for understanding natural language: a survey. In: Srinivasan, S. (ed.) Guide to Big Data Applications. SBD, vol. 26, pp. 83\u2013104. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-53817-4_4"},{"issue":"1","key":"23_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1137\/120875909","volume":"56","author":"L Liberti","year":"2014","unstructured":"Liberti, L., Lavor, C., Maculan, N., Mucherino, A.: Euclidean distance geometry and applications. SIAM Rev. 56(1), 3\u201369 (2014)","journal-title":"SIAM Rev."},{"key":"23_CR14","unstructured":"Lueckmann, J.M., Boelts, J., Greenberg, D., Goncalves, P., Macke, J.: Benchmarking simulation-based inference. In: International Conference on Artificial Intelligence and Statistics, pp. 343\u2013351. PMLR (2021)"},{"key":"23_CR15","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(86), 2579\u20132605 (2008). https:\/\/jmlr.org\/papers\/v9\/vandermaaten08a.html"},{"key":"23_CR16","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26 (2013)"},{"key":"23_CR17","unstructured":"Millidge, B., Buckley, C.L.: Successor representation active inference. arXiv preprint arXiv:2207.09897 (2022)"},{"key":"23_CR18","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-64919-7_1","volume-title":"Active Inference","author":"B Millidge","year":"2020","unstructured":"Millidge, B., Tschantz, A., Seth, A.K., Buckley, C.L.: On the relationship between active inference and control as inference. In: IWAI 2020. CCIS, vol. 1326, pp. 3\u201311. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-64919-7_1"},{"issue":"5","key":"23_CR19","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s00422-019-00805-w","volume":"113","author":"T Parr","year":"2019","unstructured":"Parr, T., Friston, K.J.: Generalised free energy and active inference. Biol. Cybern. 113(5), 495\u2013513 (2019). https:\/\/doi.org\/10.1007\/s00422-019-00805-w","journal-title":"Biol. Cybern."},{"issue":"06","key":"23_CR20","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1142\/S021800140900748X","volume":"23","author":"K Riesen","year":"2009","unstructured":"Riesen, K., Bunke, H.: Graph classification based on vector space embedding. Int. J. Pattern Recognit Artif Intell. 23(06), 1053\u20131081 (2009)","journal-title":"Int. J. Pattern Recognit Artif Intell."},{"key":"23_CR21","doi-asserted-by":"publisher","first-page":"710","DOI":"10.3389\/fpsyg.2013.00710","volume":"4","author":"P Schwartenbeck","year":"2013","unstructured":"Schwartenbeck, P., FitzGerald, T., Dolan, R., Friston, K.: Exploration, novelty, surprise, and free energy minimization. Front. Psychol. 4, 710 (2013)","journal-title":"Front. Psychol."},{"key":"23_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2021.102632","volume":"107","author":"R Smith","year":"2022","unstructured":"Smith, R., Friston, K.J., Whyte, C.J.: A step-by-step tutorial on active inference and its application to empirical data. J. Math. Psychol. 107, 102632 (2022)","journal-title":"J. Math. Psychol."},{"key":"23_CR23","unstructured":"Steccanella, L., Totaro, S., Allonsius, D., Jonsson, A.: Hierarchical reinforcement learning for efficient exploration and transfer. arXiv preprint arXiv:2011.06335 (2020)"},{"key":"23_CR24","unstructured":"Whiteley, N., Andrieu, C., Doucet, A.: Efficient Bayesian inference for switching state-space models using discrete particle Markov chain Monte Carlo methods. arXiv preprint arXiv:1011.2437 (2010)"}],"container-title":["Communications in Computer and Information Science","Active Inference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28719-0_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T18:13:42Z","timestamp":1679422422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28719-0_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031287183","9783031287190"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28719-0_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Code for reproducing our experiments and analysis can be found at: .","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}},{"value":"IWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Active Inference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwai-ws2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwaiworkshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}