{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:03:47Z","timestamp":1789707827034,"version":"4.0.1"},"reference-count":20,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004199","name":"Okinawa Institute of Science and Technology Graduate University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004199","id-type":"DOI","asserted-by":"publisher"}]},{"id":[{"id":"https:\/\/ror.org\/02qg15b79","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Artificially replicating the extraordinary adaptive potential of organisms remains difficult. Machine learning approaches based on big data pursue behavioral adaptation through generalization from training data, but often learn slowly and struggle with out-of-distribution situations. We propose that organisms may not adapt primarily through generalization alone but by rapidly eliminating infeasible solutions through online trial-and-error, effectively performing a search over the behavior space. If this search is complete, it is guaranteed to find an existing physically robust solution within a finite but unbounded time. For continuous behavioral domains that contain uncountably infinite behaviors, we introduce a mathematical framework for constructing a countably infinite dense subset of all behaviors using a mutable graph to segment behavior space, allowing any behavior to be progressively approximated arbitrarily well. Graph evolution is regulated by a heuristic feedback loop between outward growth and internal refinement; refinement is partially determined by a Bernoulli variance term related to binary entropy. Using this construction, we implement a proof-of-concept behavioral search algorithm and evaluate it on maze navigation and simple continuous control tasks. These preliminary results establish the practical feasibility of this approach in low-dimensional simulated environments while exposing unresolved limitations in terms of dimensional scaling and the incorporation of prior information.<\/jats:p>","DOI":"10.3390\/e28091013","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:29:29Z","timestamp":1789133369000},"page":"1013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hyperadaptability Through Self-Organizing Behavioral Search"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4069-4444","authenticated-orcid":false,"given":"Alex","family":"Baranski","sequence":"first","affiliation":[{"name":"Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Okinawa 904-0495, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Tani","sequence":"additional","affiliation":[{"name":"Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Okinawa 904-0495, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","first-page":"230","article-title":"On computable numbers, with an application to the Entscheidungsproblem","volume":"42","author":"Turing","year":"1936","journal-title":"Proc. Lond. Math. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.tics.2020.10.004","article-title":"Structuring knowledge with cognitive maps and cognitive graphs","volume":"25","author":"Peer","year":"2021","journal-title":"Trends Cogn. Sci."},{"key":"ref_3","unstructured":"Schaul, T., Horgan, D., Gregor, K., and Silver, D. (2015, January 7\u20139). Universal value function approximators. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_4","first-page":"5048","article-title":"Hindsight experience replay","volume":"Volume 30","author":"Guyon","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/s41586-020-03157-9","article-title":"First return, then explore","volume":"590","author":"Ecoffet","year":"2021","journal-title":"Nature"},{"key":"ref_6","first-page":"1","article-title":"Intrinsically motivated goal exploration processes with automatic curriculum learning","volume":"23","author":"Forestier","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1162\/EVCO_a_00025","article-title":"Abandoning objectives: Evolution through the search for novelty alone","volume":"19","author":"Lehman","year":"2011","journal-title":"Evol. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pugh, J.K., Soros, L.B., and Stanley, K.O. (2016). Quality diversity: A new frontier for evolutionary computation. Front. Robot. AI, 3.","DOI":"10.3389\/frobt.2016.00040"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s42492-019-0016-7","article-title":"Brief review of image denoising techniques","volume":"2","author":"Fan","year":"2019","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rand, D., and Young, L.S. (1981). Detecting strange attractors in turbulence. Proceedings of the Dynamical Systems and Turbulence, Warwick 1980, Springer.","DOI":"10.1007\/BFb0091903"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0004-3702(99)00052-1","article-title":"Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning","volume":"112","author":"Sutton","year":"1999","journal-title":"Artif. Intell."},{"key":"ref_14","unstructured":"Shaw, R., and Bransford, J. (1977). The theory of affordances. Perceiving, Acting, and Knowing: Toward an Ecological Psychology, Lawrence Erlbaum Associates."},{"key":"ref_15","unstructured":"LaValle, S.M. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, Computer Science Department, Iowa State University. Available online: https:\/\/msl.cs.illinois.edu\/~lavalle\/papers\/Lav98c.pdf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/70.508439","article-title":"Probabilistic roadmaps for path planning in high-dimensional configuration spaces","volume":"12","author":"Kavraki","year":"1996","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/s41586-020-03051-4","article-title":"Mastering atari, go, chess and shogi by planning with a learned model","volume":"588","author":"Schrittwieser","year":"2020","journal-title":"Nature"},{"key":"ref_18","unstructured":"Hubert, T., Schrittwieser, J., Antonoglou, I., Barekatain, M., Schmitt, S., and Silver, D. (2021, January 18\u201324). Learning and planning in complex action spaces. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_19","first-page":"1479","article-title":"Unifying count-based exploration and intrinsic motivation","volume":"29","author":"Bellemare","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. 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