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Through a set of experiments on various problem domains, DIAS is shown able to solve problems with different dimensionality and complexity, to require no hyperparameter tuning for new problems, and to exhibit lifelong learning, that is, to adapt rapidly to run-time changes in the problem domain, and to do it better than a standard, noncollective approach. DIAS therefore demonstrates a role for ALife in building scalable, general, and adaptive problem-solving systems.<\/jats:p>","DOI":"10.1162\/artl_a_00418","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T16:11:15Z","timestamp":1701706275000},"page":"259-276","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["Domain-Independent Lifelong Problem Solving Through Distributed ALife Actors"],"prefix":"10.1162","volume":"30","author":[{"given":"Babak","family":"Hodjat","sequence":"first","affiliation":[{"name":"Cognizant AI Labs. babak@cognizant.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hormoz","family":"Shahrzad","sequence":"additional","affiliation":[{"name":"Cognizant AI Labs University of Texas at Austin Department of Computer 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