{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:02:43Z","timestamp":1777521763658,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2003,9,1]],"date-time":"2003-09-01T00:00:00Z","timestamp":1062374400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2003,9]]},"abstract":"<jats:p>Natural intelligence and autonomous agents face difficulties when acting in information-dense environments. Assailed by a multitude of stimuli they have to make sense of the inflow of information, filtering and processing what is necessary, but discarding that which is unimportant. This paper aims at investigating the interactions between evolution of the sensorial channel extracting the information from the environment and the simultaneous individual adaptation of agent-control. Our particular goal is to study the influence of learning on the evolution of sensors, with learning duration being the tunable parameter. A genetic algorithm governs the evolution of sensors appropriate for the agent solving a simple grid world task. The performance of the agent is taken as fitness; \u2018sensors\u2019 are conceived as a map from environmental states to agent observations, and individual adaptation is modeled by Q-learning. Our experimental results show that due to the principles of cognitive economy learning and varying the degree thereof actually transforms the fitness landscape. In particular we identify a trade-off between learning speed (load) and sensor accuracy (error). These results are further reinforced by theoretical analysis: we derive an analytical measure for the quality of sensors based on the mutual entropy between the system of states and the selection of an optimal action, a concept recently proposed by Polani, Martinetz, and Kim.<\/jats:p>","DOI":"10.1177\/1059712303113002","type":"journal-article","created":{"date-parts":[[2004,4,21]],"date-time":"2004-04-21T20:12:39Z","timestamp":1082578359000},"page":"159-177","source":"Crossref","is-referenced-by-count":2,"title":["Evolution and Learning: Evolving Sensors in a Simple MDP Environment"],"prefix":"10.1177","volume":"11","author":[{"given":"Tobias","family":"Jung","sequence":"first","affiliation":[{"name":"Institut f\u00fcr Informatik, Gutenberg-Universit\u00e4t Mainz,"}]},{"given":"Peter","family":"Dauscher","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Informatik, Gutenberg-Universit\u00e4t Mainz,"}]},{"given":"Thomas","family":"Uthmann","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Informatik, Gutenberg-Universit\u00e4t Mainz,"}]}],"member":"179","published-online":{"date-parts":[[2003,9,1]]},"reference":[{"key":"atypb1","unstructured":"Ackley, D. & Littman, M. (1991). Interactions between learning and evolution. In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II, SFI studies in the sciences of complexity (Vol. X, pp. 487-509). Reading, MA: Addison-Wesley ."},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"Adami, C. (1998). Introduction to artificial life. New York Springer Verlag .","DOI":"10.1007\/978-1-4612-1650-6"},{"key":"atypb3","unstructured":"Barlow, H. B. (1959). Sensory mechanisms, the reduction of redundancy, and intelligence . In Proceedings of the Symposium on the Mechanisation of Thought Processes, (pp. 535-539 )."},{"key":"atypb4","unstructured":"Bellman, R. (1957). Dynamic programming. NJ Princeton University Press ."},{"key":"atypb5","doi-asserted-by":"crossref","unstructured":"Bruner, J. S., Goodnow, J. J. & Austin, G. A. (1956). A study of thinking. NY Wiley and Sons .","DOI":"10.2307\/1292061"},{"key":"atypb6","unstructured":"Dautenhahn, K., Polani, D. & Uthmann, T. (Eds.). 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