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Aktius, Malin
Publikasjoner (3 av 3) Visa alla publikasjoner
Aktius, M. & Ziemke, T. (2012). Kognitiv robotik (1ed.). In: Jens Allwood, Mikael Jensen (Ed.), Kognitionsvetenskap: (pp. 551-560). Studentlitteratur
Åpne denne publikasjonen i ny fane eller vindu >>Kognitiv robotik
2012 (svensk)Inngår i: Kognitionsvetenskap / [ed] Jens Allwood, Mikael Jensen, Studentlitteratur, 2012, 1, s. 551-560Kapittel i bok, del av antologi (Fagfellevurdert)
sted, utgiver, år, opplag, sider
Studentlitteratur, 2012 Opplag: 1
HSV kategori
Forskningsprogram
Teknik
Identifikatorer
urn:nbn:se:his:diva-6959 (URN)978-91-44-05166-6 (ISBN)
Tilgjengelig fra: 2012-12-28 Laget: 2012-12-28 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Morse, A. & Aktius, M. (2009). Dynamic liquid association: Complex learning without implausible guidance. Neural Networks, 22(7), 875-889
Åpne denne publikasjonen i ny fane eller vindu >>Dynamic liquid association: Complex learning without implausible guidance
2009 (engelsk)Inngår i: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 22, nr 7, s. 875-889Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Simple associative networks have many desirable properties, but are fundamentally limited by their inability to accurately capture complex relationships. This paper presents a solution significantly extending the abilities of associative networks by using an untrained dynamic reservoir as an input filter. The untrained reservoir provides complex dynamic transformations, and temporal integration, and can be viewed as a complex non-linear feature detector from which the associative network can learn. Typically reservoir systems utilize trained single layer perceptrons to produce desired output responses. However given that both single layer perceptions and simple associative learning have the same computational limitations, i.e. linear separation, they should perform similarly in terms of pattern recognition ability. Further to this the extensive psychological properties of simple associative networks and the lack of explicit supervision required for associative learning motivates this extension overcoming previous limitations. Finally, we demonstrate the resulting model in a robotic embodiment, learning sensorimotor contingencies, and matching a variety of psychological data. (C) 2008 Elsevier Ltd. All rights reserved.

sted, utgiver, år, opplag, sider
Elsevier, 2009
Identifikatorer
urn:nbn:se:his:diva-7799 (URN)10.1016/j.neunet.2008.10.008 (DOI)000270524500004 ()2-s2.0-69449104579 (Scopus ID)
Tilgjengelig fra: 2013-03-28 Laget: 2013-03-21 Sist oppdatert: 2017-12-06bibliografisk kontrollert
Aktius, M., Nordahl, M. & Ziemke, T. (2007). A Behavior-Based Model of the Hydra, Phylum Cnidaria. In: 9th European Conference, ECAL 2007: Advances in Artificial Life. Paper presented at 9th European Conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007. (pp. 1024-1033). Springer Berlin/Heidelberg
Åpne denne publikasjonen i ny fane eller vindu >>A Behavior-Based Model of the Hydra, Phylum Cnidaria
2007 (engelsk)Inngår i: 9th European Conference, ECAL 2007: Advances in Artificial Life, Springer Berlin/Heidelberg, 2007, s. 1024-1033Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Behavior-based artificial systems, e.g. mobile robots, are frequently designed using (various degrees and levels of) biology as inspiration, but rarely modeled based on actual quantitative empirical data. This paper presents a data-driven behavior-based model of a simple biological organism, the hydra. Four constituent behaviors were implemented in a simulated animal, and the overall behavior organization was accomplished using a colony-style architecture (CSA). The results indicate that the CSA, using a priority-based behavioral hierarchy suggested in the literature, can be used to model behavioral properties like latency, activation threshold, habituation, and duration of the individual behaviors of the hydra. Limitations of this behavior-based approach are also discussed.

sted, utgiver, år, opplag, sider
Springer Berlin/Heidelberg, 2007
Serie
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, E-ISSN 0302-9743 ; 4648
Emneord
behavior-based modeling, data-driven modeling, hydra, colony-style architecture
HSV kategori
Forskningsprogram
Teknik
Identifikatorer
urn:nbn:se:his:diva-2091 (URN)10.1007/978-3-540-74913-4_103 (DOI)000250749000103 ()2-s2.0-38049067103 (Scopus ID)978-3-540-74912-7 (ISBN)
Konferanse
9th European Conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007.
Tilgjengelig fra: 2008-05-28 Laget: 2008-05-28 Sist oppdatert: 2018-01-12bibliografisk kontrollert
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