MindRACES

Skip to content
You are here: Home » Events » sinList
Sections
Personal tools

CognitiveScienceNews

Apprendimento per rinforzo e codifica tramite popolazione neurale: un modello per il reaching applicato a due task  2007-03-19
WIVA3 - 3° Workshop Italiano Vita Artificiale
Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot.  2007-04-16
Schembri M., Mirolli M., Baldassarre G. (submitted). Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot. The 6th IEEE International Conference on Development and Learning (ICDL2007).
A Model of Reaching That Integrates Reinforcement Learning and Population Encoding of Postures  2007-03-15
Ognibene, Dimitri and Rega, Angelo and Baldassarre, Gianluca From Animals to Animats 9, 9th International Conference on Simulation of Adaptive Behavior, SAB 2006, Rome, Italy, September 25-29, 2006, Proceedings.
  2008-05-15
Rappresentazioni Anticipatorie: Tre Studi Simulativi   2007-04-15
Giovanni Pezzulo (2006) Proceedings del 3° Convegno Nazionale di Scienze Cognitive (AISC 2006).
Gradient descent methods in learning classifier systems  2007-04-23
Butz, M.V., Goldberg, D.E., & Lanzi, P.L. (2005) Gradient descent methods in learning classifier systems: Improving XCS performance in multistep problems. IEEE Transactions on Evolutionary Computation, 9, 452-473.
Integrating Epistemic Action (Active Vision) and Pragmatic Action (Reaching): A Neural Architecture for Camera-Arm Robots.  2008-05-15
Ognibene, D.; Balkenius, C. & Baldassarre, G.. 2008. Integrating Epistemic Action (Active Vision) and Pragmatic Action (Reaching): A Neural Architecture for Camera-Arm Robots. Minoru Asada and Jun Tani and John Hallam and Jean-Arcady Meyer (ed.). The tenth International Conference on the SIMULATION OF ADAPTIVE BEHAVIOR (SAB'08). Osaka, Japan. Springer. july.
A reinforcement-learning model of top-down attention based on a potential-action map  2008-05-15
Ognibene, D.; Balkenius, C. & Baldassarre, G.. 2008. A reinforcement-learning model of top-down attention based on a potential-action map. Pezzulo Giovanni, Rino Falcone, Castelfranchi Cristiano (ed.). The Anticipatory Approach. Berlin. Springer-Verlag.
  2009-10-14
 

Powered by Plone

Anticipatory Cognitive Science is a research field that ensembles artificial intelligence, biology, psychology, neurology, engineering and philosophy in order to build anticipatory cognitive systems that are able to face human tasks with the same anticipatory capabilities and performance. In deep: Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures. Its organizational origins are in the mid-1970s when the Cognitive Science Society was formed and the journal Cognitive Science began. Since then, more than sixty universities in North America, Europe, Asia, and Australia have established cognitive science programs, and many others have instituted courses in cognitive science.