Using a net to catch a mate: evolving CTRNNs for the dowry problem
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Toward Spinozist Robotics: Exploring the Minimal Dynamics of Behavioral Preference
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Toward Spinozist Robotics: Exploring the Minimal Dynamics of Behavioral Preference
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Extended Homeostatic Adaptation: Improving the Link between Internal and Behavioural Stability
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Enactive artificial intelligence: Investigating the systemic organization of life and mind
Artificial Intelligence
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Flexible and multistable pattern generation by evolving constrained plastic neurocontrollers
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Guiding for associative learning: how to shape artificial dynamic cognition
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
A review of long-term memory in natural and synthetic systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Evolving plastic neural networks for online learning: review and future directions
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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In psychology the 'A not B' error, whereby infants perseverate in reaching to the location where a toy was previously hidden after it has been moved to a new location, has been the subject of fifty years research since it was first identified by Piaget [1]. This paper describes a novel implementation of the 'A not B' error paradigm which is used to test the notion that minimal systems evolutionary robotics modelling can be used to explore developmental process and to generate new hypotheses for test in natural experimental populations. The model demonstrates that agents controlled by plastic continuous time recurrent neural networks can perform the 'A not B' task and that homeostatic mediation of plasticity can produce perseverative error patterns similar to those observed in human infants. In addition, the model shows a developmental trend for the production of perseverative errors to reduce during development.