Towards navigation without sensory inputs: modelling Hesslow?s simulation hypothesis in artificial cognitive agents

Abstract: In the recent years a growing interest in Cognitive Science has been directed to the cognitive role of the agent's ability to predict the consequences of their actions, without actual engagement with their environment. The creation of an experimental model for Hesslow's simulation hypothesis, based on the use of a simulated adaptive agent and the methods of evolutionary robotics within the general perspective of radical connectionism, is reported in this dissertation. A hierarchical architecture consisting of a mixture of (recurrent) experts is investigated in order to test its ability to produce an 'inner world', functional stand-in for the agent's interactions with its environment. Such a mock world is expected to be rich enough to sustain 'blind navigation', which means navigation based solely on the agent's own internal predictions. The results exhibit the system's vivid internal dynamics, its critical sensitivity to a high number of parameters and, finally, a discrepancy with the declared goal of blind navigation. However, given the dynamical complexity of the system, further analysis and testing appear necessary.