<p>The human brain is always trying to predict ahead in time. Many say that it is possible to take actions only based on internal simulations of the brain. A recent trend in the field of Artificial Intelligence is to provide agents with an “inner world” or internal simulations. This inner world can then be used instead of the real world, making it possible to operate without any inputs from the real world.</p><p>This final year project explores the possibility to navigate collision-free in a dynamic environment, using only internal simulation of sensor input instead of real input. Three scenarios will be presented that show how internal simulation operates in a dynamic environment. The results show that it is possible to navigate entirely based on predictions without a collision.</p>

<p>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.</p>

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.

The human brain is always trying to predict ahead in time. Many say that it is possible to take actions only based on internal simulations of the brain. A recent trend in the field of Artificial Intelligence is to provide agents with an “inner world” or internal simulations. This inner world can then be used instead of the real world, making it possible to operate without any inputs from the real world. This final year project explores the possibility to navigate collision-free in a dynamic environment, using only internal simulation of sensor input instead of real input. Three scenarios will be presented that show how internal simulation operates in a dynamic environment. The results show that it is possible to navigate entirely based on predictions without a collision.