Abstract
In this paper, we investigate the feasibility of building action-planning mechanisms capable of autonomously determining task-achieving sequences of actions (plans), using previously acquired subsymbolic representations. These subsymbolic representations are acquired by the robot autonomously during an exploration phase. Furthermore, we investigate whether such subsymbolic mechanisms can employ generalisation techniques in order to pursue plans through unexplored regions of the robot’s environment. Performance comparison of three subsymbolic action-planning mechanisms on different tasks conclude the paper.
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