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Composability in Cognitive Hierarchies

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AI 2016: Advances in Artificial Intelligence (AI 2016)

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This paper develops a theory of node composition in a formal framework for cognitive hierarchies. It builds on an existing model for the integration of symbolic and sub-symbolic representations in a robot architecture consisting of nodes in a hierarchy. A notion of behaviour equivalence between cognitive hierarchies is introduced and node composition operators that preserve this equivalence are defined. This work is significant in two respects. Firstly, it opens the way for a formal comparison between cognitive robotic systems. Secondly, composition, more precisely decomposition, has been shown to be important to many fields, and may therefore prove of practical benefit in the context of cognitive systems.

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This material is based upon work supported by the Asian Office of Aerospace Research and Development (AOARD) under Award No: FA2386-15-1-0005. This research was also supported under Australian Research Council’s (ARC) Discovery Projects funding scheme (project number DP 150103035). Michael Thielscher is also affiliated with the University of Western Sydney.

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Correspondence to David Rajaratnam .

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Rajaratnam, D., Hengst, B., Pagnucco, M., Sammut, C., Thielscher, M. (2016). Composability in Cognitive Hierarchies. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham.

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  • Print ISBN: 978-3-319-50126-0

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