TY - GEN
T1 - Building concepts for AI agents using information theoretic co-clustering
AU - Chen, Jason R.
PY - 2010
Y1 - 2010
N2 - High level conceptual thought seems to be at the basis of the impressive human cognitive ability, and AI researchers aim to replicate this ability in artificial agents. Classical top-down (Logic based) and bottom-up (Connectionist) approaches to the problem have had limited success to date. We review a small body of work that represents a different approach to AI. We call this work the Bottom Up Symbolic (BUS) approach and present a new BUS method to concept construction. While valid concepts have been constructed using previous methods under this approach, we show in this paper that the one-sided clustering methods generally used there may fail to uncover valid concepts even when they clearly exist. We show that by using a Co-clustering algorithm that searches for an optimal partitioning based on the Mutual Information between the category and consequent components of a concept, the concept formation outcome is improved. We test our approach on data from experiments using a real mobile robot operating in the real world, and show that our Co-clustering based approach leads to significant performance improvement compared to previous approaches.
AB - High level conceptual thought seems to be at the basis of the impressive human cognitive ability, and AI researchers aim to replicate this ability in artificial agents. Classical top-down (Logic based) and bottom-up (Connectionist) approaches to the problem have had limited success to date. We review a small body of work that represents a different approach to AI. We call this work the Bottom Up Symbolic (BUS) approach and present a new BUS method to concept construction. While valid concepts have been constructed using previous methods under this approach, we show in this paper that the one-sided clustering methods generally used there may fail to uncover valid concepts even when they clearly exist. We show that by using a Co-clustering algorithm that searches for an optimal partitioning based on the Mutual Information between the category and consequent components of a concept, the concept formation outcome is improved. We test our approach on data from experiments using a real mobile robot operating in the real world, and show that our Co-clustering based approach leads to significant performance improvement compared to previous approaches.
UR - http://www.scopus.com/inward/record.url?scp=77957844153&partnerID=8YFLogxK
U2 - 10.1109/IS.2010.5548372
DO - 10.1109/IS.2010.5548372
M3 - Conference contribution
SN - 9781424451647
T3 - 2010 IEEE International Conference on Intelligent Systems, IS 2010 - Proceedings
SP - 355
EP - 360
BT - 2010 IEEE International Conference on Intelligent Systems, IS 2010 - Proceedings
T2 - 2010 IEEE International Conference on Intelligent Systems, IS 2010
Y2 - 7 July 2010 through 9 July 2010
ER -