Comparison and Analysis of Expertness Measure in Knowledge Sharing Among Robots

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


Robot expertness measures are used to improve learning performance of knowledge sharing techniques. In this paper, several fuzzy Q-learning methods for knowledge sharing i.e. Shared Memory, Weighted Strategy Sharing (WSS) and Adaptive Weighted Strategy Sharing (AdpWSS) are studied. A new measure of expertise based on regret evaluation is proposed. Regret measure takes uncertainty bounds of two best actions, i.e. the greedy action and the second best action into account. Knowledge sharing simulations and experiments on real robots were performed to compare the effectiveness of the three expertness measures i.e. Gradient (G), Average Move (AM) and our proposed measure. The proposed measure exhibited the best performance among the three measures. Moreover, our measure that is applied to the AdpWSS does not require the predefined setting of cooperative time, thus it is more practical to be implemented in real-world problems.


Average Move Knowledge Sharing Real Robot Learning Trial Reward Rate 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.FIBO, King Mongkut’s University of Technology ThonburiThailand
  2. 2.School of Computer ScienceUniversity of BirminghamUnited Kingdom

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