Artificial Intelligence

   

A New Divergence Measure for Basic Probability Assignment and Its Applications in Extremely Uncertain Environments

Authors: Liguo Fei, Yong Hu, Yong Deng, Sankaran Mahadevan

Information fusion under extremely uncertain environments is an important issue in pattern classification and decision-making problem. Dempster-Shafer evidence theory (D-S theory) is more and more extensively applied to information fusion for its advantage to deal with uncertain information. However, the results opposite to common sense are often obtained when combining the different evidences using Dempster’s combination rules. How to measure the difference between different evidences is still an open issue. In this paper, a new divergence is proposed based on Kullback-Leibler divergence in order to measure the difference between different basic probability assignments (BPAs). Numerical examples are used to illustrate the computational process of the proposed divergence. Then the similarity for different BPAs is also defined based on the proposed divergence. The basic knowledge about pattern recognition is introduced and a new classification algorithm is presented using the proposed divergence and similarity under extremely uncertain environments, which is illustrated by a small example handling robot sensing. The method put forward is motivated by desperately in need to develop intelligent systems, such as sensor-based data fusion manipulators, which need to work in complicated, extremely uncertain environments. Sensory data satisfy the conditions 1) fragmentary and 2) collected from multiple levels of resolution.

Comments: 9 Pages.

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Submission history

[v1] 2017-12-13 08:17:06

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