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Agent Behavior Prediction and Its Generalization Analysis by Tian, Fei; Li, Haifang; Chen, Wei; Qin, Tao; Chen, Enhong; Liu, Tie-Yan is a scholarly article available to read on EtoBox.

What is Agent Behavior Prediction and Its Generalization Analysis about?

Machine learning algorithms have been applied to predict agent behaviors in real-world dynamic systems, such as advertiser behaviors in sponsored search and worker behaviors in crowdsourcing. The behavior data in these systems are generated by live agents: once the systems change due to the adoption of the prediction models learnt from the behavior data, agents will observe and respond to these changes by changing their own behaviors accordingly. As a result, the behavior data will evolve and will not be identically and independently distributed, posing great challenges to the theoretical analysis on the machine learning algorithms for behavior prediction. To tackle this challenge, in this paper, we propose to use Markov Chain in Random Environments (MCRE) to describe the behavior data, and perform generalization analysis of the machine learning algorithms on its basis. Since the one-step transition probability matrix of MCRE depends on both previous states and the random environment, conventional techniques for generalization analysis cannot be directly applied. To address this issue, we propose a novel technique that transforms the original MCRE into a higher-dimensional time-hom

Author

Tian, Fei; Li, Haifang; Chen, Wei; Qin, Tao; Chen, Enhong; Liu, Tie-Yan

Published

2014

Language

EN