Qifeng Qiao

Publications (4)0 Total impact

• Article: Behavior Pattern Recognition using A New Representation Model

  Qifeng Qiao, Peter A. Beling
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  ABSTRACT: We study the use of inverse reinforcement learning (IRL) as a tool for the recognition of agents' behavior on the basis of observation of their sequential decision behavior interacting with the environment. We model the problem faced by the agents as a Markov decision process (MDP) and model the observed behavior of the agents in terms of forward planning for the MDP. We use IRL to learn reward functions and then use these reward functions as the basis for clustering or classification models. Experimental studies with GridWorld, a navigation problem, and the secretary problem, an optimal stopping problem, suggest reward vectors found from IRL can be a good basis for behavior pattern recognition problems. Empirical comparisons of our method with several existing IRL algorithms and with direct methods that use feature statistics observed in state-action space suggest it may be superior for recognition problems.
  01/2013;
• Source

  Available from: Peter Beling

  Article: Inverse Reinforcement Learning with Gaussian Process

  Qifeng Qiao, Peter A. Beling
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  ABSTRACT: We present new algorithms for inverse reinforcement learning (IRL, or inverse optimal control) in convex optimization settings. We argue that finite-space IRL can be posed as a convex quadratic program under a Bayesian inference framework with the objective of maximum a posterior estimation. To deal with problems in large or even infinite state space, we propose a Gaussian process model and use preference graphs to represent observations of decision trajectories. Our method is distinguished from other approaches to IRL in that it makes no assumptions about the form of the reward function and yet it retains the promise of computationally manageable implementations for potential real-world applications. In comparison with an establish algorithm on small-scale numerical problems, our method demonstrated better accuracy in apprenticeship learning and a more robust dependence on the number of observations.
  08/2012;
• Conference Proceeding: Classroom Video Assessment and Retrieval via Multiple Instance Learning.

  Qifeng Qiao, Peter A. Beling
  Artificial Intelligence in Education - 15th International Conference, AIED 2011, Auckland, New Zealand, June 28 - July 2011; 01/2011
• Conference Proceeding: Localized Content Based Image Retrieval with Self-Taught Multiple Instance Learning.

  Qifeng Qiao, Peter A. Beling
  ICDM Workshops 2009, IEEE International Conference on Data Mining Workshops, Miami, Florida, USA, 6 December 2009; 01/2009