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Skill acquisition through program-level imitation in a real-time domain.

Mark A Wood1, Joanna J Bryson

  • 1University of Bath, BA2 7AY Bath, UK.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 10, 2007
PubMed
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This study introduces an imitation learning system for complex environments. It demonstrates how social learning simplifies skill acquisition, adaptable for various species.

Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Robotics

Background:

  • Imitation learning is crucial for agents in complex environments.
  • Social learning offers inherent biases that can simplify skill acquisition.
  • Existing systems struggle with dynamic, real-time task learning.

Purpose of the Study:

  • To develop an imitation learning system for complex dynamic environments.
  • To frame social learning as a specialized form of general skill learning.
  • To demonstrate the system's efficacy in a real-time virtual reality game.

Main Methods:

  • Decomposition of skill learning into four subproblems.
  • Modification of Roy's CELL system to address all subproblems.
  • Demonstration within the Unreal Tournament environment.

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Main Results:

  • The developed system successfully learns tasks in a complex dynamic environment.
  • The modified CELL system effectively handles the decomposed skill learning subproblems.
  • The system showcases the potential of social learning biases in skill acquisition.

Conclusions:

  • Social learning biases significantly simplify skill learning problems.
  • The proposed imitation learning system is effective in dynamic, real-time settings.
  • This approach offers a unified framework for general skill and social learning.