SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report

  • Christopher Child
  • Kostas Stathis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3394)


We present a framework for building agents that learn using SMART, a system that combines stochastic model acquisition with reinforcement learning to enable an agent to model its environment through experience and subsequently form action selection policies using the acquired model. We extend an existing algorithm for automatic creation of stochastic strips operators [9] as a preliminary method of environment modelling. We then define the process of generation of future states using these operators and an initial state and finally show the process by which the agent can use the generated states to form a policy with a standard reinforcement learning algorithm. The potential of SMART is exemplified using the well-known predator prey scenario. Results of applying SMART to this environment and directions for future work are discussed.


World State Inductive Logic Programming Learn Agent Reinforcement Learning Algorithm State Action Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christopher Child
    • 1
  • Kostas Stathis
    • 1
  1. 1.Department of Computing, School of InformaticsCity UniversityLondon

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