Plan-Based Policy-Learning for Autonomous Feature Tracking


  • Maria Fox King's College London
  • Derek Long King's College London
  • Daniele Magazzeni King's College London



Policy-learning, Control, Plan execution, Monte Carlo simulation


Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.




How to Cite

Fox, M., Long, D., & Magazzeni, D. (2012). Plan-Based Policy-Learning for Autonomous Feature Tracking. Proceedings of the International Conference on Automated Planning and Scheduling, 22(1), 38-46.