Attention control learning in the decision space using state estimation

Zahra Gharaee, Alireza Fatehi, Maryam Mirian, Majid Nili Ahmadabadi

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)


The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.
Original languageEnglish
Pages (from-to)1659-1674
JournalInternational Journal of Systems Science
Issue number7
Publication statusPublished - 2014 Aug 8

Subject classification (UKÄ)

  • Philosophy
  • Robotics


  • Attention Control
  • State Estimation
  • Bayesian Reinforcement Learning
  • Decision Making
  • Mixture of Experts


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