Attention control learning in the decision space using state estimation
(2014) In International Journal of Systems Science 47(7). p.1659-1674- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f8c1e605-1ddd-47bd-974c-fcca4966c51a
- author
- Gharaee, Zahra LU ; Fatehi, Alireza ; Mirian, Maryam and Nili Ahmadabadi, Majid
- organization
- publishing date
- 2014-08-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Attention Control, State Estimation, Bayesian Reinforcement Learning, Decision Making, Mixture of Experts
- in
- International Journal of Systems Science
- volume
- 47
- issue
- 7
- pages
- 1659 - 1674
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85000195459
- ISSN
- 0020-7721
- DOI
- 10.1080/00207721.2014.945982
- language
- English
- LU publication?
- yes
- id
- f8c1e605-1ddd-47bd-974c-fcca4966c51a
- date added to LUP
- 2017-05-15 17:10:57
- date last changed
- 2022-04-09 08:26:43
@article{f8c1e605-1ddd-47bd-974c-fcca4966c51a,
abstract = {{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.}},
author = {{Gharaee, Zahra and Fatehi, Alireza and Mirian, Maryam and Nili Ahmadabadi, Majid}},
issn = {{0020-7721}},
keywords = {{Attention Control; State Estimation; Bayesian Reinforcement Learning; Decision Making; Mixture of Experts}},
language = {{eng}},
month = {{08}},
number = {{7}},
pages = {{1659--1674}},
publisher = {{Taylor & Francis}},
series = {{International Journal of Systems Science}},
title = {{Attention control learning in the decision space using state estimation}},
url = {{http://dx.doi.org/10.1080/00207721.2014.945982}},
doi = {{10.1080/00207721.2014.945982}},
volume = {{47}},
year = {{2014}},
}