Assisted Feature Engineering and Feature Learning to Build Knowledge-Based Agents for Arcade Games. Andelefski, B. & Schiffer, S. In van den Herik, J., Rocha, A. P., & Filipe, J., editors, ICAART 2017 - Proceedings of the 4th International Conference on Agents and Artificial Intelligence, volume 2 - Artificial Intelligence, pages 228–238, February 6-8, 2017. SciTePress.
abstract   bibtex   
Human knowledge can greatly increase the performance of autonomous agents. Leveraging this knowledge is sometimes neither straightforward nor easy. In this paper, we present an approach for assisted feature engineering and feature learning to build knowledge-based agents for three arcade games within the Arcade Learning Environment. While existing approaches mostly use model-free approaches we aim at creating a descriptive set of features for world modelling and building agents. To this end, we provide (visual) assistance in identifying and modelling features from RAM, we allow for learning features based on labeled game data, and we allow for creating basic agents using the above features. In our evaluation, we compare different methods to learn features from the RAM. We then compare several agents using different sets of manual and learned features with one another and with the state-of-the-art.
@inproceedings{ Andelefski:Schiffer:ICAART2017:AssistedFeatureLearning,
  author       = {Bastian Andelefski and Stefan Schiffer},
  title        = {Assisted Feature Engineering and Feature Learning to Build Knowledge-Based Agents for Arcade Games},
  year         = {2017},
  pages        = {228--238},
  editor       = {Jaap van den Herik  and Ana Paula Rocha and Joaquim Filipe},
  booktitle    = {ICAART 2017 - Proceedings of the 4th International Conference on Agents and Artificial Intelligence},
  volume       = {2 - Artificial Intelligence},
  location     = {Porto, Portugal},
  month        = {February 6-8},
  publisher    = {SciTePress},
  isbn         = {978-989-758-220-2},
  abstract     = {Human knowledge can greatly increase the performance
                  of autonomous agents. Leveraging this knowledge is
                  sometimes neither straightforward nor easy. In this
                  paper, we present an approach for assisted feature
                  engineering and feature learning to build
                  knowledge-based agents for three arcade games within
                  the Arcade Learning Environment. While existing
                  approaches mostly use model-free approaches we aim
                  at creating a descriptive set of features for world
                  modelling and building agents. To this end, we
                  provide (visual) assistance in identifying and
                  modelling features from RAM, we allow for learning
                  features based on labeled game data, and we allow
                  for creating basic agents using the above
                  features. In our evaluation, we compare different
                  methods to learn features from the RAM. We then
                  compare several agents using different sets of
                  manual and learned features with one another and
                  with the state-of-the-art.},
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021