Authors:
Bastian Andelefski
and
Stefan Schiffer
Affiliation:
RWTH Aachen University, Germany
Keyword(s):
Assisted Feature Engineering, Feature Learning, Arcade Learning Environment, Knowledge-Based Agents.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Intelligent User Interfaces
;
Knowledge Discovery and Information Retrieval
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Visualization
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.