Autonomous Hypothesis Generation as an Environment Learning Mechanism for Agent Design

• Published in: Artificial Life and Computational Intelligence pp. 210 - 225
• Main Authors: Wang, Bing, Merrick, Kathryn E., Abbass, Hussein A.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: autonomous agents; causal relations; co-evolutionary algorithms
• ISBN: 3319148028; 9783319148021
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-14803-8_17

Studies on agent design have been focused on the internal structure of an agent that facilities decision-making subject to domain specific tasks. The domain and environment knowledge of an artificial agent is often hard coded by system engineers, which is both time-consuming and task dependent. In order to enable an agent to model its general environment with limited human involvement, in this paper, we first define a novel autonomous hypothesis generation problem. Consequently, we present two algorithms as its solutions. Experiments show that an agent using the proposed algorithm can correctly reconstruct its environment model to a certain extent.