Book | Chapter
Autonomous hypothesis generation as an environment learning mechanism for agent design
pp. 210-225
Abstract
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.
Publication details
Published in:
Randall Marcus (2015) Artificial life and computational intelligence: first Australasian conference, acalci 2015, Newcastle, nsw, India, february 5-7, 2015. proceedings. Dordrecht, Springer.
Pages: 210-225
DOI: 10.1007/978-3-319-14803-8_17
Full citation:
Wang Bing, Merrick Kathryn E., Abbass Hussein A. (2015) „Autonomous hypothesis generation as an environment learning mechanism for agent design“, In: M. Randall (ed.), Artificial life and computational intelligence, Dordrecht, Springer, 210–225.