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(2015) Artificial life and computational intelligence, Dordrecht, Springer.
Autonomous hypothesis generation as an environment learning mechanism for agent design
Bing Wang , Kathryn E. Merrick , Hussein A. Abbass
pp. 210-225
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
DOI: 10.1007/978-3-319-14803-8_17
Full citation:
Wang, B. , Merrick, K. E. , Abbass, H. A. (2015)., Autonomous hypothesis generation as an environment learning mechanism for agent design, in M. Randall (ed.), Artificial life and computational intelligence, Dordrecht, Springer, pp. 210-225.
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