Titlebook: Artificial Intelligence XXXV; 38th SGAI Internatio Max Bramer,Miltos Petridis Conference proceedings 2018 Springer Nature Switzerland AG 20

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束縛

龍卷風(fēng)

cathartic

Tuning the Discount Factor in Order to Reach Average Optimality on Deterministic MDPsce of being insensitive to different policies with equivalent average. This paper analyzes the impact of such different criteria on a series of experiments, and then provides a threshold for the discount factor in order to ensure average optimality for discounted-optimal policies in the deterministic case.

爆米花

Conference proceedings 2018uling; Machine Learning; Industrial Applications of Artificial Intelligence; Planning and Scheduling in Action; Machine Learning in Action; Applications of Machine Learning; and Applications of Agent Systems and Genetic Algorithms..

獸群

0302-9743 and Scheduling; Machine Learning; Industrial Applications of Artificial Intelligence; Planning and Scheduling in Action; Machine Learning in Action; Applications of Machine Learning; and Applications of Agent Systems and Genetic Algorithms..978-3-030-04190-8978-3-030-04191-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

碳水化合物

隨意

https://doi.org/10.1057/9781137521903 and rule based systems can take advantage of this parallelism. It is hoped that this work will support further exploration of parallel neural and rule based systems, and support further work in cognitive modelling and cognitive architecture.

SLUMP

Helen Silva de Joyce,Susan Feez properties of the pairs drive exploitation. We adopt curriculum learning to organise the ordering of pairs at training time using similarity knowledge as a heuristic for pair sorting. The results of our experimental evaluation show that these strategies are key to optimising training.

Cerumen

Research and Practice in Applied Linguisticsance of an existing Genetic Algorithm that does Work Area Optimization. We will also compare our approach with prior knowledge generated with the help of human experts with years of experience in the field. We show that our new approach is as good as or better in generating the prior knowledge when compared to human experts.