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GECCO
2007
Springer
137views Optimization» more  GECCO 2007»
14 years 4 months ago
Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dynamic optimization problems online, i.e. as time goes by. For a proper design, t...
Peter A. N. Bosman, Han La Poutré
CHI
2011
ACM
13 years 1 months ago
The mathematical imagery trainer: from embodied interaction to conceptual learning
We introduce an embodied-interaction instructional design, the Mathematical Imagery Trainer (MIT), for helping young students develop grounded understanding of proportional equiva...
Mark Howison, Dragan Trninic, Daniel Reinholz, Dor...
ATAL
2005
Springer
14 years 3 months ago
Discovering strategic multi-agent behavior in a robotic soccer domain
2. THE MASM ALGORITHM An input to the MASM algorithm is a time-annotated multi-agent action sequence. The action sequence is then transformed into an action graph. An action graph ...
Andraz Bezek
ECAL
1999
Springer
14 years 2 months ago
Integrating Unsupervised Learning, Motivation and Action Selection in an A-life Agent
How can we expect an A-life Agent to learn how to perform tasks when it is not told what those tasks are, and it is not provided any indication or feedback as to its performance? ...
Mark Witkowski
IJCAI
2007
13 years 11 months ago
Representations for Action Selection Learning from Real-Time Observation of Task Experts
The association of perception and action is key to learning by observation in general, and to programlevel task imitation in particular. The question is how to structure this info...
Mark A. Wood, Joanna Bryson