AAAI Publications, Twenty-Second IAAI Conference

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Learning from Sensors and Past Experience in an Autonomous Oceanographic Probe
Albert Vilamala, Enric Plaza, Josep Lluis Arcos

Last modified: 2010-07-05


The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine regions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phytoplankton structures. Because the system has to work inboard, a main goal of our approach is to dramatically reduce the dimensionality of the problem. Specifically, our development uses two AI techniques, namely Particle Swarm Optimization and Case-Based Reasoning.We report results of experiments performed with simulated environments.


Particle Swarm Optimization; Case-Based Reasoning

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