Abstract
The challenge of the Rescue Simulation League is for a team of robots or agents to learn an optimal response to mitigate the effects of natural disasters. To operate optimally, several problems have to be jointly solved like task allocation, path planning, and coalition formation. Solve these difficult problems can be quite overwhelming for newcomer teams. We created a tutorial that demonstrates how these problems can be tackled using artificial intelligence and machine learning algorithms available in the Open image in new window and the Open image in new window . Here we show (1) how to analyze and model disaster scenario data for developing rescue decision-making algorithms, and (2) how to incorporate state-of-the-art machine learning algorithms into Rescue Agent Simulation competition code using the Open image in new window Engine API for Java.
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