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Abstract - Machine Learning || Roni Khardon
Associate Professor - Tufts University
Markov decision processes (MDP) capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. Relational MDPs capture problems where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. For example, a system managing shipment of packages using trucks and airplanes must optimize for short delivery time and low costs. Such problems are often huge in size and yet offer potential for efficient algorithmic solutions taking advantage of their structure.
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