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Decision making is all around us. Everyone makes choices everyday, from the moment we open our eyes in the morning. Some of them do not have very important consequences in our life. However, in the business world, managers make decisions that have a great impact on the future of their own company. Furthermore, when several competing agents are involved in a decision making situation and the combination of their actions affects each other's revenues, the problem becomes more complicated. When we are aware of someone observing and reacting to our behavior, one might occasionally prefer a…mehr

Produktbeschreibung
Decision making is all around us. Everyone makes choices everyday, from the moment we open our eyes in the morning. Some of them do not have very important consequences in our life. However, in the business world, managers make decisions that have a great impact on the future of their own company. Furthermore, when several competing agents are involved in a decision making situation and the combination of their actions affects each other's revenues, the problem becomes more complicated. When we are aware of someone observing and reacting to our behavior, one might occasionally prefer a sub-optimal choice aimed at causing confusion on the adversary, so that it will be more difficult for him to guess our decision in future encounters, which may report us a larger benefit. The objective of this book is the analysis and design of adversarial decision and optimization-based models which are able to represent adversarial situations. We are going to conduct theoretical studies and propose practical applications including imitation games and patrolling domains.
Autorenporträt
Pablo J. Villacorta obtained his MSc in Computer Engineering in 2009, his MSc in Statistics in 2012 (National Extraordinary Award) and his PhD in Computer Science and AI (adversarial decision making) in 2015, all from University of Granada, Spain. He currently works as a data scientist in Big Data industry using open-source tools like R and Spark.