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A Data-Driven Framework For Decision Making Under Uncertainty: Integrating Markov Decision Processes, Hidden Markov Models And Predictive Modeling, Hossein Kamalzadeh

Operations Research and Engineering Management Theses and Dissertations

The problem of decision making under uncertainty can be broken down into two parts. First, how do we learn about the world? This involves the problem of modeling the system and its uncertainty. Secondly, given what we currently know about the world, how should we decide what to do, taking into account uncertainty of future events and observations that may change our conclusions. Many systems evolve over time and often the next state of the system is not known with certainty, often modeled as a probability distribution over system states. Dealing with such systems especially when we can make a …