Policy Explanation and Model Refinement in Decision-Theoretic Planning
OZ Khan - 2013 - uwspace.uwaterloo.ca
Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for
sequential decision-making under uncertainty. MDPs provide a generic framework that can …
sequential decision-making under uncertainty. MDPs provide a generic framework that can …
Minimization of State Uncertainty in Planning using ML Algorithms
AN Soni - Ankit Narendrakumar Soni (2020). Minimization of …, 2020 - papers.ssrn.com
We present a novel methodology for diminishing state vulnerability in arranging before
tackling the arranging issue. This is finished by making predictions about the state based on …
tackling the arranging issue. This is finished by making predictions about the state based on …
[PDF][PDF] Monte Carlo Tree Search with Fixed and Adaptive State Abstractions
Monte Carlo tree search (MCTS) is a popular approach to solving Markov decision problems
with large state spaces due to the relative insensitivity of MCTS algorithms to the size of the …
with large state spaces due to the relative insensitivity of MCTS algorithms to the size of the …
[PDF][PDF] Lessons Learned from Creating a Course Advising Tool
We detail some lessons learned while designing and testing a course selection tool for
undergraduates at a large state university. Between 2009–2011 we conducted two surveys …
undergraduates at a large state university. Between 2009–2011 we conducted two surveys …
[PDF][PDF] Research Statement: Theory and Data for Better Decisions
N Mattei - nickmattei.net
My research broadly centers around the theory and practice of artificial intelligence (AI) and
its applications; I am enticed by problems that require a blend of techniques to develop …
its applications; I am enticed by problems that require a blend of techniques to develop …