Thm. 3.2] we can construct an SCM

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Many state-of-the-art algorithms for solving Partially Observable Markov Decision Processes (POMDPs) rely on turning the problem into a "fully observable" problem--a belief MDP--and exploiting the piece-wise linearity and convexity

Much of everyday human thinking and learning can be understood in terms of program induction: constructing a procedure that maps inputs to desired outputs, based on observing example input-output pairs.

Clustering, and in particular center based clustering, are central problems in unsupervised learning.

The resulted estimator reduces to that of [47] for T = 0, and [44] for T = 1 and γ = 1 .

In this paper, we propose a methodology designed to support decision-making during the execution phase of military ground combat operations, with a focus on one's actions. This methodology generates and evaluates recommendations for various courses of action for a mechanized battalion, commencing with an initial set assessed by their anticipated outcomes. It systematically produces thousands of individual action alternatives, followed by evaluations aimed at identifying alternative courses of action with superior outcomes. These alternatives are appraised in light of the opponent's status and actions, considering unit composition, force ratios, types of offense and defense, and anticipated advance rates. Field manuals evaluate battle outcomes and advancement rates. The processes of generation and evaluation work concurrently, yielding a variety of alternative courses of action. This approach facilitates the management of new course generation based on previously evaluated actions. As the combat unfolds and conditions evolve, revised courses of action are formulated for the decision-maker within a sequential decision-making framework.