Nature

Probabilistic Model Checking and Markov Decision Processes

Probabilistic model checking and Markov decision processes (MDPs) form two interlinked branches of formal analysis for systems operating under uncertainty. These techniques offer a mathematical ...
Nature

Markov Decision Processes and Stochastic Control

Markov decision processes (MDPs) and stochastic control constitute pivotal frameworks for modelling decision-making in systems subject to uncertainty. At their core, MDPs provide a structured means to ...
JSTOR Daily

Partially Observable Total-Cost Markov Decision Processes with Weakly Continuous Transition Probabilities

This paper describes sufficient conditions for the existence of optimal policies for partially observable Markov decision processes (POMDPs) with Borel state, observation, and action sets, when the ...
JSTOR Daily

Optimality of Quasi-Open-Loop Policies for Discounted Semi-Markov Decision Processes

Quasi-open-loop policies consist of sequences of Markovian decision rules that are insensitive to one component of the state space. Given a semi-Markov decision process (SMDP), we distinguish between ...

How Clear Probabilistic Models Can Strengthen Intelligent Systems in an Uncertain World

Software engineer Sai Bhargav Yalamanchi notes that mathematical tools helping practitioners interpret uncertainty have ...