Solving Markovian decision processes by successive elimination of variables

Voting by Successive Elimination andStrategic

We study the impact of strategic choices of self-interested candidates of whether or not to enter an election. We focus on strategic candidacy in the context of the tree and binary voting procedures used in many settings, including committees. We ooer a comprehensive analysis for the special but important case of voting by successive elimination. Strategic candidacy slightly enlarges the set of...

Online Learning in Markovian Decision Processes

Non-Deterministic Policies in Markovian Decision Processes

Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional meth...

Solving Concurrent Markov Decision Processes

Typically, Markov decision problems (MDPs) assume a single action is executed per decision epoch, but in the real world one may frequently execute certain actions in parallel. This paper explores concurrent MDPs, MDPs which allow multiple non-conflicting actions to be executed simultaneously, and presents two new algorithms. Our first approach exploits two provably sound pruning rules, and thus...

Solving Hybrid Markov Decision Processes

Markov decision processes (MDPs) have developed as a standard for representing uncertainty in decision-theoretic planning. However, MDPs require an explicit representation of the state space and the probabilistic transition model which, in continuous or hybrid continuousdiscrete domains, are not always easy to define. Even when this representation is available, the size of the state space and t...