High dimensionality of belief space in DEC-POMDPs is one of the major causes that makes the optimal joint policy computation intractable. The belief state for a given agent is a probability distribution over the system states and the policies of other agents. Belief compression is an efficient POMDP approach that speeds up planning algorithms by projecting the belief state space to a low-dimens...

The computational complexity of nite horizon policy evaluation and policy existence problems are studied for several policy types and representations of Markov decision processes. In almost all cases, the problems are shown to be complete for their complexity classes; classes range from nondeterministic logarithmic space and probabilistic logarithmic space (highly parallelizable classes) to exp...

Model checking temporal properties is often reduced to finding accepting cycles in Büchi automata. A key ingredient for an effective distributed model checking technique is a distribution policy that does not split the potential accepting cycles of the corresponding automaton among several nodes. In this paper, we introduce a distribution policy to reduce the number of split cycles. This policy...

The computational complexity of nite horizon policy evaluation and policy existence problems are studied for several policy types and representations of Markov decision processes. In almost all cases, the problems are shown to be complete for their complexity classes; classes range from nondeterministic logarithmic space and probabilistic logarithmic space (highly parallelizable classes) to exp...

The virtual tax village in line with the transformation of the tax process from self-declaration based method to electronic data based method and with the aim of compiling and developing the tax information bank and using technology, taxpayers in a virtual tax space. It collects the ID card holders and their financial data with the help of modern technologies and by benefiting from systematic m...

We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner’s goal is to find the best policy, or way of behaving, in some space of policies, although “be...

A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature spaces, which are reproducing kernel Hilbert spaces. Distributions over states given the observations are obtained by applying the kernel Bayes’ rule to these distribution embeddings. Policies and value functions are defin...

This article examines the evolving policy context of municipal open-space acquisition in New Jersey. The authors hypothesize that voters’ interest in open-space protection is sensitive to changes in state policy and that municipal acquisition may have exclusionary effects. The authors examine local acquisition practices using three different approaches: voting behavior, municipal acquisition, a...

Is Open Space Preservation a Form of Exclusionary Zoning? The Evolution of Municipal Open-Space Policies in New Jersey Stephan Schmidt Cornell University Kurt Paulsen University of Wisconsin, Madison Abstract: This paper examines the evolving policy context of municipal open space acquisition in New Jersey. We hypothesize that voters’ interest in open space protection is sensitive to changes in...