The value 1 problem is a natural decision problem in algorithmic game theory. For partially observable Markov decision processes with reachability objective, this problem is defined as follows: are there observational strategies that achieve the reachability objective with probability arbitrarily close to 1? This problem was shown undecidable recently. Our contribution is to introduce a class o...

Heuristic search algorithms can find solutions that take the form of a simple path (A*), a tree or acyclic graph (AO*). We present a novel generalization of heuristic search (called LAO*) that can find solutions with loops, that is, solutions that take the form of a cyclic graph. We show that it can be used to solve Markov decision problems without evaluating the entire state space, giving it a...

The directional and proximity models offer dramatically different theories for how voters make decisions and fundamentally divergent views of the supposed microfoundations on which vast bodies of literature in theoretical rational choice and empirical political behavior have been built. We demonstrate here that the empirical tests in the large and growing body of literature on this subject amou...

The directional and proximity models o er dramatically di erent theories for how voters make decisions, and fundamentally divergent views of the supposed microfoundations on which vast literatures in theoretical rational choice and empirical political behavior have been built. We demonstrate here that the empirical tests in the large and growing literature on this subject amount to theoretical ...

As classical planning branches out to consider richer models, many extensions approach decision theoretic models. Decision theory research uses models like MDPs and POMDPs which are very expressive, but can be difficult to scale. Whereas, planning research concentrates quite a bit on scalability. Our previous work and future doctoral thesis concentrates on extending the planning model toward pr...

Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorith...

One of the most important challenges in understanding expert perception is in determining what information in a complex scene is most valuable (reliable) for a particular task, and how experts learn to exploit it. For the task of parameter estimation given multiple independent sources of data, Bayesian data fusion provides a solution to this problem that involves promoting data to a common para...

This paper has two objectives. The rst is to introduce a unifying framework for risk-sensitive control of Markov Decision Processes (MDPs). The framework includes, as special cases, a number of approaches that have been considered in the literature. The second objective is to apply risk-sensitive criteria to the control of queueing systems. We generalize the uniformization methodology that rela...

The problem of formulating plans under uncertainty and coping with dynamic decision problems is a major task of both artificial intelligence and control theory applications in medicine. In this paper we will describe a software package, called DT-Planner, designed to represent and solve dynamic decision problems that can be modelled as Markov decision processes, by exploiting a novel graphical ...

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