Markov games are a generalization of Markov decision process to a multi-agent setting. Two-player zero-sum Markov game framework offers an effective platform for designing robust controllers. This paper presents two novel controller design algorithms that use ideas from game-theory literature to produce reliable controllers that are able to maintain performance in presence of noise and paramete...

In this paper we introduce a novel approach to continual planning and control, called Dynamics Based Control (DBC). The approach is similar in spirit to the Actor-Critic [6] approach to learning and estimation-based differential regulators of classical control theory [13]. However, DBC is not a learning algorithm, nor can it be subsumed within models of standard control theory. We provide a gen...

By combining information theory, statistical decision theory, and maximum entropy to address the decision fusion problems, a statistical decision fusion theory is obtained. The theory explains why decision fusion is so difficult and why the performance of decision fusion systems does not always meet expectations. The theory suggests how statistical decision systems such as the conceptual "Famil...

In this paper, we formulate six different resolutions of a continuous-time approximation of the Wright-Fisher sample genealogical process. We derive Markov chains for the six different approximations in the spirit of J.F.C. Kingman. These Markov chains are essential for inference methods. One of the resolutions is the well-known n-coalescent due to Kingman. The second resolution was mentioned b...

Behavioral accounting research deals with a complex set of phenomenon including the broad domain of human decision making under uncertainty. Two aspects of decision making of particular relevance to accounting and auditing research are two constructs that are inexorably interrelated: uncertainty and information (evidence). This paper introduces a theoretical perspective that enriches the knowle...

We describe a decision-theoretic approach to providing user oriented and resource-adapted navigation instructions within buildings. Although information is broadcast by infrared senders in a unidirectional manner, we found it possible to treat the problem of assigning each sender both a user-oriented and resource-adapted broadcast program as a fully observable Markov decision problem.

While the philosophical literature has extensively studied how decisions relate to arguments, reasons and justifications, decision theory almost entirely ignores the latter notions and rather focuses on preference and belief. In this article, we argue that decision theory can largely benefit from explicitly taking into account the stance that decision-makers take towards arguments and counter-a...

Discrete time Markov processes with multidimensional compact state space are considered where the coordinate processes are locally interacting and change their states synchronously. The interaction structure of the process is determined by some general graph. Decision makers control synchronuosly the system's behaviour on the coordinate level using only local information. Conditions are given w...

We consider the Hamiltonian cycle problem (HCP) embedded in a controlled Markov decision process. In this setting, HCP reduces to an optimization problem on a set of Markov chains corresponding to a given graph. We prove that Hamiltonian cycles are minimizers for the trace of the fundamental matrix on a set of all stochastic transition matrices. In case of doubly stochastic matrices with symmet...