Feature Markov Decision Processes (ΦMDPs) [Hut09] are well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale realworld problems. In this article I extend ΦMDP to ΦDBN. The primary contribution is to derive a cost criterion that allows...

This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions. In this paper, the construction of a DBN based on simulati...

Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.

In this paper, we investigate the use of dynamic Bayesian networks (DBNs) to explicitly represent models of hidden features, such as articulatory or other phonological features, for automatic speech recognition. In previous work using the idea of hidden features, the representation has typically been implicit, relying on a single hidden state to represent a combination of features. We present a...

This paper explores a strategy for determining public space safety. Due to varied purposes and locations, each public space has architecture as well as facilities. A generalized analysis of capacities for public spaces is essential. The method we propose is to examine a public space with a given architecture. We used Bayesian Belief Network to determine the level of safety and identify points o...

This paper proposes a proactive management system for the events that occur across multiple personal user devices, including desktop PCs, laptops, and smart phones. We implemented the Personal Event Management Service using Dynamic Bayesian Networks (PEMS-DBN) system that proactively executes appropriate tasks across multiple devices without explicit user requests by recognizing the user’s devi...

We study dynamic reliability of systems where system components age with a constant failure rate and there is a budget constraint. We develop a methodology to effectively prepare a predictive maintenance plan of such a system using dynamic Bayesian networks (DBNs). DBN representation allows monitoring the system reliability in a given planning horizon and predicting the system state under diffe...

In this paper, we focus on inferring social roles in conversations using information extracted only from the speaking styles of the speakers. We model the turn-taking behavior of the speakers with dynamic Bayesian networks (DBNs), which provide the capability of naturally formulating the dependencies between random variables. More specifically, we first explore the usefulness of a simple DBN, n...

in this research, the decision on belief (dob) approach was employed to analyze and classify the states of uni-variate quality control systems. the concept of dob and its application in decision making problems were introduced, and then a methodology for modeling a statistical quality control problem by dob approach was discussed. for this iterative approach, the belief for a system being out-o...

Given a belief network with evidence, the task of finding the l most probable ex­ planations (MPE) in the belief network is that of identifying and ordering the l most probable instantiations of the non-evidence nodes of the belief network. Although many approaches have been proposed for solving this problem, most work only for restricted topologies (i.e., singly connected belief net­ works). I...