In this paper, we describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition; and, in case of having ...

This paper gives an algebraic derivation of the posterior for both the noisy-or and naive Bayes models, as a function of both input messages and probability table parameters. By examining these functions we show a technique where the naive Bayes model may be used to approximate a logical-OR, rather than its typical interpretation as a logicalAND. The technique is to avoid the use of disconfirmi...

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This metho...

BACKGROUND Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluat...

In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients...

Recurrent Neural Networks (RNNs) have seen a massive surge in popularity in recent years, particularly with the advent of modern architectures such as LSTMs. These sophisticated modern models have resulted in significant performance gains across a number of challenging tasks. Despite their success, we still struggle to provide a rigorous theoretical analysis of these models, or to truly underst...

1 Introduction

Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for...

MOTIVATION The main goal in this paper is to develop accurate probabilistic models for important functional regions in DNA sequences (e.g. splice junctions that signal the beginning and end of transcription in human DNA). These methods can subsequently be utilized to improve the performance of gene-finding systems. The models built here attempt to model long-distance dependencies between non-ad...

Wide-angle sonar mapping of the environ­ ment by mobile robot is nontrivial due to sev­ eral sources of uncertainty: dropouts due to "specular" reflections, obstacle location un­ certainty due to the wide beam, and distance measurement error. Earlier papers address the latter problems, but dropouts remain a problem in many environments. We present an approach that lifts the overoptimistic in­ d...