Paternities search with object-oriented bayesian networks

Paternity dispute problems are examples of situations in which forensic approach the DNA profiles study is a common procedure. To implement this approach an efficient tool are the object-oriented Bayesian networks (OOBN). Along this paper are presented the various OOBN adequate to solve the simple paternity dispute and more complex paternity dispute problems with incomplete DNA profiles data about the putative father such as: only putative grandfather information, only putative uncle information, only putative father ‘s uncle information and only simultaneously putative uncle and putative father’s uncle information. Here it is exhibited an algebraic treatment, for the simple problem and with those the use of the object-oriented Bayesian networks is shown. Then the most complex kind of problems that may occur is presented. Although these are not the most common cases there is notice of its occurrence at least in Portuguese courts

Parameter learning algorithms for continuous model improvement using operational data

In this paper, we consider the application of object-oriented Bayesian networks to failure diagnostics in manufacturing systems and continuous model improvement based on operational data. The analysis is based on an object-oriented Bayesian network developed for failure diagnostics of a one-dimensional pick-and-place industrial robot developed by IEF-Werner GmbH.We consider four learning algorithms (batch Expectation-Maximization (EM), incremental EM, Online EM and fractional updating) for parameter updating in the object-oriented Bayesian network using a real operational dataset. Also, we evaluate the performance of the considered algorithms on a dataset generated from the model to determine which algorithm is best suited for recovering the underlying generating distribution. The object-oriented Bayesian network has been integrated into both the control software of the robot as well as into a software architecture that supports diagnostic and prognostic capabilities of devices in manufacturing systems. We evaluate the time performance of the architecture to determine the feasibility of online learning from operational data using each of the four algorithms. © Springer International Publishing AG 2017

A Situation Analysis Decision Support System Based on Dynamic Object Oriented Bayesian Networks

This paper proposes a situation analysis decision support system (SADSS) for safety of safety-critical systems where the operators are stressed by the task of understanding what is going on in the situation. The proposed SADSS is developed based on a new model-driven engineering approach for hazardous situations modeling based on dynamic object oriented Bayesian networks to reduce the complexity of the decision-making process by aiding operators’ cognitive activities. The SADSS includes four major elements: a situation data collection based on observable variables such as sensors, a situation knowledgebase which consists of dynamic object oriented Bayesian networks to model hazardous situations, a situation analysis which shows the current state of hazardous situations based on risk concept and possible near future state, and a humancomputer interface. Finally two evaluation methods for partial and full validation of SADSS are presented

Modelling complex large scale systems using object oriented Bayesian networks (OOBN)

The aim of this communication is to present a new way of how to structure modelling process of complex and large scale systems by object oriented Bayesian network (OOBN) for risk assessment and management purpose. In the first stage, we extend OOBN by presenting a new definition that introduces some flexibility, in a second stage, dynamic Bayesian networks (DBN) described by OOBN method are presented, that leads to a framework that we refer to as Dynamic Objet Oriented Bayesian Network (DOOBN). A demonstration in the domain of risk assessment of flash floods effect on the infrastructures inoperability is considered to show potential applicability of the extended OOBN

Object-Oriented Bayesian Networks for a Decision Support System

We study an economic decision problem where the actors are two rms and the Antitrust Authority whose main task is to monitor and prevent rms potential anti-competitive behaviour. The Antitrust Au- thority's decision process is modelled using a Bayesian network whose relational structure and parameters are estimated from data provided by the Authority itself. Several economic variables in uencing this de- cision process are included in the model. We analyse how monitoring by the Antitrust Authority aects rms cooperation strategies. These are modelled as a repeated prisoners dilemma using object-oriented Bayesian networks, thus enabling integration of rms decision process and external market information.Antitrust Authority, Bayesian networks, mergers, model integration, prisoners dilemma, repeated games.

Технологія моделювання на основі нечітких об’єктно-орієнтованих байєсівських мереж довіри

Представлені основні компоненти інформаційної технології індуктивного моделювання причинно-наслідкових зв’язків в умовах невизначеності на основі нечітких об’єктно-орієнтованих байєсівських мереж довіри. Технологія побудована на основі алгоритмів трансформації байєсівської мережі в вузлове дерево. Розглянуті нові більш ефективні алгоритми трансформації байєсівської мережі, отримані в результаті модифікації відомих алгоритмів, які ґрунтуються на використанні додаткової інформації про графічне представлення мережі. Конструктивно викладена функціональна модель, яка призначена для реалізації процесів трансформації нечіткої об’єктно-орієнтованої байєсівської мережі довіри.Представлены основные компоненты информационной технологии индуктивного моделирования причинно-следственных связей в условиях неопределенности на основе нечетких объектно-ориентированных байесовских сетей доверия. Технология построена на основе алгоритмов трансформации байесовской сети в узловое дерево. Рассмотрены новые более эффективные алгоритмы трансформации байесовской сети, полученные в результате модификации известных алгоритмов, основанных на использовании дополнительной информации о графическом представлении сети. Конструктивно изложена функциональная модель, которая предназначена для реализации процессов трансформации нечеткой объектно-ориентированной байесовской сети доверия.The basic components of information technology inductive modeling causation under uncertainty based on fuzzy object-oriented Bayesian networks is proposed. The technology is based on a combination of transformation algorithms Bayesian network in the junction tree. New more efficient algorithms for Bayesian network transformation are resulted from modifications known algorithms; algorithms based on the use of more information on the graphical representation of the network are considered. Structurally functional model are described, it is designed to implement the transformation of fuzzy object-oriented Bayesian networks

Investigation of a crime scene with two victims and a perpetrator through DNA traces

To deal with crime identification problems, that are examples of situations in which forensic approach the DNA profiles is frequent, it is needed an introduction to present and explain the various concepts involved. So the use of object-oriented Bayesian networks (OOBN), examples of probabilistic expert systems (PES), is shown and exemplified.info:eu-repo/semantics/acceptedVersio

Structured probabilistic inference

AbstractProbabilistic inference is among the main topics with reasoning in uncertainty in AI. For this purpose, Bayesian Networks (BNs) is one of the most successful and efficient Probabilistic Graphical Model (PGM) so far. Since the mid-90s, a growing number of BNs extensions have been proposed. Object-oriented, entity-relationship and first-order logic are the main representation paradigms used to extend BNs. While entity-relationship and first-order models have been successfully used for machine learning in defining lifted probabilistic inference, object-oriented models have been mostly underused. Structured inference, which exploits the structural knowledge encoded in an object-oriented PGM, is a surprisingly unstudied technique. In this paper we propose a full object-oriented framework for PRM and propose two extensions of the state-of-the-art structured inference algorithm: SPI which removes the major flaws of existing algorithms and SPISBB which largely enhances SPI by using d-separation