Multi-Entity Bayesian Networks Research Articles

Reasoning about location of robot-operated object based on probabilistic ontologies

Background: A robot needs to acquire the location of objects when performing daily tasks. Compared to industrial robots, a service robot faces a more complex and unstructured working environment where the location of the target object is usually uncertain. For example, due to diversity in personal habits and seasons, apples may be located in refrigerators, tables, or other places in the living environment. Methods: We propose a novel method for semantic localization of the robot-operated object based on probabilistic ontologies (PR-OWL) and multi-entity Bayesian networks (MEBN). The probabilistic web ontology language is used to describe and model the highly uncertain knowledge about the storage location of objects in the human household environment. Furthermore, the target location is inferred based on the multi-entity Bayesian network. Results: The proposed method is capable to adapting to environmental changes and achieves reliable probability estimation of object location. Experiments on simulated robotic tasks verify the effectiveness of the method. Conclusions: We show that applying PR-OWL combined with MEBN to locate the target object for the robot is feasible， which can improve the cognitive and self-adaptive ability of the robot.

Proactive and reactive context reasoning architecture for smart web services

The web of things (WoT) uses web technologies to connect embedded objects to each other and to deliver services to stakeholders. The context of these interactions (situation) is a key source of information which can be sometimes uncertain. In this paper, we focus on the development of intelligent web services. The main requirements for intelligent service are to deal with context diversity, semantic context representation and the capacity to reason with uncertain information. From this perspective, we propose a framework for intelligent services to deal with various contexts, to reactively respond to real-time situations and proactively predict future situations. For the semantic representation of context, we use PR-OWL, a probabilistic ontology based on multi-entity Bayesian networks. PR-OWL is flexible enough to represent complex and uncertain contexts. We validate our framework with an intelligent plant watering use case to show its reasoning capabilities.

A semantic approach for situation-aware ubiquitous learner support

This paper thoroughly introduces a semantic web approach for supporting decision making in ubiquitous learning environment. Probabilistic ontology will be proposed to reduce inaccuracy, randomness, and incompleteness and will support automated reasoning to support the learner choice of the learning intention, strategy, media and resource according to the predicted learning situation. The multi-entity Bayesian networks (MEBNs) was used for modelling the knowledge and analysing the content in context-aware ubiquitous learning. In fact, MEBNs offer a rigorous framework for knowledge representing and reasoning with probabilistic inference. Finally, a case study has been presented confirming the effectiveness of the proposed model.

A semantic approach for situation-aware ubiquitous learner support

This paper thoroughly introduces a semantic web approach for supporting decision making in ubiquitous learning environment. Probabilistic ontology will be proposed to reduce inaccuracy, randomness, and incompleteness and will support automated reasoning to support the learner choice of the learning intention, strategy, media and resource according to the predicted learning situation. The multi-entity Bayesian networks (MEBNs) was used for modelling the knowledge and analysing the content in context-aware ubiquitous learning. In fact, MEBNs offer a rigorous framework for knowledge representing and reasoning with probabilistic inference. Finally, a case study has been presented confirming the effectiveness of the proposed model.

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

A Probabilistic, Ontological Framework for Safeguarding the Intangible Cultural Heritage

In this article, we propose Multi-Entity Bayesian Networks (MEBNs) as the probabilistic ontological framework for the analysis of the Tsamiko and Salsa dances. More specifically, our analysis has the objective of the dancer assessment with respect to both choreography execution accuracy and the synchronization of the dance movements with the musical rhythm. For this task, we make use of the explicit, expert-provided knowledge on dance movements and their relations to the musical beat. Due to the complexity of this knowledge, the MEBNs were used as the probabilistic ontological framework in which the knowledge is formalized. The reason we opt for MEBNs for this task is that they combine Bayesian and formal (first-order) logic into a single model. In this way, the Bayesian probabilistic part of MEBNs was used to capture, using example data and training, the implicit part of the expert knowledge about dances, i.e., this part of the knowledge that cannot be formalized and explicitly defined accurately enough, while the logical maintains the explicit knowledge representation in the same way ontologies do. Moreover, we present in detail the MEBN models we built for Tsamiko and Salsa, using expert-provided explicit knowledge. Last, we conduct experiments that demonstrate the effectiveness of the proposed MEBN-based methodology we employ to achieve our analysis objectives. The results of the experiments demonstrate the superiority of MEBNs to conventional models, such as BNs, in terms of the dancer assessment accuracy.

A learner model based on multi-entity Bayesian networks and artificial intelligence in adaptive hypermedia educational systems

A learner model based on multi-entity Bayesian networks and artificial intelligence in adaptive hypermedia educational systems

Survey of Multi Entity Bayesian Network (MEBN) and its applications in probabilistic reasoning.

Bayesian networks have been at the core of the research pertaining to probabilistic reasoning. Several machine learning algorithms and techniques highly rely on Bayesian networks for their reasoning capabilities. Since several decades Bayesian networks proved to be the essential tool in the hands of researchers working in artificial intelligence domain. Yet Bayesian networks do have certain limitations [1], Bayesian networks need extensions to be more expressive and functional for probabilistic reasoning needs of various domains. Multi Entity Bayesian Networks (MEBN) is a theory combining expressivity of first-order logic principles and probabilistic reasoning of Bayesian networks. The paper thoroughly introduces the MEBN theory with an example modeling for a problem description and discusses various other works which included MEBN theory as a core of their research. The study in this paper concludes with the current highlights and challenges in MEBN and future developments.

Attention Assist: A High-Level Information Fusion Framework for Situation and Threat Assessment in Vehicular Ad Hoc Networks

Driver inattentiveness constitutes the main cause of road accidents, which makes it a major factor in road safety. In this paper, we propose a comprehensive framework to address the road safety problem by tackling it from a high-level information fusion standpoint, considering vehicular ad hoc networks (VANETs) as the deployment platform. The proposed framework relies on the multientity Bayesian networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Bayesian networks in handling uncertainty. First, the entities that influence the inattention phenomenon, as well as both their causal and semantic relationships, are identified. Next, an MEBN-based high-level information fusion framework is proposed through which entities, situations, and their relationships in specific contexts are modeled using MEBN fragments. Furthermore, MEBN inference is used to assess the situations of interest by estimating their states. To demonstrate the capabilities of the proposed framework, a collision warning system simulator has been developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of local and global information sources available in various VANET environments. If the threat of being in a near-collision situation is determined to be high, then the driver is warned accordingly. Our experimental results for two distinct single-vehicle and multivehicle categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capability of the proposed framework to efficiently achieve situation and threat assessment on the road.

SBN Constructing Algorithm Based on Extended MEBN

SBN(Series Bayesian Network) model and constructing algorithm of BN are introduced, and advantages and disadvantages of constructing SBN with MEBN(multi-entity Bayesian networks) are pointed out. Aiming at the demands of constructing SBN, expression of Markov MFrag (MEBN Fragment) within MEBN system is clarified, and probability mapping pseudo MFrags are added, then SBN constructing algorithm based on Markov extended MEBN system is studied and the constructing procedures are illuminated by an example.

Information and communication technology in personalized medicine: a clinical use-case for hepatocellular cancer

Scientific objectives This study explores ways in which the requirements and interrelationships between Personalized Medicine (PM), clinical medical practice, and basic medical research could be best served by information and communication technologies (ICT). To avoid the problems inherent in formulating ICT solutions in isolation, a use-case was developed employing hepatocellular carcinoma (HCC). The subject matter was approached from four separate, but interrelated, tasks: (1) review of current understanding and clinical practices relating to HCC; (2) propose an ICT system for dealing with the vast amount of information relating to HCC, including clinical decision support and research needs; (3) determine the ways in which a clinical liver cancer center can contribute to this ICT approach; and, (4) examine the enhancements and impact that the first three tasks, and therefore Personalized Medicine, will have on the management of HCC. The development of an IT System for Personalized Healthcare (ITS-PHC) for HCC will provide a comprehensive system to identify and then determine the relative value of the wide number of variables or Information Entities (IEs): (1) factors reflecting clinical assessment of the patient including functional status, liver function, degree of cirrhosis, and comorbidities; (2) factors reflecting tumor biology, at a molecular, genetic and anatomic level; (3) factors reflecting tumor burden and individual patient response; and (4) factors reflecting medical and operative treatments and their outcomes. Technological approaches It is our hypothesis, that if we can utilize patient-specific modeling techniques to generate valid Digital Patient Models (DPM) (utilizing these IEs) we may be able to develop a statistically valid methodology for predicting diseases, predicting treatment outcomes, preventing diseases or complications, and developing personalized treatment regimens. We are calling this proposed system Model-Based Medical Evidence (MBME), and as yet remains undeveloped. It is further postulated that MultiEntity Bayesian Networks (MEBN) used in the construction of the DPM will be utilized in the development of a practical decision support system. Literature regarding HCC was analyzed, combining epidemiology; risk factors; infectious etiologies; pathology, microenvironment and biomarkers; screening and diagnostic technologies; treatment modalities. IEs, that will be used to populate the patient databases and MEBNs required for data mining and decision support, were identifed. This information was also used to reinforce a well-established treatment protocol, i.e. the Barcelona treatment algorithm, and, to add extensions that include enhanced screening and greater specifics regarding treatment selections.

MEBN: A language for first-order Bayesian knowledge bases

Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the best-understood and most widely applied formalism for computational scientific reasoning under uncertainty. Increasingly expressive languages are emerging for which the fundamental logical basis is probability. This paper presents Multi-Entity Bayesian Networks (MEBN), a first-order language for specifying probabilistic knowledge bases as parameterized fragments of Bayesian networks. MEBN fragments (MFrags) can be instantiated and combined to form arbitrarily complex graphical probability models. An MFrag represents probabilistic relationships among a conceptually meaningful group of uncertain hypotheses. Thus, MEBN facilitates representation of knowledge at a natural level of granularity. The semantics of MEBN assigns a probability distribution over interpretations of an associated classical first-order theory on a finite or countably infinite domain. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. A proof is given that MEBN can represent a probability distribution on interpretations of any finitely axiomatizable first-order theory.