On truth-gaps, bipolar belief and the assertability of vague propositions

AbstractThis paper proposes an integrated approach to indeterminacy and epistemic uncertainty in order to model an intelligent agentʼs decision making about the assertability of vague statements. Initially, valuation pairs are introduced as a model of truth-gaps for propositional logic sentences. These take the form of lower and upper truth-valuations representing absolutely true and not absolutely false respectively. In particular, we consider valuation pairs based on supervaluationist principles and also on Kleeneʼs three-valued logic. The relationship between Kleene valuation pairs and supervaluation pairs is then explored in some detail with particular reference to a natural ordering on semantic precision. In the second part of the paper we extend this approach by proposing bipolar belief pairs as an integrated model combining epistemic uncertainty and indeterminacy. These comprise of lower and upper belief measures on propositional sentences, defined by a probability distribution on a finite set of possible valuation pairs. The properties of these measures are investigated together with their relationship to different types of uncertainty measure. Finally, we apply bipolar belief measures in a preliminary decision theoretic study so as to begin to understand how the use of vague expressions can help to mitigate the risk associated with making forecasts or promises. This then has potential applications to natural language generation systems

Uncertainty management in assessment of FMEA expert based on negation information and belief entropy

The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty. To deal with this issue, we propose a new uncertainty management approach for the assessments given by experts based on negation information and belief entropy in the Dempster–Shafer evidence theory framework. First, the assessments of FMEA experts are modeled as basic probability assignments (BPA) in evidence theory. Next, the negation of BPA is calculated to extract more valuable information from a new perspective of uncertain information. Then, by utilizing the belief entropy, the degree of uncertainty of the negation information is measured to represent the uncertainty of different risk factors in the RPN. Finally, the new RPN value of each failure mode is calculated for the ranking of each FMEA item in risk analysis. The rationality and effectiveness of the proposed method is verified through its application in a risk analysis conducted for an aircraft turbine rotor blade

Failure mode and effects analysis on the air system of an aero turbofan engine sing the Gaussian model and evidence theory

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine

An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion

Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed world where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the open world by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the open world assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed world wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few open issues still exist in the current work: the necessary properties for a belief entropy in the open world assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty

Structural Model for Estimating the Influence of Healthy Lifestyle on Episodic Memory in Adults with Subjective Memory Complaints

The aim of this study is to examine the relationships between a healthy lifestyle and episodic memory among adults with subjective memory complaints (SMCs). We proposed a structure equation model to study the association between a healthy lifestyle and episodic memory with an investigation covering 309 participants over 50 years old with SMCs. The model showed a good fit after being adjusted (p=0.054, goodness of fit index=0.981, adjusted goodness of fit index=0.956, comparative fit index=0.981, and root mean square error of approximation=0.049): a healthy lifestyle has a direct positive effect on episodic memory among adults with SMCs (β=0.60). The research model provides possible guidelines for medical staff to prevent the cognitive function decline in the risk population of Alzheimer’s disease