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

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