Is It Rational to Be Moral?

Why should all rational agents be moral? This is one ancient and challenging question about moral motivation. But there is another perhaps more tractable question about moral motivation. Why as a matter of fact are most of us motivated by moral considerations? What is it about the kind of creature I am that inclines me to be moral? Moral judgments (e.g. that it’s right to give to a certain charity) seem to be directly motivating. This chapter argues that even non-moral normative judgments often are directly motivating. A primary form of rule representation automatically carries with it motivation force.

Rule Based Networks: An Efficient and Interpretable Representation of Computational Models

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

Interval-Valued Belief Rule Inference Methodology Based on Evidential Reasoning-IRIMER

In this paper, an interval-valued belief rule inference methodology based on evidential reasoning (IRIMER) is proposed, which includes the interval-valued belief rule representation scheme and its inference methodology. This interval-valued belief rule base is designed with interval-valued belief degrees embedded in both the consequents and the antecedents of each rule, which can represent uncertain information or knowledge more flexible and reasonable than the previous belief rule base. Then its inference methodology is developed on the interval-valued evidential reasoning (IER) approach. The IRIMER approach improves and extends the recently uncertainty inference methods from the rule representation scheme and the inference framework. Finally, a case is studied to demonstrate the concrete implementation process of the IRIMER approach, and comparison analysis shows that the IRIMER approach is more flexible and effective than the RIMER [J. B. Yang, J. Liu, J. Wang, H. S. Sii and H. W. Wang, Belief rule-base interference methodology using the evidential reasoning approach-RIMER, IEEE Transaction on Systems Man and Cybernetics Part A-Systems and Humans36 (2006) 266–285.] approach and the ERIMER [J. Liu, L. Martínez, A. Calzada and H. Wang, A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems 53 (2013) 129–141.] approach.