This paper proposes a novel user intention simulation method which is a data-driven approach but able to integrate diverse user discourse knowledge together to simulate various type of users. In Markov logic framework, logistic regression based data-driven user intention modeling is introduced, and human dialog knowledge are designed into two layers such as domain and discourse knowledge, then ...

Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regre...

The use of ‘standard’ regression analysis to derive predictive equations for software development has recently been complemented by increasing numbers of analyses using less common methods, such as neural networks, fuzzy logic models, and regression trees. This paper considers the implications of using these methods and provides some recommendations as to when they may be appropriate. A compari...

We propose a formal framework for designing hybrid systems by stepwise reenement. Starting with a speciication in hybrid temporal logic, we make successively more transitions explicit until we obtain an executable system.

Land use (LU) information is of significant value for various urban studies and is needed for a wide variety of decision-making initiatives in the range of global, regional and urban areas. A challenge that researchers and practitioners have been facing in urban modeling/planning is the lack of detailed information regarding how cities are structured and how urban development evolves. This stud...

This paper considers how relational probabilistic models adapt to population size. First we show that what are arbitrary choices for nonrelational domains become a commitment to how a relational model adapts to population change. We show how this manifests in a directed model where the conditional probabilities are represented using the logistic function, and show why it needs to be extended to...

Attempts at combining logic and neural networks into neurosymbolic approaches have been on the increase in recent years. In a system, symbolic knowledge assists deep learning, which typically uses sub-symbolic distributed representation, to learn reason higher level of abstraction. We present Logic Tensor Networks (LTN), framework that supports querying, learning reasoning with both rich data a...