A Protocol and Tool for Developing a Descriptive Behavioral Model

Fuzzy rules have been used to model complex human behavior in order to develop sophisticated industrial control systems. The use of fuzzy rules to create a behavioral model provides a quantitative basis for discussing the contribution of elements of the model to theories about the behavior. The application of a protocol and tool simplifies the development of a behavioral model from observational data. Extraction of a high level, linguistic behavioral model from the observational data is used to discover knowledge about the data. Tuning of the model is accomplished by parameter optimization through the adjustment of membership functions using the genetic fuzzy, self-adaptive system. A case study demonstrating the use of the protocol and tool is presented. In the study, a behavioral model is developed that integrates the analysis of the observational data with Social Network Analysis. The integrated behavioral model provides an effective platform for a quantitative analysis of the activities impacting behavior.  M.S

Triangulation of Bayesian networks with recursive estimation of distribution algorithms

AbstractBayesian networks can be used as a model to make inferences in domains with intrinsic uncertainty, that is, to determine the probability distribution of a set of variables given the instantiation of another set. The inference is an NP-hard problem. There are several algorithms to make exact and approximate inference. One of the most popular, and that is also an exact method, is the evidence propagation algorithm of Lauritzen and Spiegelhalter [S.L. Lauritzen, D.J. Spiegelhalter, Local computations with probabilities on graphical structures and their application on expert systems, Journal of the Royal Statistical Society B 50 (2) (1988) 157–224], improved later by Jensen et al. [F.V. Jensen, S.L. Lauritzen, K.G. Olesen, Bayesian updating in causal probalistic networks by local computations, In Computational Statistics Quaterly 4 (1990) 269–282]. This algorithm needs an ordering of the variables in order to make the triangulation of the moral graph associated with the original Bayesian network structure. The effectiveness of the inference depends on the variable ordering. In this paper, we will use a new paradigm for evolutionary computation, the estimation of distribution algorithms (EDAs), to get the optimal ordering of the variables to obtain the most efficient triangulation. We will also present a new type of evolutionary algorithm, the recursive EDAs (REDAs). We will prove that REDAs improve the behaviour of EDAs in this particular problem, and that their results are competitive with other triangulation techniques

Triangulation of Bayesian networks with recursive estimation of distribution algorithms

Bayesian networks can be used as a model to make inferences in domains with intrinsic uncertainty, that is, to determine the probability distribution of a set of variables given the instantiation of another set. The inference is an NP-hard problem. There are several algorithms to make exact and approximate inference. One of the most popular, and that is also an exact method, is the evidence propagation algorithm of Lauritzen and Spiegelhalter [S.L. Lauritzen, D.J. Spiegelhalter, Local computations with probabilities on graphical structures and their application on expert systems, Journal of the Royal Statistical Society B 50 (2) (1988) 157–224], improved later by Jensen et al. [F.V. Jensen, S.L. Lauritzen, K.G. Olesen, Bayesian updating in causal probabilistic networks by local computations, In Computational Statistics Quaterly 4 (1990) 269–282]. This algorithm needs an ordering of the variables in order to make the triangulation of the moral graph associated with the original Bayesian network structure. The effectiveness of the inference depends on the variable ordering. In this paper, we will use a new paradigm for evolutionary computation, the estimation of distribution algorithms (EDAs), to get the optimal ordering of the variables to obtain the most efficient triangulation. We will also present a new type of evolutionary algorithm, the recursive EDAs (REDAs). We will prove that REDAs improve the behaviour of EDAs in this particular problem, and that their results are competitive with other triangulation techniques