New hybrid cultural algorithm with local search (HCALS) is introduced to solve traveling salesman problem (TSP). The algorithm integrates the local search method into the cultural algorithm which uses social intelligence to guide and lead individuals in the population. Better solutions can be attained by controlling individuals which are locally optimized by the local search methods. The combination of these two methods can be a promising method for solving the combinatorial problems like TSP. The experimental results show that the proposed algorithm can find better solutions than other methods for TSP in most cases.

This paper presents a novel agent system which provides a user with useful recommendations of behavior and information based on behavior prediction. The agent understands user’s context from a GPS sensor and a mobile phone, and predicts user’s future behavior based on user’s context. And the prediction of user’s future behavior can be used to provide the user with the suggestion of performing behavior and/or the recommendation of information which are relevant to the predicted user’s behavior. Here, behavior prediction is performed by Dynamic Bayesian Network, which can handle time-series data. The experimental results showed that the proposed agent system was enough to provide convenient recommendation services to users.

Data analysis including outlier is more difficult to the analysis without outlier. The outlier has a chance to increase the misclassification rate and the variance of estimate in the supervised learning like classification and regression. Also the outlier becomes a cluster in the clustering as unsupervised learning. So we are hard to represent the clustering result. Because of the previous problems, it is removed generally for constructing model in data mining. But when the outlier has some information on given data, we must not remove it from training data set. In this paper, using kernel PCA (principal component analysis) and factor scores, we propose a preprocessing method to contain the outlier in the modeling. The outlier effect of given training data set is reduced by the values of kernel PCA and factor scores. We verify improved performance of our work by the experimental results using simulation data sets in regression model.

This paper presents a ringtone recommendation agent system which utilizes user's emotion to recommend an appropriate ringtone and the suitable volume level of a mobile phone. The system uses a Bayesian network (BN) to infer user's emotion and the volume level at the current situation. After inferring user's emotion, a ringtone is selected from the pool of various kinds of genres of ringtones. Also, the volume level can be controlled according to the inferred volume level. User's feedback on the inferred values can be used to retrain the BN to provide the user with a personalized service. Experimental results showed that the proposed system is quite convenient and very useful.

According to the development of robot technology, human-robot interaction (HRI) is the field of study highlighted. The study aims to find the goal of human action considering their intention and behavior based on their respective habits. To gain the principle of behavior on the goal by understanding that of human, engineers draw the inference of the result needed from planning through HRI. In this paper, plan inference for aimed goal is modeled by calculating with probability what task system performs through the observed behavior. Dynamic Bayesian network (DBN) uses the probabilistic inference to reveal the relation of data varying according to time. Machine repository pioneer data of UCI has proved that accuracy and efficiency of inference is higher than the existing DBN by lowering useless calculation applying the variable elimination method and the concept of dynamic programming for DBN algorithm.