Social agents and robots will require both learning and emotional capabilities to successfully enter society. This paper connects both challenges, by studying models of emotion generation in sequential decision-making agents. Previous work in this field has focussed on model-free reinforcement learning (RL). However, important emotions like hope and fear need anticipation, which requires a mode...

This paper focuses on enhancing the effectiveness of filter feature selection models from two aspects. First, feature-searching engine is modified based on optimization theory. Second, a point injection strategy is designed to improve the regularization capability of feature selection. The second topic is important, because overfitting is usually experienced. To evaluate the proposed strategies...

In this paper a planning framework based on Ant Colony Optimization techniques is presented. Optimal planning is a very hard computational problem which has been coped with different methodologies. Approximate methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several ...

Using a large data set on web browsing and purchasing behavior we test to what extent consumers are searching in accordance to various classical search models. We find that the benchmark model of sequential search with a known distributions of prices can be rejected based on the recall patterns we observe in the data. Moreover, we show that even if consumers are initially unaware of the price d...