Agent-based Modeling and Simulation (ABMS) is a new modeling paradigm and is one of the most exciting practical developments in modeling since the invention of relational database. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. The goals of this paper are to show how ABMS is: - Useful: Why ABMS is a good and even better modeling approach in many cases: a) Usable: How we are progressively advancing to usable ABMS systems, with better software development environments and more application experiences, and, b) Used: How ABMS is being used to solve practical problems. We proposed three types of agents to treat the system in a hierarchical way. In this paper the operation of the Agent Node is designed, and implemented.

This paper describes reactive and tracking control of a mobile robot using integration methods to combine the response of both types of controls based on fuzzy logic. Simulation and experimental results of the complete system demonstrates the effectiveness of the proposed approach.

In this paper a Learning Object Recommendation system is proposed. Learning Objects (LOs) in this context are reusable Web based resources (i.e. a web page, a video or images) that support some learning activity. The system follows a hybrid approach, combining two collaborative filtering (CF) algorithms and a fuzzy inference system (FIS) defined by the instructor. This allows the instructor to adopt the role of facilitator, making recommendations when necessary, but allowing students to work together whenever possible. We propose that the final recommendation assigned to a LO, is the weighted average of the three models: Instructor, Profile and Correlation. Finally another FIS is used to determine the weights of these recommendations, the assignment of weights aims to compensate for some of the shortcomings of collaborative filtering algorithms. An experimental evaluation of this approach is presented.

In this paper we compare four machine learning techniques for blog comments spam filtering. the machine learning techniques are the Naïve Bayes, K-nearest neighbor, neural networks and the support vector machines. For this comparative study we used a blog comment corpus that has been affected by spam, which is our study case in this work. We classify the comments of this blog comments corpus, which have 50 pages and 1024 blog comments are classified in spam an non-spam. The percentage of spam of this corpus is 67%.