Abstract
Computer games are attracting increasing interest in the Artificial Intelligence (AI) research community, mainly because games involve reasoning, planning and learning [1]. One area of particular interest in the last years is the creation of adaptive game AI. Adaptive game AI is the implementation of AI in computer games that holds the ability to adapt to changing circumstances, i.e., to exhibit adaptive behavior during the play. This kind of adaptation can be created using Machine Learning techniques, such as neural networks, reinforcement learning and bioinspired methods. In order to learn online, a system needs to overcome the main difficulties imposed by games: processing time and memory requirements. Learning in a game needs to be fast and the memory available is usually not enough to store a large number of training examples to a traditional Machine Learning technique. In this context, methods for mining data streams seem to be a natural approach. Data streams are, by definition, sequences of training examples that arrive over time [2]. In the data stream scenario, algorithms are usually incremental and capable of adapting the decision model when a change in the distribution of the training examples is detected. One particularly interesting algorithm for mining data streams is the Very Fast Decision Tree (VFDT) [3]. VFDTs are incremental decision trees designed specifically to meet the data stream problem requirements. In this paper, we analyse the use of VFDTs in the task of learning in a Computer RolePlaying Game context. First, we simulate data from manually designed tactics for a Computer RolePlaying Game, based on Spronck's static tactics [4], and test the suitability of VFDT to rapid learn these tactics. Afterwards, we conduct an experiment in order to simulate a data stream of examples where changes of tactics occur over time, and analyse how VFDT and some of its variations respond to these changes in the target concept. © 2010 IEEE.

Abstract
The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation.

Abstract

Abstract
This work addresses the problem of mining data stream generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnosis degradations of this process, using change detection mechanisms, and self-repairs the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learns can detect re-occurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models.

Abstract
In recent years we witnessed an impressive advance in the social networks field, which became a "hot" topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of trajectory. Our visualization strategy is based on trajectories of network's actors in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors by comparing the shape and position of the trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.

Abstract
In this work we address the problem of monitoring the evolution of clusters, which became an important research issue in recent years due to our ability to collect and store data that evolves over time. The evolution is traced through the detection and categorization of transitions undergone by clusters' structures computed at different points in time. We adopt two main strategies for cluster characterization - representation by enumeration and representation by comprehension -, and propose the MEC (Monitor of the Evolution of Clusters) framework, which was developed along the lines of the change mining paradigm. MEC includes a taxonomy of various types of clusters' transitions, a tracking mechanism that depends on cluster representation, and a transition detection algorithm. Our tracking mechanism can be subdivided in two methods, devised to monitor clusters' transitions: one based on graph transitions, and another based on clusters' overlap. To demonstrate the feasibility and applicability of MEC we present real world case studies, using datasets from different knowledge areas, such as Economy and Education.