SBIA, the Brazilian Symposium on Arti?cial Intelligence, is a biennial event intended to be the main forum of the AI community in Brazil. The SBIA 2004 was the 17th issue of the series initiated in 1984. Since 1995 SBIA has been accepting papers written and presented only in English, attracting researchers from all over the world. At that time it also started to have an international program committee, keynote invited speakers, and proceedings published in the Lecture Notes in Arti?cial Intelligence (LNAI) series of Springer (SBIA 1995, Vol. 991, SBIA 1996, Vol. 1159, SBIA 1998, Vol. 1515, SBIA 2000, Vol. 1952, SBIA 2002, Vol. 2507). SBIA 2004 was sponsored by the Brazilian Computer Society (SBC). It was held from September 29 to October 1 in the city of S ̃ ao Luis, in the northeast of Brazil, together with the Brazilian Symposium on Neural Networks (SBRN). This followed a trend of joining the AI and ANN communities to make the joint event a very exciting one. In particular, in 2004 these two events were also held togetherwiththeIEEEInternationalWorkshoponMachineLearningandSignal Processing (MMLP), formerly NNLP. The organizationalstructure of SBIA 2004was similar to other international scienti?cconferences.Thebackboneofthe conferencewasthe technicalprogram whichwascomplementedbyinvitedtalks,workshops,etc.onthemainAItopics.

This book constitutes the refereed proceedings of the 2nd International Joint Conference of the 10th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2006, and the 18th Brazilian Artificial Intelligence Symposium, SBIA 2006. The book presents 62 revised full papers together with 4 invited lectures. Topical sections include AI in education and intelligent tutoring systems, autonomous agents and multiagent systems, computer vision and pattern recognition, evolutionary computation and artificial life, and more.

This book constitutes the refereed proceedings of the 19th Brazilian Symposium on Artificial Intelligence, SBIA 2008, held in Salvador, Brazil, in October 2008. The 27 revised full papers presented together with 3 invited lectures and 3 tutorials were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on computer vision and pattern recognition, distributed AI: autonomous agents, multi-agent systems and game knowledge representation and reasoning, machine learning and data mining, natural language processing, and robotics.

This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.

This book constitutes the refereed proceedings of the 21st Brazilian Symposium on Artificial Intelligence, SBIA 2012, held in Curitiba, Brazil, in October 2012. The 23 revised full papers presented were carefully reviewed and selected from 81 submissions. The papers cover the following topics: knowledge representation, machine learning, machine learning and computer vision, agent-based and multi-agent systems, robotics and language, as well as constraints.

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

This book constitutes the proceedings of the 20th Brazilian Symposium on Artificial Intelligence, SBIA 2010, held in São Bernardo do Campo, Brazil, in October 2010. The 31 papers presented were carefully reviewed and selected from 91 submissions. The topics covered are: ontologies, knowledge representation and reasoning; machine learning; autonomous agents and multiagent systems; natural language processing; planning and scheduling; constraints and search; and logics for AI.

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.