Chairs: Alneu de Andrade Lopes - alneu@icmc.usp.br
            Roseli Aparecida Romero - romero.roseliaparecida@gmail.com

All tutorials are free for registered participants.

Kenneth A. De Jong - Sun 21/10 14:00
Title: Evolutionary Computation: A Unified Approach
The field of Evolutionary Computation has experienced tremendous growth over the past 15 years, resulting in a wide variety of evolutionary algorithms and applications.  The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to  se the relationships between them, assess strengths and weaknesses, and make good choices for new application areas.
This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices.   The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it.
Finally, the framework is used to identify some important open issues that need further research.

Thomas G. Dietterich - Sun 21/10 16:30
Title: Machine Learning in Ecological Science and Environmental Policy
There are many exciting opportunities for machine learning research in ecological science and environmental policy. This tutorial will present examples of existing and emerging applications of machine learning and challenges for machine learning research.  The tutorial is organized around a "pipeline" from data collection through the development of machine learning models to the optimization of management policies.  The tutorial is divided into three main sections.
Section 1: Data acquisition.  We describe the many kinds of sensors (including human observers) that are employed in ecological research and describe three case studies of the application of machine learning: optimal sensor placement, computer vision for species monitoring, and machine learning for analysis of Doppler weather radar.
Section 2: Machine Learning of Ecological Models. We first describe static models of species distribution with three case studies: Species Distribution Models (SDMs) from presence-only data, SDMs eBird (bird watcher) data, and SDMs that also model detection and observer expertise.  Second, we describe dynamical models including Collective Graphical Models for species migration and Meta-Population Models.
Section 3: Policy Optimization. This section describes four case studies and associated machine learning research problems: linking populations of Red Cockaded Woodpeckers (an endangered species), robust optimal solution of Markov Decision Processes (MDPs) for fisheries management, solution of spatio-temporal MDPs for wildfire management, and application of Partially-Observable Markov Decision Processes for managing difficult-to-observe invasive species.

Anthony Hunter - Sun 21/10 14:00
Title: Introduction to Computational Models of Argument
Computational Models of Argument are being developed with the aim of reflecting how human argumentation uses conflicting information to construct and analyse arguments. Argumentation involves identifying arguments and counterarguments relevant to an issue (e.g. What are the pros and cons for the safety of mobile phones for children?). Argumentation may also involve weighing, comparing, or evaluating arguments (e.g. What sense can we make of the arguments concerning mobile phones for children?) and it may involve drawing conclusions (e.g. A parent answering the question "Are mobile phones safe for my children?"). In addition, argumentation may involve convincing an audience (e.g. A politician making the case that mobile phones should be banned for children because the risk of radiation damage is too great). In this tutorial, we will consider both graph-based and logic-based formalizations of argumentation, introducing some of the basic concepts, and reviewing a range of proposals and results.

Silvia Botelho, Paulo Dews, Luan Silveira, Diones Fischer, Felipe Almeida, Matheus Longaray - Sun 21/10 8:30
Title: Vamos Construir nossos próprios Robôs?
ROS - Robot Operating System
Um SDK para Robótica e Ambientes Inteligentes
Este tutorial visa introduzir o Sistema Operacional ROS (Robot Operating System), abordando seus principais conceitos, funcionalidades e potencialidades. Originalmente desenvolvido pelo Laboratório de Inteligência Artificial da Universidade de Stanford, atualmente, o ROS é uma plataforma livre, disponibilizada sob licença BSD, para o desenvolvimento de redes inteligentes de sensores e atuadores, indo desde ambientes de realidade aumentada, até aplicativos em computação ubíqua e redes de sensores. Com ampla utilização no meio acadêmico e comercial o ROS está sendo considerado como a principal ferramenta para o desenvolvimento de aplicações robóticas, fornecendo bibliotecas e aplicativos de auxílio ao desenvolvimento de sistemas robóticos autônomos e customizados. Neste curso pretende-se abordar os principais conceitos relativos a abstração de hardware, drivers de dispositivos, bibliotecas, visualizadores, troca de mensagem, gerenciamento de pacotes; em aplicações associadas à robótica inteligente e smart environments. O ROS é disponibilizado para os sistemas operacionais Unix-like, como MacOS e Linux. Recentemente, o ROS também foi disponibilizado para o sistema operacional android, desenvolvido pela Google, e padrão em tablets e smartphones. Com isso, o ROS amplia seu potencial de uso, permitindo novas funcionalidades e aplicações.

Renato Assunção - Sun 21/10 8:30
Title: Bayesian Nonparametrics and its applications.
Many problems in biology, computational vision, and natural language processing are adopting procedures based on a novel class of models for Bayesian statistics and machine learning, the Dirichlet process (DP). For instance, Latent Dirichlet Allocation is a popular approach in entity extraction on webpages and topic analysis for news articles and comments. The main appeal of DP based-methods is that they can scale their complexity with data, while representing uncertainty in both the parameters and the structure. However, the DP mathematical foundations are challenging and this is seen as a difficulty to their more widespread use. The set of all possible solutions for a given learning problem is the parameter space and
in DP models we have an infinite dimensional parameter space. For instance, in nonlinear regression, the parameter space could be the set of all smooth functions; in a density estimation problem, it could be the set of all probability densities. DP assumes a prior distribution specified on this infinite dimensional space and this constitutes a Bayesian nonparametric model. This tutorial will present the main aspects of DP and some illustrative applications in bioinformatics and information retrieval. We will introduce its definition, representations, inference algorithms, as well as some generalizations such as Polya trees.

Scott Sanner - Sun 21/10 16:30
Title: Recent Advances in Continuous Planning.
This tutorial will outline the importance of continuous variables and actions in deterministic and stochastic planning and how one can cope with the complexity of planning for such problems in practice. The first part of the tutorial will focus on modeling real-world phenomena both abstractly (deterministic transition models, MDPs, POMDPs) and in terms of formal planning languages (PDDL, PPDDL, and RDDL).
The second part of the tutorial will focus on solution methods, ranging from search in deterministic settings, through to approximation methods such as discretization and sampling.From this point, the tutorial will shift to an in-depth focus on very recent work that allows the exact solution of an expressive range of continuous planning problems via a technique referred to as Symbolic Dynamic Programming. The tutorial will conclude by briefly connecting continuous planning to related fields such as Control Theory and Scheduling.

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