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Artificial Intelligence Research and Development
For almost twenty years the Catalan Association of Artificial Intelligence (ACIA) has been promoting cooperation between researchers in artificial intelligence within the Catalan speaking community. This book presents the proceedings of the 16th International Conference (CCIA 2013), held at the University of Vic (UVIC), Catalonia, Spain, in October 2013. This annual conference aims to foster discussion of the latest developments in artificial intelligence within the community of Catalan countries, as well as amongst members of the AI community worldwide. The book contains the 26 full papers, 5 short papers and 12 poster presentations from the conference, which are grouped under the following topics: relational learning, planning; satisfiability and constraints; perception and image processing; preprocessing; patterns extraction and learning; post-processing, model interpretability and decision support; recommenders, similarity and CBR; and multiagent systems.
Monitoring and Visualizing Membrane-Based Processes
This much-needed critical review of the main monitoring techniques conveys profound knowledge of their fundamentals, possibilities and limits, strengths and weaknesses when applied to membrane processes, clearly demonstrating which technique is most suitable for a given process. A practical approach is adopted throughout, providing case studies for the monitoring of selected membrane-based processes. After an introductory section, the book goes on to look at optical and electronic microscopic techniques, followed by electrical, laser and acoustic techniques, and finishes off with process-oriented monitoring techniques. For both researchers and professionals working in the industry.
Robot Motion and Control 2007
Robot Motion Control 2007 presents very recent results in robot motion and control. Forty-one short papers have been chosen from those presented at the sixth International Workshop on Robot Motion and Control held in Poland in June 2007. The authors of these papers have been carefully selected and represent leading institutions in this field.
Intelligent Autonomous Systems 7
The goal of the Seventh International Conference on Intelligent Autonomous Systems (IAS-7) was to exchange and stimulate research ideas that make future robots and systems more intelligent and autonomous. This volume of proceedings contains 71 technical papers by authors from 15 countries.
From Natural to Artificial Neural Computation
This volume presents the proceedings of the International Workshop on Artificial Neural Networks, IWANN '95, held in Torremolinos near Malaga, Spain in June 1995. The book contains 143 revised papers selected from a wealth of submissions and five invited contributions; it covers all current aspects of neural computation and presents the state of the art of ANN research and applications. The papers are organized in sections on neuroscience, computational models of neurons and neural nets, organization principles, learning, cognitive science and AI, neurosimulators, implementation, neural networks for perception, and neural networks for communication and control.
Teaching and Learning in Multilingual Contexts
It is clearly illogical to search for one good, universal solution for multilingual education when educational contexts differ so widely due to demographic and social factors. The situation is further complicated by the motivations of learners and teachers, and by attitudes towards multilingualism and ‘otherness’. The studies in this volume seek to investigate not only whether certain solutions and practices are ‘good’, but also when and for whom they make sense. The book covers a wide range of Western multilingual contexts, and uncovers common themes and practices, shared aims and preoccupations, and often similar solutions, within seemingly diverse contexts. In addition to chapters based on empirical data, this book offers theoretical contributions in the shape of a discussion of the appropriateness of L1-Ln terminology when discussing complex multilingual realities, and looks at how the age factor works in classroom settings.
Computational Kinematics
Computational kinematics is an enthralling area of science with a rich spectrum of problems at the junction of mechanics, robotics, computer science, mathematics, and computer graphics. The covered topics include design and optimization of cable-driven robots, analysis of parallel manipulators, motion planning, numerical methods for mechanism calibration and optimization, geometric approaches to mechanism analysis and design, synthesis of mechanisms, kinematical issues in biomechanics, construction of novel mechanical devices, as well as detection and treatment of singularities. The results should be of interest for practicing and research engineers as well as Ph.D. students from the fields of mechanical and electrical engineering, computer science, and computer graphics.
Eighth International Work-Conference on Artificial and Natural Neural Networks
We present in this volume the collection of finally accepted papers of the eighth edition of the “IWANN” conference (“International Work-Conference on Artificial Neural Networks”). This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evolutionary systems). Since the first edition of IWANN in Granada (LNCS 540, 1991), the Artificial Neural Network (ANN) community, and the domain itself, have matured and evolved. Under the ANN banner we find a very heterogeneous scenario with a main interest and objective: to better understand nature and beings for the correct elaboration of theories, models an...
Modelling and Planning for Sensor Based Intelligent Robot Systems
This edited and reviewed volume consists of papers that were originally presented at a workshop in the Scientific Center at Schloss Dagstuhl, Germany. It gives an overview of the field and presents the latest developments in the areas of modeling and planning for sensor based robots. The particular topics addressed include active vision, sensor fusion, environment modeling, motion planning, robot navigation, distributed control architectures, reactive behavior, and others.
Reinforcement Learning
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears ...
