Research and Development in Intelligent Systems XXIX

Sample

Mehdi Dastani, Jomi F. Hübner, Brian Logan

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Workshop on Programming Multi-Agents Systems held in Valencia, Spain, in June 2012. The 10 revised full papers presented were carefully selected from 14 submissions covering a wide range of topics in multi-agent system programming languages, including language design and efficient implementation, agent communication, and robot programming. I addition to these regular papers, the volume includes six papers from the Multi-Agent programming Contest 2012 (MAPC).

Sample

Sebastian Thrun, Lorien Pratt

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.

Sample

Xiang Sean Zhou, Yong Rui, Thomas S. Huang

Exploration of Visual Data presents latest research efforts in the area of content-based exploration of image and video data. The main objective is to bridge the semantic gap between high-level concepts in the human mind and low-level features extractable by the machines. The two key issues emphasized are "content-awareness" and "user-in-the-loop". The authors provide a comprehensive review on algorithms for visual feature extraction based on color, texture, shape, and structure, and techniques for incorporating such information to aid browsing, exploration, search, and streaming of image and video data. They also discuss issues related to the mixed use of textual and low-level visual features to facilitate more effective access of multimedia data. Exploration of Visual Data provides state-of-the-art materials on the topics of content-based description of visual data, content-based low-bitrate video streaming, and latest asymmetric and nonlinear relevance feedback algorithms, which to date are unpublished.

Sample

Abdul Sattar, ed

This book constitutes the refereed proceedings of the 10th Australian Joint Conference on Artificial Intelligence, AI'97, held in Perth, Australia, in November/December 1997. The volume presents 48 revised full papers selected from a total of 143 submissions. Also included are three keynote talks and one invited paper. The book is divided into topical sections on constraint satisfaction and scheduling, computer vision, distributed AI, evolutionary computing, knowledge-based systems, knowledge representation and reasoning, learning and machine vision, machine learning, NLP and user modeling, neural networks, robotics and machine recognition, and temporal qualitative reasoning.