Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Logic Programming and Nonmonotonic Reasoning
This volume contains the refereed proceedings of the 11th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2011, held in May 2011 in Vancouver, Canada. The 16 revised full papers (13 technical papers, 1 application description, and 2 system descriptions) and 26 short papers (16 technical papers, 3 application description, and 7 system descriptions) which were carefully reviewed and selected from numerous submissions, are presented together with 3 invited talks. Being a forum for exchanging ideas on declarative logic programming, nonmonotonic reasoning, and knowledge representation, the conference aims to facilitate interactions between those researchers and practitioners interested in the design and implementation of logic-based programming languages and database systems, and those who work in the area of knowledge representation and nonmonotonic reasoning.
Logic Programming and Nonmonotonic Reasoning
This book constitutes the refereed proceedings of the 15th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2019, held in Philadelphia, PA, USA, in June 2019. The 22 full papers and 3 short papers presented in this volume were carefully reviewed and selected from a total of 39 submissions. The papers were organized in topical sections named: applications; argumentation; foundations and complexity; knowledge representation and reasoning; and systems.
Judgment Aggregation
Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it. Table of Contents: Preface / Acknowledgments / Logic Meets Social Choice Theory / Basic Concepts / Impossibility / Coping with Impossibility / Manipulability / Aggregation Rules / Deliberation / Bibliography / Authors' Biographies / Index
Introduction to Graph Neural Networks
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed...
Graph-Based Semi-Supervised Learning
While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promis...
A Concise Introduction to Models and Methods for Automated Planning
Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, a...
Logic Programming
This book constitutes the refereed proceedings of the 25th International Conference on Logic Programming, ICLP 2009, held in Pasadena, CA, USA, in July2009. The 29 revised full papers together with 9 short papers, 4 invited talks, 4 invited tutorials, and the abstracts of 18 doctoral consortium articles were carefully reviewed and selected from 69 initial submissions. The papers cover all issues of current research in logic programming, namely semantic foundations, formalisms, nonmonotonic reasoning, knowledge representation, compilation, memory management, virtual machines, parallelism, program analysis, program transformation, validation and verification, debugging, profiling, concurrency, objects, coordination, mobility, higher order, types, modes, programming techniques, abductive logic programming, answer set programming, constraint logic programming, inductive logic programming, alternative inference engines and mechanisms, deductive databases, data integration, software engineering, natural language, web tools, internet agents, artificial intelligence, bioinformatics.
ECAI 2014
The role of artificial intelligence (AI) applications in fields as diverse as medicine, economics, linguistics, logical analysis and industry continues to grow in scope and importance. AI has become integral to the effective functioning of much of the technical infrastructure we all now take for granted as part of our daily lives. This book presents the papers from the 21st biennial European Conference on Artificial Intelligence, ECAI 2014, held in Prague, Czech Republic, in August 2014. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as we...
General Game Playing
General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue, general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves. General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. GGP is an interesting application in its own right. It is intellectually engaging and more than a little fun. But it is much more than that. It provides a theoretica...
Lifelong Machine Learning, Second Edition
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past he...
