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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 9th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2007, held in Tempe, AZ, USA, May 2007. This conference encompasses theoretical studies, design and implementation of logic based programming languages and database systems, and development of experimental systems.
General Orders
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Robot Learning from Human Teachers
Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminolo...
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
Network Embedding
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Computational Models of Argument
In its classical form, the study of argumentation focuses on human-oriented uses of argument, such as whether an argument is legitimate or flawed, engagement in debate, or the rhetorical aspects of argumentation. In recent decades, however, the study of logic and computational models of argumentation has emerged as a growing sub-area of AI. This book presents the Seventh International Conference on Computational Models of Argument (COMMA’18), held in Warsaw, Poland, from 12 to 14 September 2018. Since its inception in 2006, the conference and its related activities have developed alongside the steady growth of interest in computational argumentation worldwide, and the selection of 25 full ...
Logic Programming and Nonmonotonic Reasoning
This volume contains the proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2009), held during September 14–18, 2009 in Potsdam, Germany. LPNMR is a forum for exchanging ideas on declarative logic programming, nonmonotonic reasoning and knowledge representation. The aim of the c- ference is to facilitate interaction between researchers interested in the design and implementation of logic-based programming languages and database s- tems, and researchers who work in the areas of knowledge representation and nonmonotonic reasoning. LPNMR strives to encompass theoretical and expe- mental studies that have led or will lead to the construction...
Learning and Decision-Making from Rank Data
The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state-of-the-art algorithms, such as algorithms based on M...
Reasoning with Probabilistic and Deterministic Graphical Models
Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielde...
