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• Relatedness of Fuzzy Sets, Fuzzification of Allen's Temporal Interval Relation
• Reasoning About Qualitative Relations Between Fuzzy Intervals
• Temporal Information Retrieval with Vague Events
• Representation and Composition of Fuzzy Spatial Relations
• Fuzzification of the Region Connection Calculus
• Reasoning in the Fuzzy Region Connection Calculus
• Geographic Information Retrieval with Vague Regions.
• (source: Nielsen Book Data)

This book comprehensively studies fuzzy temporal and spatial information, starting from the basics on fuzzy set theory and temporal/spatial reasoning, the development of a new model to represent fuzzy temporal/spatial information, the study of efficient and complete reasoning algorithms, and their application in an information retrieval context. This useful volume presents the first approach that goes beyond merely representing information, by thoroughly addressing a variety of reasoning tasks. It also focuses on applications in the domain of information retrieval, and demonstrating the practical importance of the proposed framework.
(source: Nielsen Book Data)

• Relatedness of Fuzzy Sets, Fuzzification of Allen's Temporal Interval Relation
• Reasoning About Qualitative Relations Between Fuzzy Intervals
• Temporal Information Retrieval with Vague Events
• Representation and Composition of Fuzzy Spatial Relations
• Fuzzification of the Region Connection Calculus
• Reasoning in the Fuzzy Region Connection Calculus
• Geographic Information Retrieval with Vague Regions.
• (source: Nielsen Book Data)

This book comprehensively studies fuzzy temporal and spatial information, starting from the basics on fuzzy set theory and temporal/spatial reasoning, the development of a new model to represent fuzzy temporal/spatial information, the study of efficient and complete reasoning algorithms, and their application in an information retrieval context. This useful volume presents the first approach that goes beyond merely representing information, by thoroughly addressing a variety of reasoning tasks. It also focuses on applications in the domain of information retrieval, and demonstrating the practical importance of the proposed framework.
(source: Nielsen Book Data)

• Combinatorial Games: From Theoretical Solving to AI Algorithms
• A Gentle Introduction to Reinforcement Learning
• Possibilistic Graphical Models for Uncertainty Modeling
• On the Explanation of SameAs Statements using Argumentation
• Reasoning with Multiple-Agent Possibilistic Logic
• Incremental Preference Elicitation in Multi-Attribute Domains for Choice and Ranking with the Borda Count
• Graphical Models for Preference Representation: An Overview
• Diffusion of Opinion and Influence
• Fuzzy Labeling for Abstract Argumentation: An Empirical Evaluation
• A Belief-Based Approach to Measuring Message Acceptability
• Intertranslatability of Labeling-based Argumentation Semantics
• Preference Inference Based on Pareto Models
• Persuasion Dialogues via Restricted Interfaces using Probabilistic Argumentation
• Metric Logic Program Explanations for Complex Separator Functions
• A Two-Stage Online Approach for Collaborative Multi-Agent Planning under Uncertainty
• 9-ASP for Computing Repairs with Existential Ontologies
• Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy
• Fuzzy Quantified Structural Queries to Fuzzy Graph Databases
• Reasoning with Data
• A New Challenge For AI
• Probabilistic Spatial Reasoning in Constraint Logic Programming
• ChoiceGAPs: Competitive Diffusion as a Massive Multi-Player Game in Social Networks
• Challenges for Efficient Query Evaluation on Structured Probabilistic Data
• Forgetting-Based Inconsistency Measure
• A Possibilistic Multivariate Fuzzy c-Means Clustering Algorithm
• A Measure of Referential Success Based on Alpha-Cuts
• Graded Justification of Arguments via Internal and External Endogenous Features.

This book constitutes the refereed proceedings of the 10th International Conference on Scalable Uncertainty Management, SUM 2016, held in Nice, France, in September 2016. The 18 regular papers and 5 short papers were carefully reviewed and selected from 35 submissions. Papers are solicited in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information. These include (but are not restricted to) applications in decision support systems, risk analysis, machine learning, belief networks, logics of uncertainty, belief revision and update, argumentation, negotiation technologies, semantic web applications, search engines, ontology systems, information fusion, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.

• Introduction
• Preliminaries
• Fuzzy Answer Set Programming
• Aggregated Fuzzy Answer Set Programming
• Core Fuzzy Answer Set Programming
• Reducing FASP to Fuzzy SAT
• Conclusions.

Answer set programming (ASP) is a declarative language tailored towards solving combinatorial optimization problems. It has been successfully applied to e.g. planning problems, configuration and verification of software, diagnosis and database repairs. However, ASP is not directly suitable for modeling problems with continuous domains. Such problems occur naturally in diverse fields such as the design of gas and electricity networks, computer vision and investment portfolios. To overcome this problem we study FASP, a combination of ASP with fuzzy logic -- a class of manyvalued logics that can handle continuity. We specifically focus on the following issues: 1. An important question when modeling continuous optimization problems is how we should handle overconstrained problems, i.e. problems that have no solutions. In many cases we can opt to accept an imperfect solution, i.e. a solution that does not satisfy all the stated rules (constraints). However, this leads to the question: what imperfect solutions should we choose? We investigate this question and improve upon the state-of-the-art by proposing an approach based on aggregation functions. 2. Users of a programming language often want a rich language that is easy to model in. However, implementers and theoreticians prefer a small language that is easy to implement and reason about. We create a bridge between these two desires by proposing a small core language for FASP and by showing that this language is capable of expressing many of its common extensions such as constraints, monotonically decreasing functions, aggregators, S-implicators and classical negation. 3. A well-known technique for solving ASP consists of translating a program P to a propositional theory whose models exactly correspond to the answer sets of P. We show how this technique can be generalized to FASP, paving the way to implement efficient fuzzy answer set solvers that can take advantage of existing fuzzy reasoners.