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.