Symbolic and Quantitative Approaches to Reasoning and Uncertainty [electronic resource] / edited by Anthony Hunter, Simon Parsons.

On the Dynamics of Default Reasoning -- Nonmonotonic and Paraconsistent Reasoning: From Basic Entailments to Plausible Relations -- A Comparison of Systematic and Local Search Algorithms for Regular CNF Formulas -- Query-answering in Prioritized Default Logic -- Updating Directed Belief Networks -- Inferring Causal Explanations -- A Critique of Inductive Causation -- Connecting Lexicographic with Maximum Entropy Entailment -- Avoiding Non-Ground Variables -- Anchoring Symbols to Vision Data by Fuzzy Logic -- Filtering vs Revision and Update: let us Debate! -- Irrelevance and Independence Axioms in Quasi-Bayesian Theory -- Assessing the value of a candidate -- Learning Default Theories -- Knowledge Representation for Inductive Learning -- Handling Inconsistency Efficiently in the Incremental Construction of Stratified Belief Bases -- Rough Knowledge Discovery and Applications -- Gradient Descent Training of Bayesian Networks -- Open Default Theories over Closed Domains -- Shopbot Economics -- Optimized Algorithm for Learning Bayesian Network from Data -- Merging with Integrity Constraints -- Boolean-like Interpretation of Sugeno Integral -- An Alternative to Outward Propagation for Dempster-Shafer Belief Functions -- On bottom-up pre-processing techniques for automated default reasoning -- Probabilisitc Logic Programming under Maximum Entropy -- Lazy Propagation and Independence of Causal Influence -- A Monte Carlo Algorithm for Combining Dempster-Shafer Belief Based on Approximate Pre-Computation -- An Extension of a lInguistic Negation Model allowing us to Deny Nuanced Property Combinations -- Argumentation and Qualitative Decision Making -- Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach -- State Recognition in Discrete Dynamical Systems using Petri Nets and Evidence Theory -- Robot Navigation and Map Building with the Event Calculus -- Information Fusion in the Context of Stock Index Prediction -- Defeasible Goals -- Logical Deduction using the Local Computation Framework.