Representing Uncertain Knowledge

1 The Nature of Uncertainty.- 1.1 Introduction.- 1.2 Representation and management of uncertainty.- 1.3 The structure of this book.- 2 Bayesian Probability.- 2.1 Introduction.- 2.2 Foundations.- 2.3 Resolution by independence.- 2.4 Belief propagation through local computation.- 2.5 MUNIN - An application of probabilistic reasoning in electromyography.- 2.6 Learning from the children of Great Ormond Street.- 2.7 Discussion.- 2.8 Conclusions.- 3 The Certainty Factor Model.- 3.1 Introduction.- 3.2 Operation.- 3.3 Simple worked example.- 3.4 Discussion.- 3.5 Conclusions.- 4 Epistemic Probability: the Dempster-Shafer theory of evidence.- 4.1 Introduction.- 4.2 A short history of epistemic probability.- 4.3 The Dempster-Shafer theory of evidence.- 4.4 How to act on a belief.- 4.5 Evidential reasoning applied to robot navigation.- 4.6 Discussion.- 4.7 Conclusions.- 5 Reasoning with Imprecise and Vague Data.- 5.1 Introduction.- 5.2 Crisp sets and imprecision.- 5.3 Vague and approximate concepts.- 5.4 Possibilistic logic.- 5.5 Discussion.- 5.6 Conclusions.- 6 Non-monotonic Logic.- 6.1 Introduction.- 6.2 A brief overview of formal logic.- 6.3 Non-monotonic logics.- 6.4 Discussion.- 6.5 Conclusion.- 7 Argumentation.- 7.1 Introduction.- 7.2 Heuristic models of argumentation.- 7.3 Logical models of argumentation.- 7.4 Discussion.- 7.5 Conclusions.- 8 Overview.- 8.1 Introduction.- 8.2 Resumé.- 8.3 Verbal uncertainty expressions.- 8.4 Uncertainty and decision making.- 8.5 Meta-level reasoning and control.- 8.6 Future trends: the convergence of symbolic and quantitative methods?.- References.- Author Index.