Learning and Reasoning with Complex Representations

Inductive constraint logic programming: An overview.- Some approaches to reasoning with incomplete and changing information.- Curried least general generalization: A framework for higher order concept learning.- Approximate validity.- Inductive theories from equational systems.- The role of default representations in incremental learning.- Learning stable concepts in a changing world.- Inducing complex spatial descriptions in two dimensional scenes.- A framework for learning constraints: Preliminary report.- Induction of constraint logic programs.- Belief network algorithms: A study of performance based on domain characterisation.- A Group Decision and Negotiation Support System for argumentation based reasoning.- From belief revision to design revision: Applying theory change to changing requirements.- Using histories to model observations in theories of action.- Modelling inertia in action languages.- Combinatorial interpretation of uncertainty and conditioning.- Probabilistic diagnosis as an update problem.- Cooperative combination of default logic and autoepistemic logic.