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1	Default reasoning and neural networks Govender, I. (Irene) 06 1900 (has links) In this dissertation a formalisation of nonmonotonic reasoning, namely Default logic, is discussed. A proof theory for default logic and a variant of Default logic - Prioritised Default logic - is presented. We also pursue an investigation into the relationship between default reasoning and making inferences in a neural network. The inference problem shifts from the logical problem in Default logic to the optimisation problem in neural networks, in which maximum consistency is aimed at The inference is realised as an adaptation process that identifies and resolves conflicts between existing knowledge about the relevant world and external information. Knowledge and data are transformed into constraint equations and the nodes in the network represent propositions and constraint equations. The violation of constraints is formulated in terms of an energy function. The Hopfield network is shown to be suitable for modelling optimisation problems and default reasoning. / Computer Science / M.Sc. (Computer Science) Default logic Normal default theories Resolution theorem proving Neural networks Penalty logic Hopfield network training algorithm Nonmonotonic inferences Expectations Connectionist Inference mechanism Conflict resolution strategies 006.32 Non-monotonic logic Default logic Neural networks (Computer science) Nonmonotonic reasoning
2	Default reasoning and neural networks Govender, I. (Irene) 06 1900 (has links) In this dissertation a formalisation of nonmonotonic reasoning, namely Default logic, is discussed. A proof theory for default logic and a variant of Default logic - Prioritised Default logic - is presented. We also pursue an investigation into the relationship between default reasoning and making inferences in a neural network. The inference problem shifts from the logical problem in Default logic to the optimisation problem in neural networks, in which maximum consistency is aimed at The inference is realised as an adaptation process that identifies and resolves conflicts between existing knowledge about the relevant world and external information. Knowledge and data are transformed into constraint equations and the nodes in the network represent propositions and constraint equations. The violation of constraints is formulated in terms of an energy function. The Hopfield network is shown to be suitable for modelling optimisation problems and default reasoning. / Computer Science / M.Sc. (Computer Science) Default logic Normal default theories Resolution theorem proving Neural networks Penalty logic Hopfield network training algorithm Nonmonotonic inferences Expectations Connectionist Inference mechanism Conflict resolution strategies 006.32 Non-monotonic logic Default logic Neural networks (Computer science) Nonmonotonic reasoning

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Default reasoning and neural networks

Default reasoning and neural networks