creators_name: Edmonds, Bruce editors_name: Bouquet, Paolo editors_name: Serafini, Luciano editors_name: Brezillon, Patrick editors_name: Benerecetti, Massimo editors_name: Castellani, Francesca type: confpaper datestamp: 2001-08-30 lastmod: 2011-03-11 08:54:47 metadata_visibility: show title: The Pragmatic Roots of Context ispublished: pub subjects: cog-psy subjects: comp-sci-art-intel subjects: comp-sci-mach-learn subjects: ling-prag subjects: phil-sci full_text_status: public keywords: context, knowledge transference, learning, inferrence, model, modelling, modeling, conditions of application, recognition, simulation, agent, Bak, network, GP, genetic programming, artificial stock market abstract: When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference ­ a simple model of the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there are (at least) two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actually applies (the `internal' approach); or (if this is feasible) one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down (or `foundationalist') approach where one tries to lay down general, a priori principles and a bottom-up (or `scientific') approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment. 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