David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
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. It ends with a plea for the cooperation of the AI and Machine Learning communities as both learning and inference is needed if context is to make complete sense.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
Bruce Edmonds (2013). Complexity and Context-Dependency. Foundations of Science 18 (4):745-755.
Similar books and articles
Kent Bach (2012). Context Dependence. In Manuel García-Carpintero & Max Kölbel (eds.), The Continuum Companion to the Philosophy of Language. Continuum International Pub.
D. C. Gooding & T. R. Addis (2008). Modelling Experiments as Mediating Models. Foundations of Science 13 (1):17-35.
Marc W. Howard, Karthik H. Shankar & Udaya K. K. Jagadisan (2011). Constructing Semantic Representations From a Gradually Changing Representation of Temporal Context. Topics in Cognitive Science 3 (1):48-73.
Sohee Park, Junghee Lee, Bradley Folley & Jejoong Kim (2003). Schizophrenia: Putting Context in Context. Behavioral and Brain Sciences 26 (1):98-99.
Alexander Riegler (1992). Constructivist Artificial Life, and Beyond. In Barry McMullin (ed.), Proceedings of the Workshop on Autopoiesis and Perception. Dublin City University: Dublin, Pp. 121–136.
Scott Moss & Bruce Edmonds (1994). Modelling Learning as Modelling. Philosophical Explorations.
Kirsten Malmkjær & John Williams (eds.) (1998). Context in Language Learning and Language Understanding. Cambridge University Press.
Added to index2009-01-28
Total downloads17 ( #214,019 of 1,902,047 )
Recent downloads (6 months)5 ( #167,851 of 1,902,047 )
How can I increase my downloads?