B. Edmonds. 1688, page 119--132. Springer Berlin / Heidelberg, (1999)
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 two ways of modelling context: one can either attempt
to investigate the contexts that arise out of the heuristics that
a particular agent actua lly applies or 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 approach where one tries to lay down general,
a priori principles and a bottom-up 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.
Proceedings of the Modeling and Using Context: Second International
and Interdisciplinary Conference, CONTEXT'99, Trento, Italy, September
1999. Lecture Notes in Computer Science
%0 Book Section
%1 edmonds1999
%A Edmonds, Bruce
%B Proceedings of the Modeling and Using Context: Second International
and Interdisciplinary Conference, CONTEXT'99, Trento, Italy, September
1999. Lecture Notes in Computer Science
%D 1999
%E Bouquet, Paolo
%E Serafini, Luciano
%E Brézillon, Patrick
%E Benerecetti, Massimo
%E Castellani, Francesca
%I Springer Berlin / Heidelberg
%K Computer Science
%P 119--132
%T The Pragmatic Roots of Context
%V 1688
%X 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 two ways of modelling context: one can either attempt
to investigate the contexts that arise out of the heuristics that
a particular agent actua lly applies or 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 approach where one tries to lay down general,
a priori principles and a bottom-up 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.
@inbook{edmonds1999,
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 two ways of modelling context: one can either attempt
to investigate the contexts that arise out of the heuristics that
a particular agent actua lly applies or 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 approach where one tries to lay down general,
a priori principles and a bottom-up 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.},
added-at = {2007-05-04T05:48:10.000+0200},
author = {Edmonds, Bruce},
biburl = {https://www.bibsonomy.org/bibtex/2e49a361d6ffccae54c6409d638ece79c/p_ansell},
booktitle = {Proceedings of the Modeling and Using Context: Second International
and Interdisciplinary Conference, {CONTEXT}'99, Trento, Italy, September
1999. Lecture Notes in Computer Science},
description = {Context-aware business processes},
editor = {Bouquet, Paolo and Serafini, Luciano and Br\'ezillon, Patrick and Benerecetti, Massimo and Castellani, Francesca},
interhash = {4f08355f8465c60dfc87fee649f18cc4},
intrahash = {e49a361d6ffccae54c6409d638ece79c},
keywords = {Computer Science},
owner = {peter},
pages = {119--132},
pdf = {HonoursResearch/Edmonds1999-ThePragmaticRootsOfContext.pdf},
publisher = {Springer Berlin / Heidelberg},
timestamp = {2007-05-04T05:48:11.000+0200},
title = {The Pragmatic Roots of Context},
volume = 1688,
year = 1999
}