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: transferrence, learning, inference, context, heuristic, pragmatism, modelling, methodology

Contents

1. - Introduction

2. - Causal Structure

3. - Contexts emerge from Modelling Heuristics

4. - Internal and External Conceptions of `context'

4.1. - The internal approach
4.2. - The external approach

5. - Context in Different Domains

5.1. - Shared physical environment
5.2. - Shared social environment
5.3. - Shared biology

6. - Bottom-up and Top-down Approaches to Modelling Context

6.1. - Top-down
6.2. - Bottom-up

7. - A Bottom-up Investigation of Internal Context in an Artificial Agent

7.1. - Overview
7.2. - The Learning Algorithm
7.3. - Analysis of the Network in Terms of Learned Knowledge
7.4. - The Environment
7.5. - Implementation
7.6. - Preliminary Results

8. - Conclusion

Acknowledgements

References

The Pragmatic Roots of Context - Bruce Edmonds - 31 MAR 99

[Next] [Previous] [Top]

Generated with CERN WebMaker