Use this URL to cite or link to this record in EThOS:
Title: Aitana : a developmental cognitive artifact to explore the evolution of conceptual representations of cellular automata-based complex systems
Author: Marques Pita, Manuel Arturo
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2006
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
This thesis explores cognitive mechanisms that process models of complex systems – represented in their implicit form – in order to produce explicit redescriptions, which reveal knowledge about these models that is not accessible on the implicit level. The aim of this exploration is to support new ways of conceptualising the phenomenon of emergence, the main characterising feature of complex systems in general. The main problem tackled in this thesis concerns the development of representations that support new forms of model conceptualisation. Specifically, the thesis focuses on producing primordial explicit representations (where primordial means that they are not the composition of lower level explicit forms) directly from the implicit levels of knowledge, with the latter being represented in terms of Cellular Automata (CA) rules. Three hypotheses concerning the capabilities of CA redescription mechanisms are tested. The hypotheses state that such mechanisms are able (1) to capture the whole (or most significant) extent of the input implicit representation, (2) to produce redescriptions that are more compact (in terms of dimensionality) than the input representation and (3) to allow (or to provide substrate to allow) the derivation of “knowledge” about what has been learnt – the knowledge that is implicit in the low level representational forms acquired through learning. The hypotheses are tested by means of Aitana – a prototype developed specifically for this purpose. This developmental cognitive artifact is capable of learning implicit models of Cellular Automata that achieve certain pre-specified emergent behaviours, using Genetic Algorithms as its learning mechanism. Once acquired, implicit representations are processed, in order to produce explicit representations. The mechanisms for redescribing CA rules are based on the study of their endogenous spatial properties. In particular, two redescription transducers or modules are implemented in Aitana and explored through a set of case-studies. Aitana’s artificial cognitive development confirms the three hypotheses of this thesis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available