An Empirical Framework for Objective Testing for P-Consciousness in an Artificial Agent

Document Type: 
Article
Article Type: 
Experimental
Disciplines: 
Artificial intelligence
Topics: 
Cognition
Keywords: 
Artificial Intelligence, Artificial General Intelligence, P-consciousness
Deposited by: 
Mr Colin Hales on 22 February 2009
Date of Issue: 
2009
Authors: 
Colin Hales
Journal/Publication Title: 
The Open Artificial Intelligence Journal
Volume: 
3
Page Range: 
1-15
Official URL: 
http://www.bentham.org/open/toaij/openaccess2.htm
Abstract: 
Two related and relatively obscure issues in science have eluded empirical tractability. Both can be directly traced to progress in artificial intelligence. The first is scientific proof of consciousness or otherwise in anything. The second is the role of consciousness in intelligent behaviour. This document approaches both issues by exploring the idea of using scientific behaviour self-referentially as a benchmark in an objective test for P-consciousness, which is the relevant critical aspect of consciousness. Scientific behaviour is unique in being both highly formalised and provably critically dependent on the P-consciousness of the primary senses. In the context of the primary senses P-consciousness is literally a formal identity with scientific observation. As such it is intrinsically afforded a status of critical dependency demonstrably no different to any other critical dependency in science, making scientific behaviour ideally suited to a self-referential scientific circumstance. The ‘provability’ derives from the delivery by science of objectively verifiable ‘laws of nature’. By exploiting the critical dependency, an empirical framework is constructed as a refined and specialised version of existing propositions for a ‘test for consciousness’. The specific role of P-consciousness is clarified: it is a human intracranial central nervous system construct that symbolically grounds the scientist in the distal external world, resulting in our ability to recognise, characterise and adapt to distal natural world novelty. It is hoped that in opening a discussion of a novel approach, the artificial intelligence community may eventually find a viable contender for its long overdue scientific basis.
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