| Summary: | Artificial adaptive systems inspired or derived from neuro-biological components and processes have shown great promise at several levels. One behaviour required for the continuous functional operation of advanced neuro-biological systems is sleep. A definitive function or purpose for sleep and of the associated phenomenology such as dreaming, remains elusive. Correspondingly there remain many unresolved issues within the domain of artificial learning systems. One such aspect that largely remains intractable is the management of experiences once learned and encoded. This is the general problem of developing a persuasive explanation or scalable strategy for the contiguous organisation of internal representation and memory within finite resources; it is from this parallel perspective in which this research is set. This research is an exploration into the cognition of sleep and dreaming in humans and animals. Positioned between sleep & dreaming research and the machine learning domain, this thesis reports on an approach to improve the latter by formulating theories emerging from the former. Recent research investigating the responsibility of sleep processes in modifying memory have shown that for the avian and mammalian brain sleep plays an important role in long term cognitive development. A set of observations are created from the current understanding of both the benefits of sleep and the processes involved, including dreaming. From these observations the first contribution of this thesis is presented; several proposals for the cognitive benefits of sleep and dreaming in aspects of perception, consolidation, scalability, generalisation and representational conceptualisation. Previous research has investigated some aspects of sleep and dreaming in relation to machine learning. These have been positioned at two extremes of the machine learning paradigm; low level, emergent behaviour of artificial neural networks or high level, directed behaviour of symbolic artificial intelligence. This is the first report of direct research into the translation of the benefits by analogous mechanisms of sleep and dreaming at a level in-between earlier research. This combination is characterised by creating a foundation for a new genre of artificial learning strategies derived directly from sleep and dream phenomenology, Oneiric Machine Learning.! Anticipatory classifier systems (ACS) represent a niche group of machine learning systems derived from the established machine learning field of learning classifier systems (LCS). ACS are capable of latent learning; learning for the reward of learning and subsequently creating an internal generalised model of the environment. This feature aligned within the LCS framework provides an ideal developmental template. A review of the latent learning background and ACS algorithmi~. detail sets the basis for several applications illustrative of the Oneiric Machine Learning approach. Empirical evidence demonstrates how an adapted ACS system can exploit a dreamlike emergent thread based on an incomplete, generalised model of the environment to reduce the number of real actions required to reach model competency. Conceptual solutions to restrictions limiting the role to which ACS/LCS systems can represent some aspects advocated by Oneiric Machine 'Learning are presented. In mitigation of these restrictions, two novel prototype systems are described; the first introduces a method of implicitly managing state generalisation by the building of concept links into the classifier rule. The second illustrates automatic state alias triggered state augmentation and off-line resolution. Although remaining under development 1 Oneiric: of or relating to dreams or dreaming. Adapted from Oneiric Behaviour (Jouvet, 1979) used to describe rapid eye movement (REM) sleep re-animation. results in these new directions present plausible systems level architectures that are in part experimentally demonstrated. Novel solutions are presented to structural and procedural problems that promote the future development of cognitive systems within the LeS framework setting a direction for future studies.
|
|---|
