Borg, James Martin (2018) The emergence and utility of social behaviour and social learning in artificial evolutionary systems. Doctoral thesis, Keele University.

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The questions to be addressed here are all aimed at beginning to assess the emergence and utility of social behaviour and social learning in artificial evolutionary systems. Like any biological adaptation, the adaptation to process and use social information must lead to an overall increase in the long term reproductive capability of any population utilising such an adaptation - this increase in fecundity also being accompanied by increased survivability and therefore adaptability. In nature, social behaviours such as co-operation, teaching and agent aggregation, all seem to provide improved levels of fitness, resulting in an improved and more robust set of general behaviours - in the human case these social behaviours have led to cumulative culture and the ability to rapidly adapt to, and thrive in, an astonishing number of environments. In this thesis we begin to look at why the evolutionary adaptation to process and use social information, leading to social learning and social behaviour, proves to be such a useful adaptation, and under which circumstances we would expect to see this adaptation, and its resulting mechanisms and strategies, emerge.

We begin by asking these questions in two contexts; firstly what does social learning enable that incremental genetic evolution alone does not, and secondly what benefit does social learning provide in temporally variable environments. We go on to assess how differing social learning strategies affect the utility of social learning, and whether social information can be utilised by an evolutionary process without any accompanying within-lifetime learning processes (and whether the accommodation of social information results in any notable behavioural changes). By addressing the questions posed here in this way, we can begin to shed some light on the circumstances under which the adaptations for the accommodation and use of social information begin to emerge, and ultimately lead to the emergence of robust socially intelligent artificial agents.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Contributors: Channon, AD (Thesis advisor)
Depositing User: Lisa Bailey
Date Deposited: 07 Mar 2018 09:37
Last Modified: 19 Nov 2020 09:43

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