The emergence and utility of social behaviour and social learning in artificial evolutionary systems

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

Social Learning and Cultural Evolution in Artificial Life

We describe the questions and discussions raised at the First Workshop on Social Learning and Cultural Evolution held at the Artificial Life Conference 2016 in Cancun, Mexico in July 2016. The purpose of the workshop was to assemble artificial life researchers interested in social learning and cultural evolution into one group so that we could focus on recent work and interesting open questions. Our discussion related to both the mechanisms of social learning and cultural evolution and the consequences and influence of social learning and cultural evolution on living systems. We present the contributions of our workshop presenters and conclude with a discussion of the more important open questions in this area

Explaining Evolutionary Agent-Based Models via Principled Simplification

Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid

The Effect of Social Information Use without Learning on the Evolution of Behaviour

In a recent paper by Borg & Channon [6] it was shown that social information alone, decoupled from any within-lifetime learning, can result in improved performance on a food foraging task compared to when so13 cial information is unavailable. Here we assess whether access to social information leads to significant behavioral differences both when access to social information leads to improved performance on the task, and when it does not; do any behaviors resulting from social information use, such as movement and increased agent interaction, persist even when the ability to discriminate between poisonous and non-poisonous food is no better than when social information is unavailable? Using a neuroevolutionary artifi20 cial life simulation, here we show that social information use can lead to the emergence of behaviors that differ from when social information is un22 available, and that these behaviors act as a promoter of agent interaction. The results presented here suggest that the introduction of social infor24 mation is sufficient, even when decoupled from within-lifetime learning, for the emergence of pro-social behaviors. We believe this work to be the first use of an artificial evolutionary system to explore the behavioural consequences of social information use in the absence of within-lifetime learning

Evolving developmental, recurrent and convolutional neural networks for deliberate motion planning in sparse reward tasks

Motion planning algorithms have seen a diverse set of approaches in a variety of disciplines. In the domain of artificial evolutionary systems, motion planning has been included in models to achieve sophisticated deliberate behaviours. These algorithms rely on fixed rules or little evolutionary influence which compels behaviours to conform within those specific policies, rather than allowing the model to establish its own specialised behaviour. In order to further these models, the constraints imposed by planning algorithms must be removed to grant greater evolutionary control over behaviours. That is the focus of this thesis. An examination of prevailing neuroevolution methods led to the use of two distinct approaches, NEAT and HyperNEAT. Both were used to gain an understanding of the components necessary to create neuroevolution planning. The findings accumulated in the formation of a novel convolutional neural network architecture with a recurrent convolution process. The architecture’s goal was to iteratively disperse local activations to greater regions of the feature space. Experimentation showed significantly improved robustness over contemporary neuroevolution techniques as well as an efficiency increase over a static rule set. Greater evolutionary responsibility is given to the model with multiple network combinations; all of which continually demonstrated the necessary behaviours. In comparison, these behaviours were shown to be difficult to achieve in a state-of-the-art deep convolutional network. Finally, the unique use of recurrent convolution is relocated to a larger convolutional architecture on an established benchmarking platform. Performance improvements are seen on a number of domains which illustrates that this recurrent mechanism can be exploited in alternative areas outside of planning. By presenting a viable neuroevolution method for motion planning a potential emerges for further systems to adopt and examine the capability of this work in prospective domains, as well as further avenues of experimentation in convolutional architectures

The effect of social information use without learning on the evolution of social behavior

In a recent paper by Borg (2017) it was shown that social information alone, decoupled from any within-lifetime learning, can result in improved performance on a food foraging task compared to when social information is unavailable. Here we assess whether access to social information leads to significant behavioral differences both when access to social information leads to improved performance on the task, and when it does not; do any behaviors resulting from social information use, such as movement and increased agent interaction, persist even when the ability to discriminate between poisonous and non-poisonous food is no better than when social information is unavailable? Using a neuroevolutionary artificial life simulation, here we show that social information use can lead to the emergence of behaviors that differ from when social information is unavailable, and that these behaviors act as a promoter of agent interaction. The results presented here suggest that the introduction of social information is sufficient, even when decoupled from within-lifetime learning, for the emergence of pro-social behaviors. We believe this work to be the first use of an artificial evolutionary system to explore the behavioural consequences of social information use in the absence of within-lifetime learning