On the emergence of structure in behaviours evolved through embodied imitation in a group of robots

摘要

This paper describes research in which we model social interactions between artificial agents using real robots. We show that variations that arise from embodiment allow certain behaviours, those that are more robust to the processes of embodied imitation, to emerge and evolve during multiple cycles of imitation. We test 3 memory strategies: no memory, limited memory and unlimited memory, and experimental results appear to show that with limited memory, those behaviours are more likely to become dominant within the robots’ collective memory. Introduction Social learning, which enables individuals to learn from each other, is a powerful mechanism in social animals, including humans. An important form of social learning is imitation, in which an individual observes and replicates another’s actions. Imitation has been widely studied both by biologists and psychologists; biological research on imitation mostly focusses on its adaptive value for the organism, whereas psychologists are largely interested in the function of imitation and the mechanisms in which it plays a part (Zentall, 2001). There is continuing debate on the definition of imitation and whether it is unique to humans but what is not in doubt is that imitation clearly serves an important role in the development of social cognition in humans. For example, Dautenhahn et al reported that human babies are born with the ability to imitate a wide range of behaviours, including mouth opening and tongue protrusion (Dautenhahn et al., 2003). Meltzoff and Moore (Meltzoff and Moore, 1992) stated that human infants use imitation to enrich their understanding of people and their activities. Through imitation, humans are able to become part of a very complex social environment: human society. Imitation has also been seen as an important facet of cultural transmission; Dawkins argued (Dawkins, 1976) that imitation is a prerequisite for the evolution of culture, as it allows transmission of behaviours, with variation, between individuals. The study of imitation in robotics has received crossdisciplinary attention in recent years. In the context of robotics research, Bakker and Kuniyoshi (Bakker and Kuniyoshi, 1996) defined imitation thus: “Imitation takes place when an agent learns a behaviour from observing the execution of that behaviour by a teacher”. This definition hints at how imitation is implemented and is used in most robotics research. Skill acquisition by human or robot demonstration has been widely investigated ((Scassellati, 1999); (Mataric, 2000)). This approach holds the promise that we may be able to overcome the necessity to program every behaviour a robot may need to perform, as the robot can learn new behaviours through observing demonstrations of those behaviours. However, as stated above, as well as supporting skill transmission between individuals, in human society, imitation has a social dimension, allowing individuals to become part of a social community. Alissandrakis et al. (Alissandrakis et al., 2004) stated that imitation may serve as a stepping stone towards the development of social cognition in artificial agents as it can form social integration with other artificial agents or with humans. Imitation research in robotics might also usefully address the question of how culture emerges and evolves as a novel property in groups of social animals. In (Winfield and Erbas, 2011) we introduce embodied imitation as a method for modelling the emergence of behavioural ‘traditions’ in social agents. There has been some work examining the social dimension of imitation in robotics. Steels and Kaplan (Steels and Kaplan, 2001) stated that social learning can play a crucial role in initiating a humanoid robot into a linguistic culture. He used methods such as initiating open-ended dialogues among humans and robotic agents, in which social learning could be embedded. Billard (Billard, 1999) claimed that imitation can be used to enhance autonomous robots’ learning of com