Open-Ended Evolutionary Robotics: an Information Theoretic Approach

This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach

An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. However, the role played by the environment in influencing the effectiveness of each type of learning is not well understood. In this paper, we address this question by analysing the performance of a swarm in a range of simulated, dynamic environments where a distributed evolutionary algorithm for evolving a controller is augmented with a number of different individual learning mechanisms. The learning mechanisms themselves are defined by parameters which can be either fixed or inherited. We conduct experiments in a range of dynamic environments whose characteristics are varied so as to present different opportunities for learning. Results enable us to map environmental characteristics to the most effective learning algorithm.Comment: In GECCO 201

An Approach To Artificial Society Generation For Video Games

Since their inception in the 1940s, video games have always had a need for non-player characters (NPCs) driven by some form of artificial intelligence (AI). More recently, researchers and developers have attempted to create believable, or human-like, agents by modeling them after humans by borrowing concepts from the social sciences. This thesis explores an approach to generating a society of such believable agents with human-like attributes and social connections. This approach allows agents to form various kinds of relationships with other agents in the society, and even provides an introductory form of shared or influenced attributes based on their spouse or parents. Our proposed method is a simplified system for generating a society, but shows great potential for future work. As a modularized and parameterized framework, there are many opportunities for adding new layers to the system to improve the realism of the generated society

A Closer Look at Adaptation Mechanisms in Simulated Environment-Driven Evolutionary Swarm Robotics

This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called mEDEA.Firstly, mEDEA is extended with an explicit relative fitness measure while still maintaining the distributed nature of the algorithm. Two ways of using the relative fitness are investigated: influencing the spreading of genomes and performing an explicit genome selection. Both methods lead to an improvement in the swarm’s ability to maintain energy over longer periods.Secondly, a communication energy model is derived and introduced into the simulator to investigate the influence of accounting for the costs of communication in the distributed evolutionary algorithm where communication is a key component.Thirdly, a method is introduced that relates environmental conditions to a measure of the swarm’s behaviour in a 3-dimensional map to study the environment’s influence on the emergence of behaviours at the individual and swarm level. Interesting regions for further experimentation are identified in which algorithm specific characteristics show effect and can be explored.Finally, a novel individual learning method is developed and used to investigate how the most effective balance between evolutionary and lifetime-adaptation mechanisms is influenced by aspects of the environment a swarm operates in. The results show a clear link between the effectiveness of different adaptation mechanisms and environmental conditions, specifically the rate of change and the availability of learning opportunities