Autobiographic agents in dynamic virtual environments - performance comparison for different memory control architectures

Abstract

In this paper, we extend our previous work in investigating the performance of different autobiographic memory control architectures which are developed based on a basic subsumption control architecture for Artificial Life autonomous agents surviving in a dynamic virtual environment. In our previous work we showed how autonomous agents’ survival in a static virtual environment can benefit from autobiographic memory, with a kind of communication of experiences in multi-agent experiments. In the current work we extend the existing memory architecture by enhancing its functionalities and introducing Long-term Autobiographic Memory, which is derived from the inspiration of human memory schema - categorical rules or scripts that psychologists in human memory research believe all humans possess to interpret the word. A large-scale and dynamic virtual environment is created to compare the performance of various types of agents with various memory control architectures, and each agent’s behaviour is observed and analyzed together with lifespan measurements. Results confirm our previous research hypothesis that autobiographic memory can prove beneficial – indicating increases in the lifespan of an autonomous, autobiographic, minimal agent. Furthermore, the utility of combining Long-term Memory with Short-term Memory is established. We finally discuss the environmental factors influencing the performance of each architecture and the areas for future work.