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

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