Designing Synthetic Memory Systems for Supporting Autonomous Embodied Agent Behaviour

We present a method for the creation of autonomous agent memory systems that support real-time social behaviours relating to perception and attention. Our memory design methodology is modular in nature: modules are connected by transformation operators representative of different forms of information processing and which fall into two general categories: information filters and integrators. Filters are selective and only retain relevant information, while integrators process information into more compact and high-level representations. We demonstrate the flexibility and feasibility of our methodology by elaborating two examples which have been implemented and shown successful for supporting fundamental agent social behaviours in a virtual environment