Information-computation-evolution agent model for natural computation

Biological systems provide an exceptional source of inspiration for the design of autonomous adaptive agents. In this work, we propose ICE (or Information-Computation-Evolution), a new bio-inspired agent model for complex, multiagent self-organized systems, such as biological or social systems. The model proposes a physical organization of reality using three main components - information organization, graph-based computation and evolution. It emphasizes a strong relationship between computation and the physical world out of which we derive information principles and a biological description of the world. Agents using ICE operate in an hierarchical structure of `realities´ where they have to conform to the information principles derived from the model and continuously explore more complex knowledge and create new connections to learn more from the world.