(Semi)Formal Artificial Life Through P-systems & Learning Classifier Systems: An Investigation into InfoBiotics

Artificial Life (ALife) has advanced enormously since A. Turing proposed in the early 50s models of pattern formation in living systems. It was Turing who first demonstrated how a simple system of coupled reaction-diffusion equations could give rise to spatial patterns in chemical concentrations through a process of chemical instability. J. von Newman, later, demonstrated that it was possible to build self-replicating abstract machines while A. Lindenmayer introduced L-systems for modelling artificial plants. The bulk of ALife research in the last 20 years has been done with a more ad-hoc bottom-up engineering approach by designing or evolving the rules that govern the local interactions of the entities in the system as to produce certain emergent behaviour. Emergence in this context is interpreted as a process within the system that could not have been predicted from merely inspecting the rules but that it is observed only by running the simulation. Some of the earliest landmarks in ALife were T. Rays' Tierra, J. Holland's Echo and L. Yaeger's Polyword. These early systems were all based on an individual based modelling framework, which were highly abstract and quite limited in the simulated details (i.e. physical and chemical laws) of the environment where the agents performed their interactions. K. Sims's virtual creatures and research like framsticks or swimmers incorporated a more accurate (albeit still arbitrary) physical reality into the ALife system. In turn, this increase in the detail of the environmental interactions allowed richer emergent processes to be observed. More recent work incorporated a more detailed biology through the addition of developmental processes, differential gene expression and genetic regulatory networks endowing ALife simulations with greater realism. Thus, as computing resources became more accessible and our biological knowledge deepened, more and more levels of biological, chemical and physical details were included in a bottom-up fashion into ALife simulations. Recent advances in analytical biotechnology, computational biology, bioinformatics and micro-biology are transforming our views of the complexity of biological systems, particularly the computations they perform (i.e. how information is processed, transmitted and stored) in order to survive, adapt and evolve in dynamic and sometimes hostile environments. We propose to capture some of these more recent biological insights, in particular those related to cell biology, as to develop sophisticated ALife simulations of cellular-like systems. Furthermore, while we propose to stick to the traditional engineering approach of building ALife systems from the bottom-up we would like to extend current research practice towards a more computationally formal and rigorous approach to the design and implementation of ALife research. In this proposal we seek a fundamental rethink on the way bottom-up Artificial Life research is conducted. Until now, much of this research has had a strong ad-hoc component with very little formalisations. We propose a new (semi) formal cellular Artificial Life methodology, which we call InfoBiotics. InfoBiotics proposes that a synergy between formal informatics methods, evolution and learning and biological and biochemical insights are a pre-requisite for a more principled practice of ALife research. The driving research issues behind this proposal are:i. What combinations of formal informatics, evolutionary and learning paradigms and biochemical insights are needed for a successful development of InfoBiotics as a principled approach to Artificial Cellular Life research? ii. What is the balance of each of the former that is needed in order to ask and, be able to, answer scientifically relevant and meaningful ALife questions from an InfoBiotics perspective?