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

自从A·图灵在50年代初提出了生命系统中模式形成的模型以来，人工生命(Alife)取得了巨大的进步。是图灵首先证明了一个简单的反应-扩散耦合方程系统如何通过化学不稳定过程产生化学浓度的空间模式。J·冯·纽曼后来证明了建立自我复制的抽象机器是可能的，而A·林登迈尔则引入了L系统来模拟人造植物。在过去的20年里，Alife的大部分研究都是通过一种更特别的自下而上的工程方法来完成的，通过设计或演变规则来管理系统中实体的局部相互作用，以产生某些紧急行为。在这种情况下的出现被解释为系统内的一个过程，仅仅通过检查规则是不能预测的，而只有通过运行模拟才能观察到。Alife中最早的标志性建筑有T.Rays的Tiera、J.Holland的Echo和L.Yaeger的Polyword。这些早期的系统都基于基于个人的建模框架，该框架高度抽象，并且在代理执行其交互作用的环境的模拟细节(即物理和化学规律)方面相当有限。K·西姆斯的虚拟生物和研究，如框架杆或游泳者，将更精确的(尽管仍然是随意的)物理现实融入到Alife系统中。反过来，环境相互作用细节的增加使人们能够观察到更丰富的紧急过程。最近的工作通过增加发育过程、差异基因表达和遗传调控网络纳入了更详细的生物学，使Alife模拟更具真实性。因此，随着计算资源变得更容易获得，以及我们生物学知识的加深，越来越多的生物、化学和物理细节以自下而上的方式被包括在Alife模拟中。分析生物技术、计算生物学、生物信息学和微生物学的最新进展正在改变我们对生物系统复杂性的看法，特别是它们为了在动态的、有时是有害的环境中生存、适应和进化而执行的计算(即如何处理、传输和存储信息)。我们建议捕捉一些最近的生物学见解，特别是那些与细胞生物学相关的见解，以开发复杂的类细胞系统的生命模拟。此外，虽然我们建议坚持自下而上构建Alife系统的传统工程方法，但我们希望将当前的研究实践扩展到更正式和更严格的计算方法，以设计和实施Alife研究。在这项建议中，我们寻求从根本上重新思考自下而上的人工生命研究的进行方式。到目前为止，这项研究的大部分都有很强的即席成分，几乎没有形式化。我们提出了一种新的(半)形式的细胞人工生命方法学，我们称之为信息生物技术。InfoBiotics提出，正式的信息学方法、进化和学习以及生物和生化见解之间的协同是Alife研究更有原则的实践的先决条件。这一提议背后的驱动研究问题是：i.需要将正式的信息学、进化和学习范式以及生化见解结合在一起，才能成功地将信息生物学发展为人工细胞生命研究的原则性方法？二、从信息生物学的角度提出并能够回答与科学相关和有意义的生命问题所需要的前者的平衡是什么？

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?