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Posted by: Venkatesh Babu
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Artificial life

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ARTIFICIAL LIFE

Venkatesh Babu, M.Tech CSE, IIT Kanpur
vbabu [@] cse.iitk.ac.in

Introduction

Human imagination and creativity is just spellbound. At the same time, our quest for knowledge is unlimited. This is a major factor distinguishing humans from other living species. For millions of years since man came into existence, he has been thinking and unraveling secrets of nature. An interesting aspect of this thought process is its cyclic and never ending nature. The process of finding answers in-turn has led us to many more questions. One such interesting question that has been haunting us since long time is on the thought process itself. A very simple question: How do we think? How are we able to find elegant solutions to very difficult problems? In short, man has now started thinking about the thought process. This is one of the main goals of cognitive science research. But cognitive science has a broader scope than that is stated above. Cognitive scientists are interested in understanding the behavior exhibited by the living. In other words people are interested in understanding the complex system called “life” that exists on our planet. Wonder where is this artificial life coming from? Thanks to the creative human thought process, people think the best way to understand life could be to create one!!!

What is ARTIFICIAL LIFE?

So, what exactly is Alife? From the introduction it sounds like defining artificial life as human created life. But, this definition puts us in an ambiguous situation of first defining what life is. In [2], Langton says:

Without other examples, it is difficult to distinguish essential properties of life - properties that would be shared by any living system - from properties that may be incidental to life in principle, but which happen to be universal to life on Earth due solely to a combination of local historical accident and common genetic descent.

In order to derive general theories about life, we need an ensemble of instances to generalize over. Since it is quite unlikely that alien life forms will present themselves to us for study in the near future, our only option is to try to create alternative life-forms ourselves - Artificial Life - literally "life made by Man rather than by Nature".

Thus, a simple, one line definition of Artificial life can be:

The term Artificial Life is used to describe research into human-made systems that possess some of the essential properties of life [1]

There are two broad sub-fields of this research: One that deals with the creation of life using the classical building blocks of nature (carbon-based life) and Another with the creation of life using the same principles but a different medium for implementation: the computer.

The former explores the possibility of “RNA worlds” by attempting to construct self-replicating molecules; the latter, by simulating simple populations of self-replicating entities also called as agents, examines the abilities and characteristics of different chemistries in supporting life-like behavior. Both approaches seek to shed light on the compelling question of the origin of life.

Is this all new?

Don’t be surprised if I say that the concept is not all new!!! People might have heard the word "Synthesis". Alife is similar to the process of synthesis. The process of synthesis has been an extremely important tool in many disciplines. Take for example Synthetic chemistry - the ability to put together new chemical compounds not found in nature - has not only contributed enormously to our theoretical understanding of chemical phenomena, but has also allowed us to fabricate new materials and chemicals that are of great practical use for industry and technology.

Artificial life amounts to the practice of "synthetic biology" and, by analogy with synthetic chemistry, the attempt to recreate biological phenomena in alternative media will result in not only better theoretical understanding of the phenomena under study, but also in practical applications of biological principles in the technology of computer hardware and software, mobile robots, spacecraft, medicine, nanotechnology, industrial fabrication and assembly, and other vital engineering projects.

By extending the horizons of empirical research in biology beyond the territory currently circumscribed by life-as-we-know-it, the study of Artificial Life gives us access to the domain of life-as-it-could-be, and it is within this vastly larger domain that we must ground general theories of biology and in which we will discover practical and useful applications of biology in our engineering endeavors.

More about its history - Source 3

Alife is closely related to and constantly nurtured by various other branches of science such as physics, mathematics, biology, computer and information science.

Biology, i.e., the study of actual life, has provided many of the roots of artificial life. The sub-fields of biology that have contributed most are microbiology and genetics, evolution theory, ecology, and development.

Physics and mathematics have also had a strong influence on artificial life. Statistical mechanics and thermodynamics have always claimed relevance to life, since life’s formation of structure is a local reversal of the second law of thermodynamics, made possible by the energy flowing through a living system. Statistical mechanics is also used to analyze some of the models used in artificial life that are sufficiently simple and abstract, such as random Boolean networks. Physics and dynamical systems have together spawned the development of synergetics and the study of complex systems, which are closely allied with artificial life. One of artificial life’s main influences from physics and mathematics has been an emphasis on studying model systems that are simple enough to have broad generality and to facilitate quantitative analysis.

However, the most heavily represented discipline among contemporary researchers in artificial life is computer science. One set of artificial life’s roots in computer science is embedded in artificial intelligence (AI), because living systems exhibit simple but striking forms of intelligence. Like AI, artificial life aims to understand a natural phenomenon through computational models.

In fact, Alife dates back to the Von Neumann era. John Von Neumann implemented the first artificial life model (without referring to it as such) with his famous creation of a self-reproducing, computation-universal entity using cellular automata. He was actually looking at many of the very issues that drive artificial life today, such as understanding the spontaneous generation and evolution of complex adaptive structures. He approached these issues with the extremely abstract methodology that typifies contemporary artificial life. Even in the absence of modern computational tools, von Neumann made striking progress.

Cybernetics developed at about the same time as von Neumann’s work on cellular automata, and he attended some of its formative meetings. Norbert Wiener is usually considered to be the originator of the field. It brought two separate foci to the study of life processes: the use of information theory and a deep study of the self-regulatory processes (homeostases), considered essential to life. Information theory typifies the abstractness and material-independence of the approach often taken within both cybernetics and artificial life.

Besides the above, Alife has a related set of roots in machine learning, inspired by the robust and flexible processes by which living systems generate complex useful structures. In particular, some machine learning algorithms such as the genetic algorithm are now seen as examples of artificial life applications. Other related areas in computer science are evolutionary programming, autonomous agents etc…

Alife from computer scientist’s viewpoint

As said in the previous paragraphs, it all started with understanding and modeling what are called complex adaptive systems. Since animal/ human cognitive systems are again complex systems, people started thinking why not employ the same approach to understand cognition and this is how artificial life got life.

A complex dynamical system, or a complex system for short, consists of a set of interacting elements where the behavior of the total is an indirect, non-hierarchical consequence of behavior of the different parts. Most artifacts like cars and other automobiles are hierarchical systems where the total behavior is a hierarchical composition of the behavior of individual parts. Some of the characteristics of complex systems are:

1. There is no central control source

2. Typically the system is open: new elements are entering and leaving the system.

3. Almost all complex systems show three main types of behaviors: equilibrium, self-organization and chaos.

4. Very often the behavior exhibited by complex systems is said to be emergent, i.e. a new kind of behavior is observable only if the system/ model is at work.

5. Also, in many complex systems, we find a global coherence despite purely local non-linear interactions.

We can find examples of such systems every where: fluids in physics, traffic on road. Cellular automata are one other very nice example of complex systems. Here one can easily find emergent complex behavior of the system.

Within the set of complex dynamic systems a subclass can be distinguished which is known as complex adaptive systems. Its main distinguishing feature is that laws of the system are no longer constant. So, in a complex adaptive system both the behavior of individual elements and the nature of interactions may change thus giving rise to a higher-order dynamics. Examples of complex adaptive systems are the economy, genetic evolution, ecological systems, social systems and communication systems especially language system.

Approaches to model the complex system - "life"

Various approaches to understand the system are known, but each of them has its own drawbacks. This has led us to develop new approaches to model the system. Few approaches are listed below:

Behaviorist approach

Study the behavior of animals in the laboratory. But, the approach is inadequate for several reasons:

Firstly it suffers from ecological invalidity. Animals behave in abnormal ways when put in alien environments.

Second, behaviorism investigates little snippets of behavior; this approach removes behaviors from the pragmatic context that gives them their meaning.

We can say that behaviorist approach is at very premature state.

The Ethological approach

Says study organisms in their natural environment. Again drawbacks:

Real world is very messy; there are too many variables for clean experimental design. Simple factors like presence of other animals and their behavior, the weather, the ambient sounds etc can have a very great influence.

Besides the above, various ethical questions are associated with conducting such a study.

The neuroscience approach

The behaviorist and ethological approaches are in some sense black-box approaches, they tell us nothing of the mechanisms by which animals communicate, whereas this approach tries to understand the mechanism underlying various mental phenomena. However, a complete theory must unite both neuropsychological and ethological levels.

The A-life approach

This approach is also called "synthetic ethology" basically because it studies the behavior of synthetic organisms in synthetic environments.

Why A-life approach excels?

One of the biggest advantages of Alife is the artificialness of environment and simulations. Alife is the cradle of artificial species with all kinds of weirdness!!! Putting it in more formal way since everything is synthetic, all the variables and system parameters are under our control and thus we can model the system according to our convenience and experiment with the same. Not so easily possible in all other approaches.

Secondly, we find AI hasn’t brought any intelligence in the computers. Even today, the so called intelligent computers can just manipulate symbols blindly without even understanding what those symbols stand for. This is the concern expressed in “Symbol Grounding”. Alife is making this symbol grounding possible. Now intelligent computers not just manipulate symbols but also understand what the symbols stand for. This is in a way a big leap in the AI research.

Besides, alife techniques have been successfully been applied in understanding many complex systems. To give you a feel of the alife approach, in my next article, I’ll be discussing about the Alife approach to understand language origins.

References:

[1] Chris Adami and Titus Brown, What is Artificial Life? Seventh International Conference on Artificial Life, August 2000.

[2] Chris G. Langton, Zooland “The Artificial life resources”.

[3] Norman H Packard, Artificial Life, Encyclopedia of Cognitive Science, Macmillan, 2000 (pdf).

[4] Luc Steels, The synthetic modeling of language origins, Evolution of communication, Vol 1, 1997, pp 1 - 34.venkatbabukr38076.5559375

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