THOUGHTS: A Bottom-Up Approach for Artificial Life Simulations (Thoughts)
Overview
The field of artificial intelligence in computer science focuses on many
different areas of computing from computer vision to natural language
processing. These top-down approaches typically concentrate on human behavior
or other animal functions. In this article we look at a bottom-up approach to
artificial life and how emergent cell behavior can produce interesting results.
With this bottom-up alife approach, we are not interested in solving any
particular task, but we are interested in observing the adaptive nature of the
entities in our simulation. We also wanted to introduce those more familiar with
software engineering to biological systems and evolutionary theory concepts.
Life is all around us. Even with inorganic material it is possible that
microscopic organisms are covering that surface. Moving forward if we want to
study, analyze and work with artificial agents, we might consider systems that
have evolved behavior over a series of steps. We should not necessarily build a
specific tool with a specific purpose but the creature that is built from the
system may produce interesting properties which are unlike the clean-room
created software that we create today. Most software and hardware today is
written to specification, line for line, most code written for today's systems
are created by man. That software is designed, coded and tested. It would be
interesting if we could start a biological like system and interesting behavior
from the system evolves over time.
When studying life, it also makes sense to go all the back to pre-life on earth
and the creation of this planet. When you look across the globe, there are
millions of species, a lot of life out there. All of that life occurred from
mutations, replications, chaotic collisions with other organisms. What is
organic life? compared to inorganic life? Life evolves, minute after minute,
day after day, year after year for millions of years across a vast planet.
Inorganic matter interacts with organic matter and from within that very complex
system, complex behavior emerges. When you look at computing systems, every
piece of software, most software was created by some person. Every line of
code, every machine instruction is mostly known to the developer or the hardware
manufacturers. The intelligent designer in this case are teams of intelligent
software and computer engineer building complex machines to act as a tool for
some purpose. Some application.
Human beings and other animals have complex machinery embedded in them. Animals
have eyes, ears. Human beings have the neocortex that allows us to predict
events that may occur in the future based on past memories. But all of these
tools that animals use to survive evolved over time. Iteration after iteration
of successes and failures. The human brain has a purpose in the context of
human survival just like the eyes and ears have a purpose. But do human beings
have a purpose? Computers have a purpose. Smart phones are tools that have a
purpose.
With software, we add all of that complex behavior. Human beings posses amazing
skills and we use our brains to adapt to the environment. All of that behavior
and response to the world around us was created by millions of years of
mammalian evolution. We are autonomous beings. All of that behavior created by
seemingly random events over time. The machinery that makes up the human being
just happen to have happened. That is a powerful thought. If we can understand
parts of that evolutionary process, maybe we can model some of of those events
through computer simulations and also generate complex behavior. Imagine if we
could instantly absolute control over the evolutionary process. Imagine if we
could remove an animals brain or ears or eyes so that future generations don't
have those functions. That animal would be helpless and couldn't function but
it just happens that they do have those capabilities and can survive as long as
their abilities allow them to. Imagine if this force could remove your brain so
that future generations of humans didn't have a brain. Or we removed the
ability to walk, see or hear. Once again, we would be helpless and human
progress would halt that instance. The evolutionary process is a complex one but
all of the creatures of the earth that have survived up to this point all posses
skills that allow them to survive. The tools given them given them through that
process.
Basic Biology Concepts
Human beings may have 100 trillion cells. There are several hundred distinct
human cell types.
The most basic cells may have a cell wall, chromosomes, plasma membrane,
fibrils, ribosomes.
DNA is the blue print for life of a cell. It mostly static, mutations may be
introduced. DNA contains the instructions for a cell's structure and function.
It is the blueprint for how the cell runs, reproduces, builds and repairs
itself, and every other function necessary for cell life. Metabolism is a
chemical reaction
A protein is a generic term for anything that is made of amino acids. Proteins
are considered the "cellular machinery"; they are constantly being synthesized,
and play many essential structural and enzymatic roles within the cell. How
does a cell die? Proteisn sythensis may be interrupted. Protein synthesis uses
about 75 percent of a cell'ss energy. Protein is a macromolceule Macromolecules
that make up cell material
Bacterial cells can change patterns of enzymes, in order to adapt them to their
specific environment.
With traits, in order to show-up, a dominant trait needs only one trait unit
from one of the parents, and the recessive one needs two, from both parents, in
order to prevail, that is the reason why the ratio between occurrences of
dominant traits and recessive traits is. The same explanation applies to the
shape traits.
keywords: earth, biosphere, inorganic vs organic material, water, DNA,
mutations, replication, simple organisms, more complex organisms, animals, human
beings, life, death, mutations, evolution, blind watchmaker, mitochrondria,
flagella, pili, cell walls, cytoplasmic membranes, ribosomes, cytoplasm.
Introduction to Artificial Life
Conway's Game of Life cellular automaton is one of the most prominent examples
of cellular automata theory. The one dimensional program consists of a cell grid
typically with several dozen or more rows and similar number of columns. Each
cell on the grid has an on or off Boolean state. Every cell on the grid survives
or dies to the next generation depending on the game of life rules. If there are
too many neighbors surrounding a cell then the cell dies due to overcrowding. If
there is only one neighbor cell, the base cell dies due to under-population.
Activity on a particular cell is not interesting but when you run the entire
system for many generations, a group of patterns begin to form.
You may notice some common patterns in the figure. After so many iterations
through the game of life rules, only a few cells tend to stay alive. We started
with a large random number of alive cells and over time those cells died off. In
a controlled environment you may begin with carefully placed live cells and
monitor the patterns that emerge to model some other natural phenomena.
Summary
Moving forward if we want to study, analyze and work with artificial agents, we
might consider systems that have evolved behavior over a series of steps. We
might not build a specific tool with a specific purpose but the creature that is
built from the system may produce interesting properties which are unlike the
clean-room created software that we create today.
With this artificial life approach, but we also want to study the simple life
forms first before moving too fast forward like human behavior.
Resources:
[1] http://en.wikipedia.org/wiki/Avida
[2] Testing Darwin
The field of artificial intelligence in computer science focuses on many
different areas of computing from computer vision to natural language
processing. These top-down approaches typically concentrate on human behavior
or other animal functions. In this article we look at a bottom-up approach to
artificial life and how emergent cell behavior can produce interesting results.
With this bottom-up alife approach, we are not interested in solving any
particular task, but we are interested in observing the adaptive nature of the
entities in our simulation. We also wanted to introduce those more familiar with
software engineering to biological systems and evolutionary theory concepts.
Life is all around us. Even with inorganic material it is possible that
microscopic organisms are covering that surface. Moving forward if we want to
study, analyze and work with artificial agents, we might consider systems that
have evolved behavior over a series of steps. We should not necessarily build a
specific tool with a specific purpose but the creature that is built from the
system may produce interesting properties which are unlike the clean-room
created software that we create today. Most software and hardware today is
written to specification, line for line, most code written for today's systems
are created by man. That software is designed, coded and tested. It would be
interesting if we could start a biological like system and interesting behavior
from the system evolves over time.
When studying life, it also makes sense to go all the back to pre-life on earth
and the creation of this planet. When you look across the globe, there are
millions of species, a lot of life out there. All of that life occurred from
mutations, replications, chaotic collisions with other organisms. What is
organic life? compared to inorganic life? Life evolves, minute after minute,
day after day, year after year for millions of years across a vast planet.
Inorganic matter interacts with organic matter and from within that very complex
system, complex behavior emerges. When you look at computing systems, every
piece of software, most software was created by some person. Every line of
code, every machine instruction is mostly known to the developer or the hardware
manufacturers. The intelligent designer in this case are teams of intelligent
software and computer engineer building complex machines to act as a tool for
some purpose. Some application.
Human beings and other animals have complex machinery embedded in them. Animals
have eyes, ears. Human beings have the neocortex that allows us to predict
events that may occur in the future based on past memories. But all of these
tools that animals use to survive evolved over time. Iteration after iteration
of successes and failures. The human brain has a purpose in the context of
human survival just like the eyes and ears have a purpose. But do human beings
have a purpose? Computers have a purpose. Smart phones are tools that have a
purpose.
With software, we add all of that complex behavior. Human beings posses amazing
skills and we use our brains to adapt to the environment. All of that behavior
and response to the world around us was created by millions of years of
mammalian evolution. We are autonomous beings. All of that behavior created by
seemingly random events over time. The machinery that makes up the human being
just happen to have happened. That is a powerful thought. If we can understand
parts of that evolutionary process, maybe we can model some of of those events
through computer simulations and also generate complex behavior. Imagine if we
could instantly absolute control over the evolutionary process. Imagine if we
could remove an animals brain or ears or eyes so that future generations don't
have those functions. That animal would be helpless and couldn't function but
it just happens that they do have those capabilities and can survive as long as
their abilities allow them to. Imagine if this force could remove your brain so
that future generations of humans didn't have a brain. Or we removed the
ability to walk, see or hear. Once again, we would be helpless and human
progress would halt that instance. The evolutionary process is a complex one but
all of the creatures of the earth that have survived up to this point all posses
skills that allow them to survive. The tools given them given them through that
process.
Basic Biology Concepts
Human beings may have 100 trillion cells. There are several hundred distinct
human cell types.
The most basic cells may have a cell wall, chromosomes, plasma membrane,
fibrils, ribosomes.
DNA is the blue print for life of a cell. It mostly static, mutations may be
introduced. DNA contains the instructions for a cell's structure and function.
It is the blueprint for how the cell runs, reproduces, builds and repairs
itself, and every other function necessary for cell life. Metabolism is a
chemical reaction
A protein is a generic term for anything that is made of amino acids. Proteins
are considered the "cellular machinery"; they are constantly being synthesized,
and play many essential structural and enzymatic roles within the cell. How
does a cell die? Proteisn sythensis may be interrupted. Protein synthesis uses
about 75 percent of a cell'ss energy. Protein is a macromolceule Macromolecules
that make up cell material
Bacterial cells can change patterns of enzymes, in order to adapt them to their
specific environment.
With traits, in order to show-up, a dominant trait needs only one trait unit
from one of the parents, and the recessive one needs two, from both parents, in
order to prevail, that is the reason why the ratio between occurrences of
dominant traits and recessive traits is. The same explanation applies to the
shape traits.
keywords: earth, biosphere, inorganic vs organic material, water, DNA,
mutations, replication, simple organisms, more complex organisms, animals, human
beings, life, death, mutations, evolution, blind watchmaker, mitochrondria,
flagella, pili, cell walls, cytoplasmic membranes, ribosomes, cytoplasm.
Introduction to Artificial Life
Conway's Game of Life cellular automaton is one of the most prominent examples
of cellular automata theory. The one dimensional program consists of a cell grid
typically with several dozen or more rows and similar number of columns. Each
cell on the grid has an on or off Boolean state. Every cell on the grid survives
or dies to the next generation depending on the game of life rules. If there are
too many neighbors surrounding a cell then the cell dies due to overcrowding. If
there is only one neighbor cell, the base cell dies due to under-population.
Activity on a particular cell is not interesting but when you run the entire
system for many generations, a group of patterns begin to form.
You may notice some common patterns in the figure. After so many iterations
through the game of life rules, only a few cells tend to stay alive. We started
with a large random number of alive cells and over time those cells died off. In
a controlled environment you may begin with carefully placed live cells and
monitor the patterns that emerge to model some other natural phenomena.
Summary
Moving forward if we want to study, analyze and work with artificial agents, we
might consider systems that have evolved behavior over a series of steps. We
might not build a specific tool with a specific purpose but the creature that is
built from the system may produce interesting properties which are unlike the
clean-room created software that we create today.
With this artificial life approach, but we also want to study the simple life
forms first before moving too fast forward like human behavior.
Resources:
[1] http://en.wikipedia.org/wiki/Avida
[2] Testing Darwin
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