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G52AIM

ARTIFICIAL INTELLIGENCE METHODS

ARTIFICIAL LIFE

March 31st, 2010

Student Name: NGUYEN VINH HIEN

Student ID: 005560

Instructor: PROFESSOR ANDRZEJ BARGIELA

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School of Computer Science &Information Technology

MARKING SCHEME

SPRING 2009/2010

Student Name: NGUYEN VINH HIEN

Student No: 005560

Module Code: G52AIM

Title of Module: Artificial Intelligence Methods

Coursework Title: Investigative Report

AttributesMarks

Allotted

Marks

Awarded

1. Theoretical Background 3

2. Application Examples 2

3. Advantages and Limitations 3

4. Style and References 2

Total Marks

Examiners Comments:

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TABLE OF CONTENTS

1. THEORETICAL BACKGROUND.................................................... 2

2. ARTIFICIAL LIFE MODEL EXAMPLE............................................. 3

2.1 ZAMIN ..................................................................... 3

2.1.1 ZAMIN ARITIFICIAL LIFE MODEL .......................... 4

2.1.2 ZAMINS UNDERLAYING STRUCTURE .................... 4

2.1.3 THE SIX AGENTS................................................ 4

2.1.4 SIMPLE ORGANISMS GENERAL STRUCTURE .......... 5

2.1.5 ANIMALS GENERAL STRUCTURE........................... 5

2.1.6 AGENTS GENERAL STRUCTURE ............................ 7

2.2 PACKING PROBLEMS.................................................. 7

3. ADVANTAGES AND LIMITATIONS .............................................. 11

3.1 ADVANTAGES ........................................................... 11

3.2 LIMITATIONS............................................................ 12

4. REFERENCES .......................................................................... 12

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1. Theoretical Background:

Artificial life (Alife) has been a new discipline since 1980s, which is the

latest development direction of the computer science following Artificial

Intelligence (AI). It was name by Christopher Langton, an American

computer scientist, in 1986 [1]. Alife is interdisciplinary field of computer

science, biology, physics, mathematics, as well as philosophers and artists.

There are three categories of Alife: software, hardware, and wetware

applying techniques of biochemical laboratory.

Christopher Langton defined Alife as follow [2]: Alife is a discipline

that studies natural life by attempting to recreate biological phenomena from

scratch within computers and other artificial media. And it complements the

traditional analytic approach of traditional biology with a synthetic approach

by taking apart living organisms to see how they work, one attempts to puttogether systems that behave like living organisms. More specific, the

discipline of Alife studies the synthesis of forms and functions that appear

alive. It allows scientists to study about biology system outside the

observable accidents of history.

Eyal Reingold and Johnathan Nightingale [3] stated that Alife relates to

Biology in such a same way as AI relates to Psychology. It has been often

said that the goal of Alife is to offer a synthetic perspective which means it

starts with a simple rule, concept and combines them to see what complex

phenomena are produced [3]. The reason why makes Alife become a

research pursuit is that how accurately computer models developed through

Alife have reflected peoples observations of biological life. In 2000, Rodney

Brooks [4] gave the relationship between matter and life. In order to build

artificial systems which exhibit properties of living organisms, AI and Alife

scientists have to understand these properties. AI scientists focus on

perception, cognition, and generation of action, whereas Alife scientists pay

attention on evolution, reproduction, morphogenesis, and metabolism.

Furthermore, according to the International Society for Complexity,

Information, and Design (ISCID) [5], although AI and Alife are overlapping,

they are still different in their approach and history, which is Alife is

concerned with specific life-oriented algorithms (e.g. genetic algorithm can

mimic nature, thus relating to biology), whereas AI concerned with looking

at how human intelligence can be replicated, thus relating to psychology.

There are still some minor definitions that can contribute to get a full picture

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to construct a system which simulates different types of living strategies,

climate, controlling, and communication systems [8].

2.1.1 Zamin Artificial Life Model:

Zamin environment is a lattice that contains three major inhabitantgroups inside. They are simple organisms, animals, and agents. As defined

by Ramin Halavati et al. [8], simple organisms get material from the world

and reproduce, but they cannot move, communicate, or respond to stimuli.

In contrast, animals can move and choose their actions, they can manipulate

simple organisms and objects, trade information or energy, but they can

only access the world using a limited passage. Agents have different access

to the world and can control the world flows and cycles. Animals can

reproduce sexually or asexually and they can communicate through two

methods: they can broadcast a message in a selectable range or to transferit directly to a specific mate [8]. Every creature must have a minimum level

of energy to live and act. Each creature can absorb materials directly from

the world and convert them into energy. After that, creatures release items

to the world to flow again in worlds material and energy cycle [8].

2.1.2 Zamins underlying structure:

In Zamin environment, there are six agents being responsible for

creating and destructing creatures, flowing the time, managing the position

and resources, applying creatures will [8].

2.1.3 The Six Agents:

The Lives Lord, Espand: responsible for creating and managing

population control for all creatures.

The Times Lord, Zorwaan: responsible for timing control.

The Positions Lords, Hormoz: responsible for objects and creature

transfers in the world.

The Resources Lord, Kiumars: responsible for managing world

resources and energy transformation.

The Gates Lord, Ashtaad: responsible for controlling animals access

to the world level.

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The Actions Lord, Mitra: in charge of accepting creatures requests and

applying their will in the world.

2.1.4 Simple Organisms General Structure:

Simple organism structure is used to design creatures. A simpleorganism requests some resources, absorbs resources to get essential

energy for living, or produce seeds, and create another organism. Organism

must implement functions in order to create a creature. Simple properties

and functions can be seen in Table 1.

Property Description

Species Specifies the species that this creature belongs to.

Name The creatures name.

Age The creatures age.

Position The creatures location in world map.

Is Alive Indicates if creature is alive or is dead.

Energy Indicates how much energy creature has now.

Resources The amount of resources that this creature owns. There are four

sets of resources:

The absorbed resource which is used to build up body, get

energy, or create seeds.

The resources that are part of the body and cannot be used

for any other tasks.

The waist resources which are the result of previous

reactions and must be released.

The embryo resources which are gathered to produce seeds.

Table 1: Simple Organisms Properties

Source: Extracted from [8]

2.1.5 Animals General Structure:

Animals have more properties and actions compared to simple

organisms and must respond to more messages. Lists of action choices,

properties, and messages can be seen in table 2, 3, and 4.

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Property Description

Hatched Specifies whether the organism is still in eggs or has hatched out.

Sex Organisms sex, which can be male, female, or asexual.

Pregnant Tell if the organism is pregnant and producing some eggs.

Orientation Specifies the direction of the creatures face, which can be up,down, left, or right.

Attack Skill Organisms performance in attacking another creature.

Defense Skill Organisms performance in defending an attack from another

creature.

Table 2: Animals Extended Properties over Single Organisms


Action Description

Hatch A creature that is still in egg can make request and get out ofegg.

Move To move to an adjacent location.

Eat To eat something and absorb its resources.

Turn To change animals orientation.

Mate To mate another animal or start pregnancy.

Lay Egg To lay some eggs

Internal

Resource

Transfer

To transfer some of the absorbed resources to the body or the

embryo that will be put in egg.

Communicate To transfer a message to another animal or to broadcast a

message.

Give

Resources

To give some resources to another creature or on egg.

Table 3: Animals Action Choices


Message Description

Create A creature receives this message when it is about to be created. A

genome may be enclosed by the message.

Choose Action Each creature receives one Choose Action message at each time

step. The world view is enclosed by this message and can be used

to select action and experience the result of the selected action.

Die Once the creature is about to die, it receive this message.

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Mating

Proposal

Once the creature requests to mate another creature, the second

one receives this message and a view of the requestor. The

receiver can reply its opinion on mating.

Mate

RequestsResult

Once a creature replies its opinion, this message is sent back to

requestor. Then requestor can reply whether it wants to commitdating.

Get Genome

for Mating

Once two creatures have mated, the male one receives a

message to pass a copy of its genome for the creation of the

embryos genome.

Begin

Pregnancy

Once female creature receives this message and a copy of males

genome to start pregnancy.

Get Resources

and Genome

for Eggs

Once an animal requests to lay eggs, it receives this message to

pass the genomes and resources for the eggs.

Text Message Once an animal receives a text message from another one, this

message is received, enclosed by messages text and senders

identity.

Table 4: Animals Received Messages


2.1.6 Agents General Structure:

Six agents predefined in Zamin are the most powerful creatures, and

new agent can be added when necessary. In agent general structure, allcontrol and world running processes are done by agents. Thus, it is believe

to be easier to use and extend than conventional Zamin model. In order to

know in depth the extension of Zamin model, please refer to [8].

The main advantage of new model is the modularity. In order to alter

something in the new model, only need to rewrite one message handler for

one agent and register the agent, and for Alife which have may interconnect

parts, this is a big advantage [8]. However, this new model has two

disadvantages. First, the designer has to find a part which is needed to

change among too many parts, and second, once the changing part is found,

changing it could affect others.

2.2 Packing Problems:

Packing problems arise in many application areas such as pallet

loading, textile cutting, container stuffing, and placement problems. They

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solve optimization problems about how to find a good arrangement of

multiple objects in a large containing region without overlap; thus it is likely

to maximize the material utilization and minimize wasted area. In order to

solve optimization of cutting lines, this model is implemented in Zamin

agent-based artificial ecosystem [8]. The approach is based on group-making policies of agents [11].

Each shape is represented by an agent. The agents relations specify

their relative position and there are no overlapping friend agents. When a

group of friend agents is formed, the friendship patterns describe the

arrangement of their shapes. Then the friendship patterns are duplicated

and some of the relations get permanent. With this iterative approach, the

quantity of the good groups increases gradually and more acceptable

solutions appear [11]. Figure 1 shows the possible relations of two arbitrary

shapes, and figure 3 shows the diagram of entire optimization process.(More detail about the optimization process can be found in A Novel

Evolutionary Approach for Optimization of Two Dimensional Irregular Shapes

Allocation article [11])

Figure 1: Possible relations for two arbitrary shapes and the

representation of one sample relation


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Figure 2: A Sample of the Optimization Process


Figure 2 illustrates a sample of an optimization process. Stage 1 shows initial

agent pool with some copies of shapes that would be combined together. Stage

2 and 3 shows pool containing shapes after once and twice gather. In stage 4,

every combined shape is duplicated. Finally, stage 5 depicts that the whole

groups are divided into smaller groups of agents. The process will continue untilwhen an acceptable group is found.

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Figure 3: Diagram of the optimization process


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Figure 3: Average waste area with different shapes number. Verticalaxis: average wasted area. Horizontal axis: Shapes number


Figure 3 shows the average waste area for different number of shapes,

and it also states that there is no relation between number of shapes needed

to optimize and the waster area.

This approach is used in applications for cutting sheets and some

comparisons with other contributions and in some tests that can arrange

successful the requested shapes and its performance does not degrade with

increment of shapes count [11].

3. Advantages and Limitations:

3.1. Advantages:

Because the field of Alife is related to biology; therefore, it makes

biologist feel interested in. Today, biologists can have a new way to learnabout life. Thus, the approach of Alife is synthetic rather than reductionist.

The major advantage of looking at life from this perspective is to help

scientists recreate life in a different medium, and no longer limited to

carbon-based life system. When scientists examine organisms of the Earth,

they can discover things that define life. Thus, building life from Alife

perspective can help scientists find characteristic and principle of life.

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Other advantage of Alife is it can be applied to real-world problems,

such as being used for researches on many different subjects from

morphogenesis of corals [13] to the role of suicide [14] in a community.

Last but not least, synthetic approach can be seen in synthetic

chemistry, which is a field to create and mix chemical compounds which are

not found in nature. It not only gives scientists a better understanding the

theory of chemical phenomena, but also allows us to create new materials

and chemicals.

From above advantages, Alife has been used greatly to technology in

the fields of computers, robots, medicine, nanotechnology and other areas.

3.2. Limitations:

So far, Alife is mostly based on fixed, static algorithms. From thedefinition of Christopher Langton [2], Alife should have ability to possess

consciousness, and it should be based on evolution rather than static, fixed

algorithms. The algorithms limit Alife to a specific task or specific way of

thinking; make it incapable of achieving a certain level of thought. But real

life should be able to think and do anything to its further extent [10] which

means Alife should be allowed to develop its own ideas, to learn, to grow

and to change by itself.

4. References:

[1] Robert A. Wilson, Frank C. Keil (1999), The MIT encyclopedia of the

cognitive sciences, The MIT Press, p.37

[2]Artificial Life, online, retrieved 17 March 2010, available at

http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/ArtificialLife#cgl

[3] Eyal Reingold and Johnathan Nightingale, Artificial Life, online, retrieved

17 March 2010, available athttp://www.psych.utoronto.ca/users/reingold/courses/ai/Alife .html

[4] Rodney Brooks (August 2000), Artificial life-From Robot Dreams to

Reality, Nature 406, p.945-947.
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/ArtificialLife#cglhttp://www.psych.utoronto.ca/users/reingold/courses/ai/alife.htmlhttp://www.psych.utoronto.ca/users/reingold/courses/ai/alife.htmlhttp://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/ArtificialLife#cgl

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[5] What is the difference between Alife and Artificial Intelligence, online,

retrieved 17 March 2010, available at

https://alifedegrees.com/whatisalife/aiVsAl/

[6] Luis M. Rocha , Mark Bedau, Dario Floreano, Robert Goldstone,

Alessandro Vespignani, and Larry Yaeger (3-7 June, 2006), online, retrieved

17 March 2010, available at http://www.alifex.org/

[7] Wikipedia, Artificial Life, online, retrieved 17 March 2010, available at

http://en.wikipedia.org/wiki/Artificial_Life

[8] Ramin Halavati, Saeed B. Shouraki, Saman H. Zadeh, Pujan Ziaie, Caro

Lucas (2004), Zamin, an Agent Based Artificial Life Model, IEEE Computer

Society , Proceedings of the Fourth International Conference on Hybrid

Intelligent Systems, p.160 - 165.

[9] Zadeh, S.H., Halavati, R., Shouraki, S.B. (2004), Emerging simple

emotional states in Zamin artificial world, Proceedings of World Automation

Congress, p.43 48.

[10] Biology Kenyon College, Artificial Life, online, retrieved 17 March

2010, available at

http://biology.kenyon.edu/slonc/bio3/AI/A_LIFE/a_life.html

[11] Ramin Halavati, Saeed B. Shouraki, Mahdieh Noroozian, and Saman H.

Zadeh (2005), A Novel Evolutionary Approach for Optimization of Two

Dimensional Irregular Shapes Allocation, World Academy of Science,

Engineering and Technology.

[12] Andrew Adanmatzky, Maciej Komosinski (2005), Artificial Life Model in

Software, Springer.

[13] R.M.H.Merks, A. Hoekstra, J.Kaandorp. P. Sloot (2003), "A Problem

Solving Environment for Modelling Stony Coral Morphogenesis", Proceedings

of 3rd International Conference on Computational Sciences, Saint Petersburg,

Russia.

[14] S.Mascaro, K.B. Korb., A.E. Nicholson, "Suicide as an Evolutionary

Stable Strategy", Proceedings of the 6th European Conference on Artificial

Life, p. 120-132.
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