Artificial Life - An Overview

Slides:



Advertisements
Similar presentations
Approaches, Tools, and Applications Islam A. El-Shaarawy Shoubra Faculty of Eng.
Advertisements

ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE SYSTEMS
Dealing with Complexity Robert Love, Venkat Jayaraman July 24, 2008 SSTP Seminar – Lecture 10.
G5BAIM Artificial Intelligence Methods
Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation Neural networks Evolutionary computation (e.g., genetic algorithms)
Fitting models to data. Step 5) Express the relationships mathematically in equations Step 6)Get values of parameters Determine what type of model you.
Genetic Algorithms Learning Machines for knowledge discovery.
Hilton’s Game of Life (HGL) A theoretical explanation of the phenomenon “life” in real nature. Hilton Tamanaha Goi Ph.D. 1st Year, KAIST, Dept. of EECS.
Today’s Plan Introduction to Artificial Life Cellular Automata
Nawaf M Albadia Introduction. Components. Behavior & Characteristics. Classes & Rules. Grid Dimensions. Evolving Cellular Automata using Genetic.
컴퓨터 그래픽스 분야의 캐릭터 자동생성을 위하여 인공생명의 여러 가지 방법론이 어떻게 적용될 수 있는지 이해
Artificial Chemistries Autonomic Computer Systems University of Basel Yvonne Mathis.
By Paul Cottrell, BSc, MBA, ABD. Author Complexity Science, Behavioral Finance, Dynamic Hedging, Financial Statistics, Chaos Theory Proprietary Trader.
The Role of Artificial Life, Cellular Automata and Emergence in the study of Artificial Intelligence Ognen Spiroski CITY Liberal Studies 2005.
Department of Information Technology Indian Institute of Information Technology and Management Gwalior AASF hIQ 1 st Nov ‘09 Department of Information.
Irreducibility and Unpredictability in Nature Computer Science Department SJSU CS240 Harry Fu.
CS 484 – Artificial Intelligence1 Announcements Lab 4 due today, November 8 Homework 8 due Tuesday, November 13 ½ to 1 page description of final project.
Computational Intelligence II Lecturer: Professor Pekka Toivanen Exercises: Nina Rogelj
Introduction to Lattice Simulations. Cellular Automata What are Cellular Automata or CA? A cellular automata is a discrete model used to study a range.
Artificial Intelligence/Life Presented by James H. Sunshine September 2, 2004.
Artificial Intelligence By Michelle Witcofsky And Evan Flanagan.
The Science of Complexity J. C. Sprott Department of Physics University of Wisconsin - Madison Presented to the First National Conference on Complexity.
I Robot.
Neural Networks and Machine Learning Applications CSC 563 Prof. Mohamed Batouche Computer Science Department CCIS – King Saud University Riyadh, Saudi.
“Politehnica” University of Timisoara Course Advisor:  Lucian Prodan Evolvable Systems Web Page:   Teaching  Graduate Courses Summer.
SIMULATIONS, REALIZATIONS, AND THEORIES OF LIFE H. H. PATTEE (1989) By Hyojung Seo Dept. of Psychology.
“It’s the “It’s the SYSTEM !” SYSTEM !” Complex Earth Systems
FREEDOM INTRODUCTORY QUESTIONS 1.Why is it that human beings, and not animals are only able to act morally 2.Define a human action. 3.Is freedom limited?
CS851 – Biological Computing February 6, 2003 Nathanael Paul Randomness in Cellular Automata.
Cellular Automata BIOL/CMSC 361: Emergence 2/12/08.
What is Evolution? How do things Evolve?. Ok, we have created the Earth Earth about 4.0 Ga. We now want to follow its evolution from past to present But.
Chapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC)
HUME ON THE ARGUMENT FROM DESIGN (Part 1 of 2) Text source: Dialogues Concerning Natural Religion, parts 2-5.
CS1001 Lecture 25. Overview Homework 4 Homework 4 Artificial Intelligence Artificial Intelligence Database Systems Database Systems.
Earth Systems Do Not Evolve To Equilibrium Fichter, Lynn S., Pyle, E.J., Whitmeyer, S.J.
제 2 주. 인공생명의 개요 Research into models and algorithms of artificial life H. Jiyang, H. Haiying and F. Yongzhe, Artificial Intelligence in Engineering, vol.
Origin Statement – August 8, 2012 From your experience so far, what do you know about science? Write down as much of the scientific method, in order, as.
Self-organizing algorithms Márk Jelasity. Decide Object control measure control loop Centralized Mindset: Control Loop ● problem solving, knowledge (GOFAI)
Sub-fields of computer science. Sub-fields of computer science.
Agent-Based Modeling ANB 218a Jeff Schank.
By Michael Alan Park, Ph.D. Central Connecticut State University
Simulating Evolutionary Social Behavior
What is cognitive psychology?
Questions and Ponderings On “Life”
ZOOLOGY—STUDY OF ANIMALS
On Routine Evolution of Complex Cellular Automata
Life in the earth system
Introduction to Evolution
Chapter 1 Introduction: Themes in the Study of Life
Why Be Ethical?/You are what You Do
Done Done Course Overview What is AI? What are the Major Challenges?
Section 2: Science as a Process
Chapter 3: Complex systems and the structure of Emergence
Introduction to Biology
Introduction Artificial Intelligent.
Modelling Dr Andy Evans In this lecture we'll look at modelling.
Biology: Exploring Life
Scientific Inquiry Unit 0.3.
R. W. Eberth Sanderling Research, Inc. 01 May 2007
The scientific study of life
Introduction to Artificial Intelligence Lecture 11: Machine Evolution
Dr. Unnikrishnan P.C. Professor, EEE
G5BAIM Artificial Intelligence Methods
Artificial Life and Emergent Behavior
CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano
Principles of Science and Systems
Hiroki Sayama NECSI Summer School 2008 Week 2: Complex Systems Modeling and Networks Agent-Based Models Hiroki Sayama
Introduction to Artificial Intelligence Instructor: Dr. Eduardo Urbina
Chapter 02 Lecture Outline
Cellular Automata (CA) Overview
Presentation transcript:

Artificial Life - An Overview Ritendra Datta Penn State University

What is Life? State of a functional activity and continual change, before death (defined complimentarily as end-of-life). Characterized by the capability to: Reproduce itself, Adapt to an environment in a quest for survival, and Take Actions independent of exterior agents.

Nature as a special case of Life The Biology of Nature so far been the scientific study of life on Earth based on Carbon-chain chemistry. However, nothing restricts the study of properties of life to carbon-chain chemistry; it is merely the only form of life so far available for study. Further motivation to study life as a generic concept comes from the hypothesis that we are perhaps just one possible atom combination that makes this life possible. We haven’t met other examples (Aliens).

…which brings us to A-Life Lack of any available non-carbon based life-forms motivates us to create an artificial environment and a set of rules for life to evolve. Artificial Life, or ALife or AL is the study of non-organic organisms, beyond the creations of nature, that possess the essential properties of life as we understand it, and whose environment is artificially created in an alternative media, which very often is a logical device like the computer.

ALife as a Synthesis approach Rather than being an analytical study of “natural” life, A-Life is a Synthesis approach to studying any form of Life. We have : an artificially-created environment (usually) within computers, A fairly universal set of rules and properties of life, derived from the one example we have of life - Natural life.

So what is the motivation? A-Life could have been dubbed as yet-another-approach to studying intelligent life, had it not been for the Emergent properties in life that motivates scientists to explore the possibility of artificially creating life and expecting the unexpected. An Emergent property is created when something becomes more than sum of its parts. For example, half a human is not capable of working without the other half, but together, capable of very complex behavior (not a representative example).

So where does A-Life fit in? The A-Life concept helps to: Study existing natural life forms by trying to simulate the generic rules they follow, the environmental parameters like entropy/chaos , and the seed, i.e. the initial set of elements on which the rules of life apply under the given environmental condition, in order to understand evolution in nature. Create new life within the digital world by creating new set of external parameters, seeds, and rules of evolution, and let life find a way.

Artificial Intelligence So is A-Life = AI ?? Both seem to approach similar problems, but… Artificial Life Artificial Intelligence Concept : Late 1980s Concept : 1960s Grounded in Biology, Physics, Chemistry, Mathematics. Pursued primarily in Comp. Sci, Engineering & Psychology. Studies Intelligence as part of Life itself Studies Intelligent behavior in isolation Bottom-Up approach - study synthesis Top-Down approach - focus is on results Views life-as-it-could-be Views life-as-it-is

A-Life : Emergence What you get when something is more than the sum of its parts. Human thoughts rely on nearly all cells that make up the brain - single cells are incapable of thought - thought is the emergence property of these cells coming together and interacting to give complex results - motivation behind CA, NN. Extreme example: Earth as a one living thing, consisting of whole of nature being in dynamic equilibrium, each part having baring on the other.

A-Life : Entropy Second Law of Thermodynamics : When two systems are joined together, the entropy (or chaos) in the combined system is greater than the sum of the individual systems. This roughly applies to all systems, including those that exchange information. Life is all about fighting against entropy : as other systems lose information to surroundings, life not only keeps hold of its information, but also increases its amount of information.

A-Life : Complexity Life is a complex system : It is a dynamic system that can keep on changing and evolving over a great period of time without dying. If the amount of information exchange in a system is varied from low to high, it gives Fixed, Periodic, and Chaotic systems in that order. Somewhere in between, a system exhibits complex behavior. Accordingly, each unit in a system either dies, freezes, pulsates, or behaves in a complex manner. Fixed No Change, No Death Periodic Change, No Evolution, No Death Chaotic Change, Evolution,Death Complex Change, Evolution, No Death

A-Life : Chaos Theory Chaos Theory explains apparent randomness - many apparently random events are not truly random - they are just iteration of simple rules on existing states (and possibly previous states) generating complex behavior - they live on the edge of total chaos. Most natural processes are chaotic - sea, wind. Some man-made processes are chaotic - Financial market. Lack of knowledge of all rules,inputs and seed prevents us from determining the exact state of such a system at a point, but knowledge of some of those dominant rules/inputs lead to possible prediction of general behavior of the system. This lack of knowledge of all parameters leads us to conclude it to be random behavior of the system.

A-Life : Current research areas Mathematical, Philosophical, Biological foundations, Social and Ethical implications of A-Life. Cellular Automata Neural Networks Genetic Algorithms Origin, Self-organization, Repair and Replication Evolutionary / Adaptive Dynamics Autonomous,Adaptive and Evolving Robots Software Agents (good/evil) Emergent Collective Behaviors, Swarms. Synthetic/Artificial Chemistry/Biology/Materials Applications: Finance, Economics, Gaming, MEMS etc

ALife:Foundation/Implications Research on Foundation tries to answer questions about the motivation behind such a ground-breaking concept, using our existing knowledge base in Math, Chemistry, Biology, Philosophy of life etc. The Question is “How, why and where can the ALife approach succeed (or fail)?” Research on Implications tries to understand and explain how the extension of life as a generic concept impacts our understanding of the very basics of natural life, shattering (or possibly not affecting) many-a-belief about God, creation and destruction. The Question here is “How does ALife fit in (if at all) to the present-day social setup of morals and ethics, often laid out by the various religious texts ?”

Alife : Cellular Automata Inspired by the way Natural biological cells behave and interact with their neighboring cells by following rules set out by the DNA code in them. Cellular Automata (CA) is an array of N-dimensional ‘cells’ that interact with their neighboring cells according to a pre-determined set of rules, to generate actions, which in turn may trigger a new series of reactions on itself or its neighbors. The best known example is Conway’s Life, which is a 2-state 2-D CA with simple rules (see on right) applied to all cells simultaneously to create generations of cells from an initial pattern. Different initial patterns generate different behavorial patterns, some die away (unstable), some blink (periodic), and the rest show complex behavior by continuing to live and evolve. Conway’s Life: Rules A living cell with 0-1 8-neighbors dies of isolation A living cell with 4+ 8-neighbors dies from overcrowding All other cells are unaffected