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[email protected] http://informatics.indiana.edu/rocha/i-bic biologically Inspired computing INDIANA UNIVERSITY Informatics luis rocha 2015 biologically-inspired computing lecture 4
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[email protected]://informatics.indiana.edu/rocha/i-bic

biologicallyInspired

computing

INDIANAUNIVERSITY

Informatics luis rocha 2015

biologically-inspired computinglecture 4

biologicallyInspired

computing

[email protected]://informatics.indiana.edu/rocha/i-bic

INDIANAUNIVERSITY

Informatics luis rocha 2015

course outlook

Assignments: 35% Students will complete 4/5 assignments

based on algorithms presented in class Lab meets in I1 (West) 109 on Lab

Wednesdays Lab 0 : January 14th (completed)

Introduction to Python (No Assignment) Lab 1 : January 28th

Measuring Information (Assignment 1) Due February 11th

Lab 2 : February 11th

L-Systems (Assignment 2)

Sections I485/H400

biologicallyInspired

computing

[email protected]://informatics.indiana.edu/rocha/i-bic

INDIANAUNIVERSITY

Informatics luis rocha 2015

Readings until now

Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural

Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 1 – Natural Computing

Lecture notes Chapter 1: “What is Life?” Chapter 2: “The logical Mechanisms of Life”

posted online @ http://informatics.indiana.edu/rocha/i-bic Papers and other materials

Life and Information Gleick, J. [2011]. The Information: A History, a Theory, a Flood.

Random House. Chapter 8. Kanehisa, M. [2000]. Post-genome Informatics. Oxford

University Press. Chapter 1. Logical mechanisms of life (H400, Optional for I485)

Langton, C. [1989]. “Artificial Life” In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47.

Optional Readings Aleksander, I. [2002]. “Understanding Information Bit by

Bit”. In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Granta.

[email protected]://informatics.indiana.edu/rocha/i-bic

biologicallyInspired

computing

INDIANAUNIVERSITY

Informatics luis rocha 2015

Do, do, do, do, do, do, do, do, doBefore we leave

Lemme tell y’all a lil’ somethingUptown Funk you up, Uptown Funk you up

Come on, danceJump on it

If you sexy, than flaunt itIf you freaky, than own it

Don’t brag about it, come show meCome on, dance

Jump on itIf you sexy, than flaunt it

Well, it’s Saturday night and we in the spot

Don’t believe me, just watchUptown Funk you up, Uptown Funk you up (say whaa?)

Uptown Funk you up, Uptown Funk you up

information of sequential messagesrate of removing uncertainty of each symbol

“syntactic” surprise But what about

function and meaning (semantics)?

biologicallyInspired

computing

[email protected]://informatics.indiana.edu/rocha/i-bic

INDIANAUNIVERSITY

Informatics luis rocha 2015

Langton, C. [1989]. “Artificial Life” In: Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47.

the logical mechanisms of life

Chris Langton Artificial Life can contribute to

theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be

life as a property of the organization of matter, rather than a property of the matter which is so organized The way information is processed

Whereas biology has largely concerned itself with the material basis of life, Artificial Life is concerned with the formal basis of life. views an organism as a large

population of simple machines Synthetic approach or emergent

behavior

life-as-it-could-be

biologicallyInspired

computing

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scientific approaches of life

Analytical Reduction to (non-

living) components Reductionism

Life is complicated chemistry

Tied to specific materiality

Does not allow emergence Function, control,

measurement, categorization, information are unnecessary “illusions”

Synthetic Construction from

components Holist

Life is Organization Networks of

components Universal or

implementation independent

Emergence “bottom-up” approach

biologicallyInspired

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what is non-life-as-it-could be?

Alife must be compared to something What is the formal/logical threshold of complexity?

Hard Alife must provide a set of rules to distinguish Alife from artificial matter

Weak Alife needs to be able to test design principles of life with simulations Bio-inspired computing needs only to produce good

results in engineering problems Comparison to “life-like” behavior is too subjective

theories of life methodology requires existing theories of life to be

compared against contributes to the meta-methodology of Biology

test and improve beyond material constraints, such as the incomplete fossil record or measurement of cellular activity

criteria for deciding good simulations or realizations?

biologicallyInspired

computing

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INDIANAUNIVERSITY

Informatics luis rocha 2015

Nature.com; ANDY POTTS; TURING FAMILY

biologicallyInspired

computing

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cybernetics

Synthetic approach Engineering-inspired Supremacy of mechanism

Postwar culture of problem solving Interdisciplinary teams Cross-disciplinary methodology

All can be axiomatized and computed Mculloch&Pitts’ work was major influence

“A logical calculus of the ideas immanent in nervous activity”. Bulletin of Mathematical Biophysics 5:115-133 (1943).

A Turing machine (any function) could be implemented with a networkof simple binary switches (if circularity/feedback is present)

post-war science

Macy Conferences: 1946-53

Warren S. McCulloch

Claude Shannon

Margaret Mead

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Cybernetics was born

The Feedback Mechanisms and Circular Causal Systems in Biology and the Social Sciences March 1946 (10 meetings between 1946

and 1953) Interdisciplinary

Since a large class of ordinary phenomena exhibit circular causality, and mathematics is accessible, let’s look at them with a war-time team culture

Participants John Von Neumann, Leonard Savage,

Norbert Wiener, Arturo Rosenblueth, Walter Pitts, Margaret Mead, Heinz von Foerster, Warren McCulloch, Gregory Bateson, Claude Shannon, Ross Ashby, etc.

Key concepts Homeostasis, Circular causality

requiring negative feedback (postulated to be very common)

Present state becomes input for action at next moment: State-determined systems

The mathematics were finally accessible

post-war science: the Josiah Macy Jr. Foundation Meetings

biologicallyInspired

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British Cybernetics

The Ratio Club (starting in1949) British cybernetics meetings

William Ross Ashby, W. Grey Walter, Alan Turing. etc “computation or the faculty of mind which calculates, plans

and reasons” Also following Wiener’s use of “Machina ratiocinatrix” in

Cybernetics (1948), following Leibniz’ “calculus ratiocinator”

Turing as cybernetician

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Shannon’s mouse

trial and error algorithm information as reduction of uncertainty in the presence

of alternatives (combinatorics) lifelike behavior

trial and error to learn path from many alternatives adapts to new situations

how is learning achieved? Correct choices, information gained from reduced

uncertainty, must be stored in memory memory of information as a design principle of

intelligence in uncertain environments 75 bit memory stored in (telephone) switching relays

Brain as (switching) machine

controlling information to achieve life-like behavior

biologicallyInspired

computing

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(complex) systems science

Systemhood properties of nature Robert Rosen

Systems depends on a specific adjective: thinghood

Systemhood: properties of arrangements of items, independent of the items Similar to “setness” or cardinality

George Klir Organization can be studied with the

mathematics of relations S = (T, R)

S: a System, T: a set of things(thinghood), R: a (or set of) relation(s) (Systemhood)

Examples Collections of books or music file are sets But organization of such sets are systems

(alphabetically, chronologically, typologically, etc.)

a science of organization across disciplines

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biologicallyInspired

computing

INDIANAUNIVERSITY

Informatics luis rocha 2015

complex networksexample of general principle of organizationBarabasi-Albert Model: leads to power-law

node degree distributions in networks Amaral et al: Most real networks have a cut-off

distribution for high degree nodes which can be computationally modeled with vertex aging.

biologicallyInspired

computing

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Informatics luis rocha 2015

artificial life as (complex) systems science

A system possesses systemhood and thinghood properties Thinghood refers to the specific material that

makes up the system Systemhood are the abstracted properties

E.g. a clock can be made of different things, but there are implementation-independent properties of “clockness”

Systems science deals with the implementation-independent aspects of systems Robert Rosen, George Klir…

systemhood

biologicallyInspired

computing

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INDIANAUNIVERSITY

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Next lectures

Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural

Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 1, pp. 1-23 – Natural Computing Chapter 8 - Artificial Life Chapter 7, sections 7.1, 7.2 and 7.4 – Fractals and L-Systems Appendix B.3.1 – Production Grammars

Lecture notes Chapter 1: What is Life? Chapter 2: The logical Mechanisms of Life Chapter 3: Formalizing and Modeling the World

posted online @ http://informatics.indiana.edu/rocha/i-bic Papers and other materials

Logical mechanisms of life (H400, Optional for I485) Langton, C. [1989]. “Artificial Life” In Artificial Life. C. Langton

(Ed.). Addison-Wesley. pp. 1-47. Optional

Flake’s [1998], The Computational Beauty of Life. MIT Press. Chapter 1 – Introduction Chapters 5, 6 (7-9) – Self-similarity, fractals, L-Systems

readings


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