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biologically-inspired computinglecture 4
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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
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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.
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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)?
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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
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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
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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?
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Nature.com; ANDY POTTS; TURING FAMILY
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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
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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
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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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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.
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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
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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