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Autonomous Agents
on the Web:
Beyond Linking and Meaning
Mike Amundsen
Principal API Architect
Layer 7 Technologies
@mamund
Preface
Relevant past, present, and future activities
Preface
• 2010 on the Web
• Abstract the hypermedia
• Provide analysis tools
• Hypermedia Factors
http://g.mamund.com/factors
Preface
• 2011 - O’Reilly
• Hypermedia focus
• Program the message
Preface
• 2012 @ WS-REST
• Affordance focus
• Program the network
Preface
• 2013 – O’Reilly
• Profile focus
• Program the description
• Application-Level
Profile Semantics (ALPS)
http://alps.io
Preface
But today, something different…
Autonomous Agents on the Web: Beyond Linking and Meaning Mike Amundsen Keynote SALAD2013
"One does not discover new lands without
consenting to lose sight of the shore for a
very long time“
- André Gide (1869-1951)
Background
Information Theory, Complex Systems,
and Hypermedia
Maxwell’s Demon
• James Clerk Maxwell (1831 - 1879)
• “... if we conceive of a being whose faculties
are so sharpened that he can follow every
molecule in its course…”
• Second Law of
Thermodynamics
“has only a
statistical certainty”
Boltzmann
• Ludwig Boltzmann (1844 - 1906)
• “Boltzman entropy”
• Macro- & micro-states
• Each possibility is a microstate
• The probability of a
macrostate is the
function of all the
microstates.
Shannon & Information
• Claude Shannon (1916 – 2001)
• “The number of bits needed to represent the
result of an uncertain event is given by its
entropy.”
• Surprisal: the "surprise" of seeing the
outcome - a highly improbable
outcome is very surprising.
(Tribus, 1961)
Turing, Tapes, & Halting
• Alan Turing (1912 – 1954)
• A Turing machine is a hypothetical device
that manipulates symbols on a strip of tape
according to a table of rules.
• “Turing's paper ... contains, in essence, the
invention of the modern
computer.” (Minsky, 1967)
• “… decide whether the
program finishes running or
continues to run forever”
Gödel and Incompleteness
• Kurt Gödel (1906 – 1978)
• “This statement is unprovable.”
• Treats the string as both data and program
Von Neumann computing
• John von Neumann (1903 – 1957)
• Described a computer architecture in which
the data and the program are both stored in
the computer's memory in the same address
space.”
• Theory of Self
Reproducing
Automata (1966)
Genes
• DNA/RNA store both the data and program.
• mRNA uses “alternative splicing” where it
greatly increases biodiversity.
Fielding architecture
• Roy Fielding (1965 - )
• “Architectural Styles and the Design of
Network-based Software Architectures”
(2001)
• “each component cannot "see" beyond the
immediate layer with
which they are
interacting.”
• “…the information
becomes the
affordance…”
Complex Systems
• “Large networks of components with no
central control and simple rules of operation
give rise to collective behavior, sophisticated
information processing, and adaptation via
learning or evolution.” (Mitchell, 2001)
• “Exhibits non-trivial emergent and
self-organizing behavior.”
The Web
• “The Web … [has] many large scale
properties … which lead to “adaptive”
behavior for the system as a whole.”
(Mitchell 2001)
So much for the background!
Current State
Media Types, HTTP, and Kelvin-ism
Media Types
• More registered hypermedia-style designs in
the last two years than in the last ten.
o Maze+XML (experimental)
o HAL (XML, JSON)
o Collection+JSON
o Siren (JSON)
o Hydra (JSON-LD)
o JSON-API (pending)
Media Types and entropy
• Designs vary in their level of “surprise”
• “surprisal” == “entropy”
• Lower the entropy, the less value the
information
• Higher the entropy, the more energy needed
to process the information.
Media Types and entropy
• text/uri-list
• Low entropy/surprisal
• Low energy needs
Media Types and entropy
• text/plain
• High entropy/surprisal
• High energy needs
Media Types and entropy
• text/html
• Moderate entropy/surprisal
• Moderate energy needs
Media Types and entropy
• From the “machine point of view”…
• What is the balance between entropy and
energy?
• Energy = computing power (coding time,
source code, memory, etc.)
Media Types and entropy
• Most applications on the Web are “one-off”
affairs - custom-coded for each solution.
• This is “high-energy computing!”
HTTP
• Hypertext Transfer Protocol
Ver 0.9 (1991) – Ver 1.1 (1999) <10 years
• HTTPbis (2013?) ~15 years since 1.1
• HTTP 2.0 (20??) >20 years since 1.1?
• No protocol-level
changes, but several
transport-level changes.
HTTP
• The Web is currently highly dependent on
a single protocol.
• Most new “protocols” build upon HTTP
o SPARQL 1.1 Graph Store HTTP Protocol.
• Most new media types assume HTTP
o JSON-LD
o HAL
The Irony of HTML and HTTP is…
It is difficult to imagine what it would be like without them.
Questions for you…
• How long will HTTP last?
• When will HTML no longer be dominant?
• How will this affect your own thinking?
• How will this affect the Web?
Kelvin-ism
• Lord Kelvin computed the age of the earth
based on “heat decay” and concluded:
“…it was more than 20 and less than 40
million year old, and probably much nearer
20 than 40”. (Kelvin, 1897)
• To his dying day, Kelvin refused to
accept the validity of other points
of view.
Near Term
Lowering entropy, decoupling protocols,
focusing on networks
Near Term – Lowering entropy
• We need more media type designs
• We need to design for low-entropy and high
information
• We need to design for machines, not humans
Near Term – Lowering entropy
• Three semantic levels in network messages
o Structure (XML, JSON, YAML, etc.)
o Protocol (H-Factors)
o Semantics (Domain concepts)
• We commonly see:
o Structure = low surprise
o Protocol = high surprise
o Semantics = high surprise
The higher the surprise in the message, the
higher the dependence on custom code on the
client/server.
Near Term – Lowering entropy
• Hypermedia Factors can lower Protocol
Surprise
• Many designs are still unexplored.
Near Term – Lowering entropy
• Profiles can lower Semantic Surprise
• http://alps.io
Near Term – Lowering entropy
• We need more machine-oriented media
types.
• Text can add entropy for machines.
• rel=“users”
vs.
<a … >Users</a>
• Imagine a hypermedia type
that humans could not
understand, but machines could.
The higher the dependence on machine-
readable messages, the lower the entropy.
Near Term – Decoupling protocols
• Most media type designs today assume a
dependence on a single protocol – HTTP.
Near Term – Decoupling protocols
• Message designs should be protocol-
agnostic.
• Use “Protocol Mapping”
to associate media-type
keywords with a selected
protocol (HTTP, FTP,
WS, CoAP, etc.)
• http://g.mamund.com/class-sked
Near Term – Focusing on networks
• Most implementations are stand-alone, one-
off models.
• We treat the Web as a sea filled with islands,
each one only barely aware of the others.
“The WWW is fundamentally
a distributed hypermedia application.”
Richard Taylor (2010)
Near Term – Focusing on networks
• The Web, biology, & social communities
exhibit properties of a “scale-free” network
• Barabási-Albert model for “preferential
attachment” (1999)
Near Term
• Lower entropy in messages
• Reduce protocol dependence
• Treat the network as the application
And there are some hard things, too.
Futures
No more central control, adaptation through
variation, competing for resources
Futures – No more central control
• If the WWW is the application, where is the
CPU? The storage? The program?
• Cellular Automata (Ulam & Von Neumann,
1940s)
• Conway’s Game of Life (1970s)
Futures – No more central control
• Cellular automata are discrete, abstract
computational systems
• In cellular automata information appears as
statistical probabilities.
• See Wolfram’s Atlas
http://atlas.wolfram.com/01/01/
Futures – No more central control
• Basic principles for automata
o Information takes the form of statistics and patterns
across the system
o Information is communicated via sampling
o There exists some level of random behavior
o Rely on fine-grained architecture, large numbers of
simple elements.
Futures – No more central control
• In “Future Web” we will create discrete,
abstract programs and they will interact
across the network.
• “What gets done on
the ‘net stays on
the ‘net.”
Futures – Adaptation via variation
• Machines will need to adapt to conditions,
learn and pass on traits.
• Learning happens via many passes and
survival of the ‘most fit’ for the task.
• “Robby” and the soda cans
o Start w/ 1xxx random attempts
o Score highest 2, splice
o Add random mutation
o Repeat
• http://g.mamund.com/robby
How can we model adaptation on the Web?
Futures – Competing for resources
• With Robby – there is a “score-keeper” for
the entire system.
• On the Web there is no score-keeper.
• In living systems, ‘scoring’ is done through
competing for limited resources.
Futures – Competing for resources
• In “Future Web” programs may compete for
scarce resources such as memory, storage,
cycles.
• RBNs (Random Boolean Networks) offer
a way to “keep score”
without central control.
(Kauffman, 1969).
• Uses attractors
o Fixed
o Oscillating
o Random
How can we model competition on the Web?
Summary
Time to head back toward shore…
Summary
• Information theory, complex biological
systems, hypermedia and the Web all share
some similar properties
Summary
• However, our current efforts ignore these
features and contain a high degree of
entropy, coupling, and lack interdependence.
Summary
• We can start today by creating low-entropy
machine-oriented messages, decouple from
network protocols, and treat the network as a
single application space.
Summary
• In the future we’ll need to give up central
control, we’ll build discrete automata, and
we’ll create a network where variation and
competition are possible.
However, in order to accomplish all that…
Autonomous Agents on the Web: Beyond Linking and Meaning Mike Amundsen Keynote SALAD2013
We must be willing to lose sight of the shore.
Autonomous Agents
on the Web:
Beyond Linking and Meaning
Mike Amundsen
Principal API Architect
Layer 7 Technologies
@mamund
Image Credits (1)
• http://th04.deviantart.net/fs71/PRE/f/2012/082/1/a/sunrise_at_sea_by_rumorvolat-d4too36.jpg
• http://www.pynchon.pomona.edu/entropy/demon.gif
• http://www.scholarpedia.org/w/images/thumb/e/e2/Timesarrowfig2.jpg/400px-Timesarrowfig2.jpg
• http://upload.wikimedia.org/wikipedia/en/e/e5/Shannonmouse.PNG
• http://upload.wikimedia.org/wikipedia/commons/thumb/7/7b/Lego_Turing_Machine.jpg/220px-
Lego_Turing_Machine.jpg
• http://notyourmomsfom.files.wordpress.com/2013/04/godel.jpg
• http://en.wikipedia.org/wiki/File:Nobili_Pesavento_2reps.png
• http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_derivation.gif
• http://jasss.soc.surrey.ac.uk/13/2/reviews/3.gif
• http://www.let.leidenuniv.nl/history/ivh/www_proposal.gif
• http://www.biostat.wisc.edu/~cdewey/mercator/cliques.png
• http://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Splicing_overview.jpg/220px-
Splicing_overview.jpg
• http://www.holon.se/folke/kurs/Distans/Ekofys/fysbas/LOT/exergy_tube.jpg
• http://ic.pics.livejournal.com/rose_griffes/11821574/185555/185555_600.jpg
• http://gigaom2.files.wordpress.com/2012/06/iphoneapps.jpg
• http://rubytrain.files.wordpress.com/2011/12/http.jpg
Image Credits (2)
• http://en.wikipedia.org/wiki/File:Kelvin-1200-scale1000.jpg
• http://shamangene.com/BLOG/wp-content/uploads/2012/09/mayan_calendar1.jpg
• http://shamangene.com/BLOG/wp-content/uploads/2012/09/mayan_calendar1.jpg
• http://www.artdes.monash.edu.au/design/assets/design_courses_communication.jpg
• http://images.fineartamerica.com/images-medium-large/hear-no-evil--see-no-evil--speak-no-evil-
william-meemken.jpg
• http://etutorials.org/shared/images/tutorials/tutorial_37/02fig03.gif
• http://walyou.com/wp-content/uploads//2011/01/sewer-maintenance-machine.jpg
• http://blog.luxuryproperty.com/wp-content/uploads/image/dubai/The-World-islands-Dubai.jpg
• http://www.ics.uci.edu/~taylor/pictures/Richard%20Taylor.jpg
• http://www.network-
science.org/fig_complex_networks_powerlaw_scalefree_node_degree_distribution_large.png
• http://upload.wikimedia.org/wikipedia/commons/e/e5/Gospers_glider_gun.gif
• http://atlas.wolfram.com/01/01/
• http://manwithoutqualities.files.wordpress.com/2010/02/swarming.jpg
• http://plato.stanford.edu/entries/cellular-automata/fig5.jpg
• http://www.styryx.com/img/itech/program/ai/genetic/robby/assignment.jpg
• http://www.historyrv.com/blog/wp-content/uploads/2011/12/natural_selection.png
Image Credits (3)
• http://steve.files.wordpress.com/2006/03/Matrix%20tut%202.jpg
• https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/Julia_immediate_basin_1_3.png/240
px-Julia_immediate_basin_1_3.png
• http://www.dichotomistic.com/images/scale-free.gif
• http://www.askamathematician.com/wp-content/uploads/2011/12/pip6_0747.jpg.jpg
• http://www.webnotwar.ca/wp-content/uploads/2011/02/semantic-web.png
• http://images4.alphacoders.com/262/262524.jpg

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Autonomous Agents on the Web: Beyond Linking and Meaning Mike Amundsen Keynote SALAD2013

  • 1. Autonomous Agents on the Web: Beyond Linking and Meaning Mike Amundsen Principal API Architect Layer 7 Technologies @mamund
  • 2. Preface Relevant past, present, and future activities
  • 3. Preface • 2010 on the Web • Abstract the hypermedia • Provide analysis tools • Hypermedia Factors http://g.mamund.com/factors
  • 4. Preface • 2011 - O’Reilly • Hypermedia focus • Program the message
  • 5. Preface • 2012 @ WS-REST • Affordance focus • Program the network
  • 6. Preface • 2013 – O’Reilly • Profile focus • Program the description • Application-Level Profile Semantics (ALPS) http://alps.io
  • 9. "One does not discover new lands without consenting to lose sight of the shore for a very long time“ - André Gide (1869-1951)
  • 10. Background Information Theory, Complex Systems, and Hypermedia
  • 11. Maxwell’s Demon • James Clerk Maxwell (1831 - 1879) • “... if we conceive of a being whose faculties are so sharpened that he can follow every molecule in its course…” • Second Law of Thermodynamics “has only a statistical certainty”
  • 12. Boltzmann • Ludwig Boltzmann (1844 - 1906) • “Boltzman entropy” • Macro- & micro-states • Each possibility is a microstate • The probability of a macrostate is the function of all the microstates.
  • 13. Shannon & Information • Claude Shannon (1916 – 2001) • “The number of bits needed to represent the result of an uncertain event is given by its entropy.” • Surprisal: the "surprise" of seeing the outcome - a highly improbable outcome is very surprising. (Tribus, 1961)
  • 14. Turing, Tapes, & Halting • Alan Turing (1912 – 1954) • A Turing machine is a hypothetical device that manipulates symbols on a strip of tape according to a table of rules. • “Turing's paper ... contains, in essence, the invention of the modern computer.” (Minsky, 1967) • “… decide whether the program finishes running or continues to run forever”
  • 15. Gödel and Incompleteness • Kurt Gödel (1906 – 1978) • “This statement is unprovable.” • Treats the string as both data and program
  • 16. Von Neumann computing • John von Neumann (1903 – 1957) • Described a computer architecture in which the data and the program are both stored in the computer's memory in the same address space.” • Theory of Self Reproducing Automata (1966)
  • 17. Genes • DNA/RNA store both the data and program. • mRNA uses “alternative splicing” where it greatly increases biodiversity.
  • 18. Fielding architecture • Roy Fielding (1965 - ) • “Architectural Styles and the Design of Network-based Software Architectures” (2001) • “each component cannot "see" beyond the immediate layer with which they are interacting.” • “…the information becomes the affordance…”
  • 19. Complex Systems • “Large networks of components with no central control and simple rules of operation give rise to collective behavior, sophisticated information processing, and adaptation via learning or evolution.” (Mitchell, 2001) • “Exhibits non-trivial emergent and self-organizing behavior.”
  • 20. The Web • “The Web … [has] many large scale properties … which lead to “adaptive” behavior for the system as a whole.” (Mitchell 2001)
  • 21. So much for the background!
  • 22. Current State Media Types, HTTP, and Kelvin-ism
  • 23. Media Types • More registered hypermedia-style designs in the last two years than in the last ten. o Maze+XML (experimental) o HAL (XML, JSON) o Collection+JSON o Siren (JSON) o Hydra (JSON-LD) o JSON-API (pending)
  • 24. Media Types and entropy • Designs vary in their level of “surprise” • “surprisal” == “entropy” • Lower the entropy, the less value the information • Higher the entropy, the more energy needed to process the information.
  • 25. Media Types and entropy • text/uri-list • Low entropy/surprisal • Low energy needs
  • 26. Media Types and entropy • text/plain • High entropy/surprisal • High energy needs
  • 27. Media Types and entropy • text/html • Moderate entropy/surprisal • Moderate energy needs
  • 28. Media Types and entropy • From the “machine point of view”… • What is the balance between entropy and energy? • Energy = computing power (coding time, source code, memory, etc.)
  • 29. Media Types and entropy • Most applications on the Web are “one-off” affairs - custom-coded for each solution. • This is “high-energy computing!”
  • 30. HTTP • Hypertext Transfer Protocol Ver 0.9 (1991) – Ver 1.1 (1999) <10 years • HTTPbis (2013?) ~15 years since 1.1 • HTTP 2.0 (20??) >20 years since 1.1? • No protocol-level changes, but several transport-level changes.
  • 31. HTTP • The Web is currently highly dependent on a single protocol. • Most new “protocols” build upon HTTP o SPARQL 1.1 Graph Store HTTP Protocol. • Most new media types assume HTTP o JSON-LD o HAL
  • 32. The Irony of HTML and HTTP is… It is difficult to imagine what it would be like without them.
  • 33. Questions for you… • How long will HTTP last? • When will HTML no longer be dominant? • How will this affect your own thinking? • How will this affect the Web?
  • 34. Kelvin-ism • Lord Kelvin computed the age of the earth based on “heat decay” and concluded: “…it was more than 20 and less than 40 million year old, and probably much nearer 20 than 40”. (Kelvin, 1897) • To his dying day, Kelvin refused to accept the validity of other points of view.
  • 35. Near Term Lowering entropy, decoupling protocols, focusing on networks
  • 36. Near Term – Lowering entropy • We need more media type designs • We need to design for low-entropy and high information • We need to design for machines, not humans
  • 37. Near Term – Lowering entropy • Three semantic levels in network messages o Structure (XML, JSON, YAML, etc.) o Protocol (H-Factors) o Semantics (Domain concepts) • We commonly see: o Structure = low surprise o Protocol = high surprise o Semantics = high surprise
  • 38. The higher the surprise in the message, the higher the dependence on custom code on the client/server.
  • 39. Near Term – Lowering entropy • Hypermedia Factors can lower Protocol Surprise • Many designs are still unexplored.
  • 40. Near Term – Lowering entropy • Profiles can lower Semantic Surprise • http://alps.io
  • 41. Near Term – Lowering entropy • We need more machine-oriented media types. • Text can add entropy for machines. • rel=“users” vs. <a … >Users</a> • Imagine a hypermedia type that humans could not understand, but machines could.
  • 42. The higher the dependence on machine- readable messages, the lower the entropy.
  • 43. Near Term – Decoupling protocols • Most media type designs today assume a dependence on a single protocol – HTTP.
  • 44. Near Term – Decoupling protocols • Message designs should be protocol- agnostic. • Use “Protocol Mapping” to associate media-type keywords with a selected protocol (HTTP, FTP, WS, CoAP, etc.) • http://g.mamund.com/class-sked
  • 45. Near Term – Focusing on networks • Most implementations are stand-alone, one- off models. • We treat the Web as a sea filled with islands, each one only barely aware of the others.
  • 46. “The WWW is fundamentally a distributed hypermedia application.” Richard Taylor (2010)
  • 47. Near Term – Focusing on networks • The Web, biology, & social communities exhibit properties of a “scale-free” network • Barabási-Albert model for “preferential attachment” (1999)
  • 48. Near Term • Lower entropy in messages • Reduce protocol dependence • Treat the network as the application
  • 49. And there are some hard things, too.
  • 50. Futures No more central control, adaptation through variation, competing for resources
  • 51. Futures – No more central control • If the WWW is the application, where is the CPU? The storage? The program? • Cellular Automata (Ulam & Von Neumann, 1940s) • Conway’s Game of Life (1970s)
  • 52. Futures – No more central control • Cellular automata are discrete, abstract computational systems • In cellular automata information appears as statistical probabilities. • See Wolfram’s Atlas http://atlas.wolfram.com/01/01/
  • 53. Futures – No more central control • Basic principles for automata o Information takes the form of statistics and patterns across the system o Information is communicated via sampling o There exists some level of random behavior o Rely on fine-grained architecture, large numbers of simple elements.
  • 54. Futures – No more central control • In “Future Web” we will create discrete, abstract programs and they will interact across the network. • “What gets done on the ‘net stays on the ‘net.”
  • 55. Futures – Adaptation via variation • Machines will need to adapt to conditions, learn and pass on traits. • Learning happens via many passes and survival of the ‘most fit’ for the task. • “Robby” and the soda cans o Start w/ 1xxx random attempts o Score highest 2, splice o Add random mutation o Repeat • http://g.mamund.com/robby
  • 56. How can we model adaptation on the Web?
  • 57. Futures – Competing for resources • With Robby – there is a “score-keeper” for the entire system. • On the Web there is no score-keeper. • In living systems, ‘scoring’ is done through competing for limited resources.
  • 58. Futures – Competing for resources • In “Future Web” programs may compete for scarce resources such as memory, storage, cycles. • RBNs (Random Boolean Networks) offer a way to “keep score” without central control. (Kauffman, 1969). • Uses attractors o Fixed o Oscillating o Random
  • 59. How can we model competition on the Web?
  • 60. Summary Time to head back toward shore…
  • 61. Summary • Information theory, complex biological systems, hypermedia and the Web all share some similar properties
  • 62. Summary • However, our current efforts ignore these features and contain a high degree of entropy, coupling, and lack interdependence.
  • 63. Summary • We can start today by creating low-entropy machine-oriented messages, decouple from network protocols, and treat the network as a single application space.
  • 64. Summary • In the future we’ll need to give up central control, we’ll build discrete automata, and we’ll create a network where variation and competition are possible.
  • 65. However, in order to accomplish all that…
  • 67. We must be willing to lose sight of the shore.
  • 68. Autonomous Agents on the Web: Beyond Linking and Meaning Mike Amundsen Principal API Architect Layer 7 Technologies @mamund
  • 69. Image Credits (1) • http://th04.deviantart.net/fs71/PRE/f/2012/082/1/a/sunrise_at_sea_by_rumorvolat-d4too36.jpg • http://www.pynchon.pomona.edu/entropy/demon.gif • http://www.scholarpedia.org/w/images/thumb/e/e2/Timesarrowfig2.jpg/400px-Timesarrowfig2.jpg • http://upload.wikimedia.org/wikipedia/en/e/e5/Shannonmouse.PNG • http://upload.wikimedia.org/wikipedia/commons/thumb/7/7b/Lego_Turing_Machine.jpg/220px- Lego_Turing_Machine.jpg • http://notyourmomsfom.files.wordpress.com/2013/04/godel.jpg • http://en.wikipedia.org/wiki/File:Nobili_Pesavento_2reps.png • http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_derivation.gif • http://jasss.soc.surrey.ac.uk/13/2/reviews/3.gif • http://www.let.leidenuniv.nl/history/ivh/www_proposal.gif • http://www.biostat.wisc.edu/~cdewey/mercator/cliques.png • http://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Splicing_overview.jpg/220px- Splicing_overview.jpg • http://www.holon.se/folke/kurs/Distans/Ekofys/fysbas/LOT/exergy_tube.jpg • http://ic.pics.livejournal.com/rose_griffes/11821574/185555/185555_600.jpg • http://gigaom2.files.wordpress.com/2012/06/iphoneapps.jpg • http://rubytrain.files.wordpress.com/2011/12/http.jpg
  • 70. Image Credits (2) • http://en.wikipedia.org/wiki/File:Kelvin-1200-scale1000.jpg • http://shamangene.com/BLOG/wp-content/uploads/2012/09/mayan_calendar1.jpg • http://shamangene.com/BLOG/wp-content/uploads/2012/09/mayan_calendar1.jpg • http://www.artdes.monash.edu.au/design/assets/design_courses_communication.jpg • http://images.fineartamerica.com/images-medium-large/hear-no-evil--see-no-evil--speak-no-evil- william-meemken.jpg • http://etutorials.org/shared/images/tutorials/tutorial_37/02fig03.gif • http://walyou.com/wp-content/uploads//2011/01/sewer-maintenance-machine.jpg • http://blog.luxuryproperty.com/wp-content/uploads/image/dubai/The-World-islands-Dubai.jpg • http://www.ics.uci.edu/~taylor/pictures/Richard%20Taylor.jpg • http://www.network- science.org/fig_complex_networks_powerlaw_scalefree_node_degree_distribution_large.png • http://upload.wikimedia.org/wikipedia/commons/e/e5/Gospers_glider_gun.gif • http://atlas.wolfram.com/01/01/ • http://manwithoutqualities.files.wordpress.com/2010/02/swarming.jpg • http://plato.stanford.edu/entries/cellular-automata/fig5.jpg • http://www.styryx.com/img/itech/program/ai/genetic/robby/assignment.jpg • http://www.historyrv.com/blog/wp-content/uploads/2011/12/natural_selection.png
  • 71. Image Credits (3) • http://steve.files.wordpress.com/2006/03/Matrix%20tut%202.jpg • https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/Julia_immediate_basin_1_3.png/240 px-Julia_immediate_basin_1_3.png • http://www.dichotomistic.com/images/scale-free.gif • http://www.askamathematician.com/wp-content/uploads/2011/12/pip6_0747.jpg.jpg • http://www.webnotwar.ca/wp-content/uploads/2011/02/semantic-web.png • http://images4.alphacoders.com/262/262524.jpg