how letting go of goals helps creativity and discovery kenneth o. stanley evolutionary complexity...

How Letting Go of Goals Helps Creativity and Discovery

Kenneth O. StanleyEvolutionary Complexity Research Group

University of Central Florida School of EECS<kstanley>@eecs.ucf.edu

In Collaboration with

Joel Lehman, Sebastian Risi, Jimmy Secretan, Nick Beato, Adam Campbell, David D'Ambrosio, Adelein Rodriguez, Jeremiah T. Folsom-Kovarik, Brian Woolley

A Sobering Message…

If you put your mind to it, you can accomplish anything

(Marty McFly in Back to the Future, 1985)

…with a Paradoxical Silver Lining

If you do not put your mind to it, you can accomplish anything

Seeking AI: Trying to Understand How Complexity Evolves

• 100 trillion connections in the human brain– The most complex structure known to exist

• How can Darwinian evolution produce such astronomical complexity?– We can investigate this question through

evolutionary computation (artificial evolution)– In the process, other fundamental principles

of innovation and discovery are uncovered– Evolution is a kind of search

Innovation, Creativity, and Discovery are Forms of Search

• We search the space of possible artifacts– All possible images– All possible three-dimensional morphologies– All possible combinations of words– All possible minds

• Most of the search space is desolate

• The gems are needles in a haystack

• The mind is a powerful search operator

Argument Preview

• Highly ambitious objectives ultimately block their own achievement

• The greatest human achievements are not the result of objective optimization

• All of search is cloaked in futility– Yet in this realization there is genuine

liberation– New opportunities will open for discoveries

that work in different ways

Picbreeder:A Microcosm for Innovation

• Website: http://picbreeder.org– Crowd-sourced picture-breeding online service

• Three years of operation• 7,500 evolved images• 500 users• Like Dawkins’ BioMorphs (from The Blind

Watchmaker, 1986) on steroids• The question: How do users discover the best

images in a vast and desolate space?

http://picbreeder.org/

How Users Breed Images

• First must decide where to start– From “scratch” : Random initial population– By “branching” : Offspring of existing image

Typical Start from Scratch Starting from a Butterfly


• First must decide where to start– From “scratch” : Random initial population– By “branching” : Offspring of existing image

Typical Start from Scratch Starting from a Face


• Next: Select parent(s) – Which do you like?


• Next: Select parent(s) – Which do you like?

Press “Evolve”after selectingparents


• Next: The offspring (next generation) appear

Parent


• Next: Repeat until satisfied and then Publish

Parent

Important: Everything Ultimately Derives from Scratch

• The beginning of evolution

• However, the images become more complex over generations

Breeding from Scratch


Parent


And so on…

Parent

The Result:Large, Growing Phylogenies

• Users build upon each other’s discoveries (30 users built this one)

Images Evolved by Picbreeder Users(All are 100% evolved: no retouching)

Picbreeder Image Representation• Images are represented

by compositional pattern-producing networks (CPPNs)

– A composition of simple functions

• A new node is sometimes introduced through mutation

• What kind of search space does this representation induce?

Gaussian

Sigmoid

Sine

Linear

Gaus

SinSig

Gaus

Gaus

SinSig

x y

HH SS BB

d

x

yf(x,y,d)= HSB

f

The Search Space is Desolate

• Almost every image looks like these– They have random weights and topologies:

Yet Picbreeder Users Found These…(All are 100% evolved: no retouching)

…and entire “species”…

…and these…(All are 100% evolved: no retouching)

…and these…How?(All are 100% evolved: no retouching)

Paradox: Images Cannot Be Re-evolved!• Pick an image• Make it an objective (i.e.

goal) for evolution on computer (NEAT)

• Run automated evolution• Output is terrible• This result

is universal – Why?

(best results from over 30,000 generations each)

(74 cumulative generations)

Why Is It Impossible to Re-discover Former Discoveries?

• You cannot find something on Picbreeder simply by looking for it

• Serendipity plays a powerful role– Yet if it is serendipity, then how can it happen

so often?

(best results from over 30,000 generations each)

The Story of the Car

• Would you expect to find a car in this space?

• I didn’t

• But then I found one

How Did I Find the Car?

• I was not looking for a car

• Rather, I chose to evolve the alien (ET) face to get more alien faces:


• But then, the alien’s eyes descended and turned into wheels


• The only way to find the car was by not looking for it

• Otherwise, I would never have selected the alien– It does not look like a car


• But I would not have evolved the alien either!• Someone else had to evolve it for me to make

my discovery

Most Top Images Have the Same Story

The stepping stones almost never resemble the final product

Moral: It Works Because There Is No Unified Goal

• The only way to find the needles in the haystack– …is by not collectively looking for them

• Users have conflicting goals and interests– Some explore with no goal– Some may have their own goals

• Yet the system as a whole has no unified objective– Every discovery is a potential stepping stone for

someone else

Stepping Stones• Steps in search space that lead to objective

• May be hard or impossible to identify a priori

– Especially for ambitious problems

• May not induce positive change in objective function

Objectives Can Be Deceptive

• The Chinese Finger Trap– What are the stepping stones?

Thought Experiment

• You have a Petri dish the size of the world– …and organisms equivalent to the very first

cells on Earth

• Your objective: Evolve human-level intelligence– You have 4 billion years

• What is your selection strategy?

Thought Experiment

• You have a Petri dish the size of the world– …and organisms equivalent to the very first

cells on Earth

• Your objective: Evolve human-level intelligence– You have 4 billion years

• What is your selection strategy?– Administer intelligence tests to single-celled

organisms!

Intelligence Does Not Resemble Its Stepping Stones

• The stepping stones:– Multicellularity– Bilateral Symmetry

• Yet they are essential to its discovery• Humans were only found because we are

not the objective– A proliferation of agnostic stepping-stones was

the necessary prerequisite– Natural evolution has no final objective

The Limits of Human Innovation

• Thought experiment: Good idea or not?– 5,000 years ago sequester all the greatest

minds to build a computer


• Thought experiment: Good idea or not?– 5,000 years ago sequester all the greatest

minds to build a computer• Bad idea!

– Vacuum tubes were not invented with computation in mind

– Electricity was not discovered with computation in mind

• Almost no prerequisite to any major invention was invented with that invention in mind!


• This realization touches many of our greatest enterprises– Artificial intelligence– Including personal goals (e.g. make $1M)

Something Is Wrong at the Heart of Search

• How else could it work?– Abandon our objectives!– Follow the gradient of interestingness

• Serendipitous discovery is not accidental

Algorithm: Abandon Objectives and Search for Novelty

• Can be hard to identify stepping stones a priori• Novelty = proxy for stepping stones

– Anything that does something different is a potential stepping stone

• Novelty is still based on information, just different information

• No final objective, just find new behaviors• Encounter solution although not looking for it!

Testing Novelty Search

• Start with an evolutionary algorithm– Evolve artificial neural networks to control

simulated robot behaviors– No fitness function– Instead, reward any behavior that is novel so far

• No concept of better or worse• No objective• Changes over evolution

Underlying Evolutionary Algorithm: NEAT

• NeuroEvolution of Augmenting Topologies– Well-established neuroevolution method– Proven in many control and decision domains

• Starts with minimal structures that “grow” through mutations

• With novelty search:– Only the novel reproduce

Novelty Search = Exhaustive Search?

• Not exactly, because NEAT induces an order

• Once simple behaviors are exhausted, novelty requires more complexity

– More novelty requires accumulating information

Experiment: Maze Navigation Domain

• Why maze navigation?– Easily visualized– Good model for deception (tuneable)

MazeNavigator

Maze Navigation Domain

• Three Experiments• Objective-based NEAT

– Fitness function: Distance to goal at end of trial

• NEAT with novelty search– Reward ending somewhere different

• NEAT with random selection

Results (Maze)• 40 runs for each method

– Objective-based NEAT: Only 3 successful runs

– Random selection: 4 successful– NEAT Novelty search: 39 out of 40 successful!

Results (Visualization)

Biped Walking

• Notoriously difficult to evolve

• Which produces walking more effectively?– Reward distance travelled (i.e. fitness)– Reward ending somewhere different (i.e. novelty)

Novelty Finds Significantly Better Gaits (50 runs)

Novelty search best: 13.7 meters Fitness-based best: 6.8 meters

(15 second trials)

Novelty and Fitness Walkers

Similar Outcomes in Many Domains

T-Mazes (2009/2010) Simulated Bee (2010)

Unenclosed Maze (2010)Two-point navigation (2010)

GP Domains (2010)

Door-locking logic(Goldsby and Cheng 2010)

Medium Maze (2008)

What Do Results Mean?

• Finding the objective is often more effective by not looking for it

• Will not always work to achieve defined goals

• There is futility at the heart of search– The lesson is the important part– Not that novelty search is a panacea

• However, greatness is possible if it is undefined

What Does It Mean for Ambitious Design Goals?

• We cannot realistically expect to achieve them by trying– Unless they are one stepping stone away – then

they come within reach

• Otherwise, we can achieve them by not trying– It is the combination of many minds with many

divergent interests that ultimately exploits a search space in the long-term, not any individual objectives

Liberation through Acceptance

• May be possible to capture the process of open-ended innovation through software– Collaborative interactive non-unified systems– Picbreeder is just an example

• Potential treasure-hunting systems– Clothing catalog, furniture catalog,

building catalog, automobile catalog– Software development– Science Funding– Web-based search

Deeper Meaning

• Search is at its most awesome when it has no objective– Natural evolution– Human Innovation– Picbreeder– Novelty search

• Not all the same but one unifying theme: no objective

Deeper Meaning

• We rarely try to emulate this form of search: instead we want to chain it

• Unleash the treasure-hunter– Avoid the temptation towards convergence

• It is in your interest - our interest - that some do not follow the path that you believe is right

To achieve our highest goals, we must be willing to abandon them

The Last Word

There remains one a priori fallacy or natural prejudice, the most deeply-rooted, perhaps, of all which we have enumerated: one which not only reigned supreme in the ancient world, but still possesses almost undisputed dominion over many of the most cultivated minds…This is, that the conditions of a phenomenon must, or at least probably will, resemble the phenomenon itself.

John Stewart Mill on the like-causes-like biasIn A System of Logic, Ratiocinative and Inductive, 1846

More information• My Homepage:

http://www.cs.ucf.edu/~kstanley • Novely Search Users Page:

http://eplex.cs.ucf.edu/noveltysearch/userspage/

• Evolutionary Complexity Research Group: http://eplex.cs.ucf.edu

• Picbreeder: http://picbreeder.org

• Email: [email protected]

http://www.cs.ucf.edu/~kstanley

http://eplex.cs.ucf.edu/noveltysearch/userspage/

http://eplex.cs.ucf.edu/

http://picbreeder.org/

mailto:[email protected]

how letting go of goals helps creativity and discovery kenneth o. stanley evolutionary complexity...

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