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)
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?
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
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 Face
How Users Breed Images
• Next: Select parent(s) – Which do you like?
How Users Breed Images
• Next: Select parent(s) – Which do you like?
Press “Evolve”after selectingparents
How Users Breed Images
• Next: The offspring (next generation) appear
Parent
How Users Breed Images
• 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
Breeding from Scratch
Breeding from Scratch
Parent
Breeding from Scratch
Parent
Breeding from Scratch
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)
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:
How Did I Find the Car?
• But then, the alien’s eyes descended and turned into wheels
How Did I Find the Car?
• 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
How Did I Find the 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!
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
The Limits of Human Innovation
• 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!
The Limits of Human Innovation
• 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
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
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
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
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]