Using Evolutionary Computation as a Creativity-Support Tool Tim ChabukUniversity of Maryland Jason LohnCarnegie Mellon University Derek LindenX5 Systems.

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Presentation transcript:

Using Evolutionary Computation as a Creativity-Support Tool Tim ChabukUniversity of Maryland Jason LohnCarnegie Mellon University Derek LindenX5 Systems Jim ReggiaUniversity of Maryland NSF CreativeIT Workshop, January 2009 Pilot

Example: Self-Replicating Machines artificial structures/systems that produce copy of themselves artificial structures/systems that produce copy of themselves why study? why study? - insight into biological replication - understanding origins of life - understanding principles/algorithms - potential applications A B B C t t+1 historically, most work done with cellular automata historically, most work done with cellular automata discrete space of cells, discrete time discrete space of cells, discrete time local, parallel computations local, parallel computations Rule set: A. B C.  B A. B C.  B etc. etc. Game of Life

Two Primary Classes of Replicators tapeTape control Construction control Construction arm Replica under construction John von Neumann (1950’s) - 29 state cells - universal constructor computer - complicated (~100k cells, 100k rules) Chris Langton (1980’s) - 8 state cells - self-replicating loop - simpler, implemented

Replication of Loop t = 80 t = 115t = 150 t = 6 t = 3 search for simplest, emergence from “primordial soup”, search for simplest, emergence from “primordial soup”, simultaneous tasks, etc. simultaneous tasks, etc.

A Half Century of Cellular Replicators hand-crafted rule sets restricted to these two broad classes Pan Z, Reggia J, Gao D. Properties of Self-Replicating Cellular Automata Systems Discovered Using Genetic Programming, Advances in Complex Systems, 10 (Suppl. 1), 2007, Question: Is automated discovery of novel self-replicating structures possible? Approach: - given an arbitrary initial configuration - use genetic programming to evolve needed rules

Evolved Replicators initial structureevolved rule tree GP ran quickly on standard PC but how do the resulting replicators work?

Resulting Replicator Behavior growth, separation of replicants fastest CA replicant ever reported t = 2 t = 1 t = 0

Another Initial Structure debris debris

larger, larger, repetitive repetitive components components  whole new family whole new family of self-replicating of self-replicating configurations configurations

Other Structures? Evolves same rule table as that created previously by people?  Replicator Factory: - creates rules for arbitrary configurations to replicate - creates rules for arbitrary configurations to replicate - rule sets are parsimonious and fast - rule sets are parsimonious and fast - novel replication process relative to past manual approaches - novel replication process relative to past manual approaches - used to study properties of replicators - used to study properties of replicators

Pilot: Causally-Guided Evolutionary Creativity causality in human creativity causality in human creativity Goal: guide EC in part using problem specific causal relations Goal: guide EC in part using problem specific causal relations Process: 1. specify problem-specific causal relations Process: 1. specify problem-specific causal relations 2. integrate causal influences on evolutionary process 2. integrate causal influences on evolutionary process 3. validate approach 3. validate approach Progress to date: Progress to date: - antenna array design as target task - antenna array design as target task - derivation of causal relations - derivation of causal relations - causal reasoning algorithm - causal reasoning algorithm - integrated with evolutionary process - integrated with evolutionary process - initial experimentation - initial experimentation