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  • 1. Embodied Artificial Evolution: the Next BIG Thing? Gusz Eiben VU University Amsterdam
    • Popular version in the
    • TED talk: "Tech Kangaroos: Evolution at Work ”
    • Search for “Eiben” on youtube
  • 2. Past, Present, Future
    • 19th century:
    • evolution is a theory , an explanatory concept that helps us understand things
    • 20 th century:
    • evolution is a tool , an active, creative concept that helps us produce things (solutions for problems) in digital spaces
    • 21 st century:
    • evolution is a tool , an active, creative concept that helps us produce things (solutions for problems) in physical spaces
  • 3. WETWARE HARDWARE EVOLUTION EVOLUTION Embodied Artificial Evolution SOFTWARE Biosphere Evolutionary Computing
  • 4. Evolutionary Computing lessons
    • +
    • Evolution can solve problems we don ’t fully understand and cannot clearly specify
    • Evolution can cope with changing situations
    • Evolution can come up with original, unexpected solutions (that can be reverse-engineered)
    • Designing good EAs can be difficult (representation, operators, parameters)
    • No good theory
    • Could take too long (can change with new hardware)
  • 5. Evolutionary computing and embodiment
    • Evolution in digital space, result in digital space
    • e.g., time tables, consumer models, robot controllers
    • EVOLUTIONARY OPTIMIZATION
    • Evolution in digital space, result in physical space
    • e.g., jet nozzle, satellite boom, P Bentley ’s coffee table
    • EVOLUTIONARY DESIGN
    • Evolution in physical space, result in physical space
    • EMBODIED ARTIFICIAL EVOLUTION
  • 6. Embodied Artificial Evolution
    • Old: Evolution of code (blueprint)
    • New: Evolution things
    • Takes place in physical objects
    • Reproduction = real birth = new object made, survivor selection = real death = object deleted
    • Fitness is environmental or hybrid environmental and task-based
    • Reproduction and survivor selection are decoupled, autonomous decisions, distributed management
  • 7. Application examples
    • Mars explorers, artificial pets, robot companions, …
    • Functional organisms, medical nano-robots, personal virus scanners, …
    • Personal replicators (networked, evolutionary, Internet of Things )
  • 8. Active vs. passive entities
    • Two kinds of artefatcs
      • Active: needs controller to govern its activities (e.g., robot)
      • Passive: needs no controller (e.g., smartphone cover)
    • In biological terms
      • Active: needs body + mind
      • Passive: only body
  • 9.
    • Different approaches, under different umbrellas
    Physical medium, embodiment ? Bottom-up, chemistry Top-down, biology Mechatronics, plastics
  • 10. A new game for “passive” stuff Manufacturing Testing (Re)design Embryonic development Evaluation Selection Reproduction propagule blueprint
  • 11. A new game for “active” stuff
    • Robots
    • very small to big
    • programmable
    • controllable
    • sensors/actuators/controllers
    • Cells
    • very small
    • self-replicating
    • self-repairing
    • self- …
    WETWARE HARDWARE Sweet spot
  • 12. Grand challenges
    • Body type
    • Reproduction - how to start
    • Kill switch - how to stop
    • Evolvability and evolution speed
    • Process control and methodology
    • Co-evolution of body & mind
    • Lifetime learning (self-awareness, mind must match body)
  • 13. Closing remarks
    • Evolution in the real world is different – matter matters
    • Computation in this new paradigm is different
      • Non-von-Neumannian architecture for information processing/computing
      • Redefines ICT, e.g., “ program ” , “ programming ” , software engineering, testing, validation, ect.
    • Big questions are implied:
      • Scientific (fundamental issues regarding evolution, notion of Life)
      • Technological (feasibility)
      • Applicability (remember IBM in the 1940ies)
      • Desirability (ethics, think of GMOs)
    • Different communities, unifying vision / umbrella needed
    • Next BIG thing ?