Artificial life - Quiz
A) Neural nets or derivatives B) First principles C) Cellular automata D) Physical ontology
A) Natural selection B) Learning C) Evolutionary hardware D) Economic modeling
A) White-box B) Transparent-box C) Black-box D) Grey-box
A) Axiomatic B) Mechanistic C) First-principles D) Phenomenological
A) Transparent walls B) Hidden mechanisms C) Ignoring internal structure D) Non-transparent nature
A) They show first principles B) They allow micro-level visibility C) They are non-mechanistic D) They reveal subsystem interactions
A) It is automatically inferred B) It is derived from axioms C) It is ignored D) It is highlighted
A) Grey-box B) White-box C) Black-box D) Phenomenological
A) Because of their non-transparent nature B) Because they ignore first principles C) Because they only use cellular automata D) Because they are too simple
A) Black-box mathematical methods B) A priori basic knowledge C) Non-deterministic logic D) Arbitrary logical rules
A) Arbitrary logical cellular automata B) Deterministic logical cellular automata C) Black-box methods D) Phenomenological derivation
A) Neural net training B) Physical ontology C) Randomized logic D) Phenomenological structure
A) Black-box statistical analysis B) Randomized phenomenological estimation C) Non-mechanistic data fitting D) Automatic hyper-logical inference
A) Hide internal structures B) Prioritize phenomenological results C) Avoid logical systems D) Derive concrete mechanistic knowledge
A) Lack of clear rules B) Reliance on black-box methods C) Use of hidden variables D) Formulation of an intrinsic axiomatic system
A) The model becomes perfectly accurate B) The model reveals hidden interactions C) The model may have weak relevance D) The model becomes a white-box model
A) Baldwin effect B) Butterfly effect C) Parallelization effect D) Mutation effect
A) Natural language B) Fixed parameter sets C) Turing complete computer program D) Machine learning weights
A) Random changes to the code B) Environmental triggers C) Manual editing by the user D) Pre-programmed evolution paths
A) Neuroevolution B) Raw processing power C) User creation and accessibility D) Turing completeness
A) With fully autonomous code B) Through module assembly C) With pre-defined and fixed behaviors D) Using Turing complete languages
A) Vladimir Georgievich Red'ko B) Christopher Langton C) Alan Turing D) John von Neumann
A) 1986 B) 1985 C) 1990 D) 1987
A) New York, New York B) Los Alamos, New Mexico C) Palo Alto, California D) Cambridge, Massachusetts
A) Surface modeling B) Simulation theories C) Software D) Social structures
A) Heuristic algorithms B) Hardware C) Hybrid systems D) Humanoid robotics
A) Weather patterns B) Water-based systems C) Biochemistry D) Wetware computing
A) Vladimir Georgievich Red'ko B) IEEE Symposium Committee C) Charles Darwin D) Christopher Langton
A) OEV B) EVE C) OPE D) OEE
A) Creating a stable equilibrium B) Achieving Open-Ended Evolution C) Perfecting biomimicry in art D) Replicating human intelligence
A) Hardware reliability B) Biochemical synthesis C) Open-Ended Evolution D) Collective system properties
A) Computer software B) Synthetic DNA C) Minimal cells D) Robots
A) Robotics B) Synthetic biology C) Computer science D) Mechanical engineering
A) Wetware B) Software C) Middleware D) Hardware
A) Salmonella B) Bacillus subtilis C) Mycoplasma laboratorium D) Escherichia coli
A) 63 B) 60 C) 61 D) 59
A) 30 B) 25 C) 20 D) 15
A) Emotional intelligence B) Digital communication C) Photosynthesis D) Kinematic self-replication
A) Bottom up B) Middle out C) Top down D) Randomized
A) Using only hardware B) From the top down C) Exclusively through mathematical simulation D) From the bottom up
A) Mario Bunge B) Douglas Blackiston C) John Maynard Smith D) Sam Kriegman
A) Strong artificial life B) Weak artificial life C) Synthetic DNA research D) Robotics |
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