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