Authors:
Anshul Kamath
1
;
Jiayi Zhao
2
;
Nick Grisanti
1
and
George D. Montañez
1
Affiliations:
1
AMISTAD Lab, Department of Computer Science, Harvey Mudd College, Claremont, CA, U.S.A.
;
2
Department of Computer Science, Pomona College, Claremont, CA, U.S.A.
Keyword(s):
Structure and Function, Agents, Genetic Algorithms.
Abstract:
Does structure dictate function and can function be reliably inferred from structure? Previous work has shown that an artificial agent’s ability to detect function (e.g., lethality) from structure (e.g., the coherence of traps) can confer measurable survival advantages. We explore the link between structure and function in simple combinatorial machines, using genetic algorithms to generate traps with structure (coherence) and no function (no lethality), generate traps with function and no structure, and generate traps with both structure and function. We explore the characteristics of the algorithmically generated traps, examine the genetic algorithms’ ability to produce structure, function, and their combination, and investigate what resources are needed for the genetic algorithms to reliably succeed at these tasks. We find that producing lethality (function) is easier than producing coherence (structure) and that optimizing for one does not reliably produce the other.