Generating the Gopher's Grounds: Form, Function, Order, and Alignment.

Previous work has shown that artificial agents with the ability to discern function from structure (intention perception) in simple combinatorial machines possess a survival advantage over those that cannot. We seek to examine the strength of the relationship between structure and function in these cases. To do so, we use genetic algorithms to generate simple combinatorial machines (in this case, traps for artificial gophers). Specifically, we generate traps both with and without structure and function, and examine the correlation between trap coherence and lethality, the capacity of genetic algorithms to generate lethal and coherent traps, and the information resources necessary for genetic algorithms to create traps with specified traits. We then use the traps generated by the genetic algorithms to see if artificial agents with intention perception still possess a survival advantage over those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when faced with traps generated by a genetic algorithm.