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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. 

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. 

We evaluate the benefits of intention perception, the ability of an agent to perceive the intentions and plans of others, in improving a software agent's survival likelihood in a simulated virtual environment. To model intention perception, we set up a multi-agent predator and prey model, where the prey agents search for food and the predator agents seek to eat the prey. We then analyze the difference in average survival rates between prey with intention perception-knowledge of which predators are targeting them-and those without. We find that intention perception provides significant survival advantages in almost all cases tested, agreeing with other recent studies investigating intention perception in adversarial situations and environmental danger assessment. 

Conjecturing that an agent's ability to perceive the intentions of others can increase its chances of survival, we introduce a simple game, the Hero's Dilemma, which simulates interactions between two virtual agents to investigate whether an agent's ability to detect the intentional stance of a second agent provides a measurable survival advantage. We test whether agents able to make decisions based on the perceived intention of an adversarial agent have advantages over agents without such perception, but who instead rely on a variety of different game-playing strategies. In the game, an agent must decide whether to remain hidden or attack an often more powerful agent based on the perceived intention of the other agent. We compare the survival rates of agents with and without intention perception, and find that intention perception provides significant survival advantages and is the most successful strategy in the majority of situations tested.