A Tale of Two Animats: What does it take to have goals?
This addresses a foundational philosophical problem in AI and cognitive science about intrinsic goals and autonomy, but it is incremental as it builds on existing evolutionary and neural network frameworks.
The paper tackled the problem of what it takes for a system to have goals by comparing two types of evolved neural networks in a simple environment, finding that only the integrated network formed a causally autonomous entity, suggesting that meaningful goals depend on understanding the system's nature rather than its actions.
What does it take for a system, biological or not, to have goals? Here, this question is approached in the context of in silico artificial evolution. By examining the informational and causal properties of artificial organisms ('animats') controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for intrinsic information, autonomy, and meaning. The focus lies on comparing two types of Markov Brains that evolved in the same simple environment: one with purely feedforward connections between its elements, the other with an integrated set of elements that causally constrain each other. While both types of brains 'process' information about their environment and are equally fit, only the integrated one forms a causally autonomous entity above a background of external influences. This suggests that to assess whether goals are meaningful for a system itself, it is important to understand what the system is, rather than what it does.