Design for a Darwinian Brain: Part 2. Cognitive Architecture

TL;DR

This paper proposes a novel cognitive architecture capable of generating an unlimited range of behaviors through stochastic exploration, with a formalized fitness measure for self-generated games and a modular, evolvable behavior language, demonstrated on a humanoid robot.

Contribution

It introduces a formal definition of fitness for self-generated games and a systematic, compositional behavior language within a cognitive architecture for open-ended learning.

Findings

Architecture can specify an unlimited range of behaviors.

Self-generated games are evaluated with a formal fitness measure.

Implementation on a humanoid robot demonstrates practical viability.

Abstract

The accumulation of adaptations in an open-ended manner during lifetime learning is a holy grail in reinforcement learning, intrinsic motivation, artificial curiosity, and developmental robotics. We present a specification for a cognitive architecture that is capable of specifying an unlimited range of behaviors. We then give examples of how it can stochastically explore an interesting space of adjacent possible behaviors. There are two main novelties; the first is a proper definition of the fitness of self-generated games such that interesting games are expected to evolve. The second is a modular and evolvable behavior language that has systematicity, productivity, and compositionality, i.e. it is a physical symbol system. A part of the architecture has already been implemented on a humanoid robot.