Evan A. Krause

An Overview of the Distributed Integrated Cognition Affect and Reflection DIARC Architecture

DIARC has been under development for over 15 years. Different from other cognitive architectures like SOAR or ACT-R, DIARC is an intrinsically component-based distributed architecture scheme that can be instantiated in many different ways. Moreover, DIARC has several distinguishing features, such as affect processing and deep natural language integration, is open-world and multi-agent enabled, and allows for “one-shot instruction-based learning” of new percepts, actions, concepts, rules, and norms. In this chapter, we will present an overview of the DIARC architecture and compare it to classical cognitive architectures. After laying out the theoretical foundations, we specifically focus on the action, vision, and natural language subsystems. We then give two examples of DIARC configurations for “one-shot learning” and “component-sharing”. We also briefly mention different use cases of DIARC, in particular, for autonomous robots in human-robot interaction experiments and for building cognitive models.

Recursive Spoken Instruction-Based One-Shot Object and Action Learning

Learning new knowledge from single instructions and being able to apply it immediately is highly desirable for artificial agents. We provide the first demonstration of spoken instruction-based oneshot object and action learning in a cognitive robotic architecture and briefly discuss the architectural modifications required to enable such fast learning, demonstrating the new capabilities on a fully autonomous robot.