Advanced Cognitive Architecture for Robot Self-Consciousness
How can robots develop self-consciousness? This paper by Antonio Chella, Marcello Frixione, and Salvatore Gaglio introduces a cognitive architecture enabling robots to reflect on their own perceptions, actions, and inner states, facilitating self-awareness and introspection.
A Cognitive Architecture for Robot Self-Consciousness, authored by Chella, Frixione, and Gaglio, presents a model built on three computational areas—subconceptual, conceptual, and linguistic—integrated to support higher-order perceptions and dynamic self-representation.
Key Highlights
- Higher-Order Perceptions: Distinguishes first-order perceptions (immediate external input) from higher-order perceptions (introspection over internal states and past experiences).
- Conceptual Spaces: Utilizes a dynamic conceptual space to represent actions, objects, and relationships in a structured manner, supporting complex cognitive reasoning.
- Knoxel Framework: Introduces knoxels, points in the conceptual space, to represent both static and dynamic scenes, enabling the robot to model its environment and self-awareness hierarchically.
- Linguistic Integration: Employs a symbolic knowledge base to link conceptual representations with semantic meaning, facilitating reasoning and introspection.
Connection to ACM
The Artificial Consciousness Module (ACM) aligns with this study by:
- Hierarchical Modeling: Leveraging the integration of lower-level sensory inputs with higher-order introspective reasoning.
- Dynamic Representations: Adopting conceptual spaces to simulate adaptive and reflective AI systems.
- Self-Reflective Agents: Incorporating mechanisms for modeling self-awareness and higher-order reasoning in virtual environments.
For a detailed exploration of the cognitive architecture and its applications, access the full paper here.
