The Hard Problem of Robot Consciousness: ACM Perspectives
What are the philosophical and functional challenges of consciousness in robots? This paper by Bruce J. MacLennan examines the role of consciousness in autonomous robots, discussing the “less hard” problems like self-awareness and intrinsic intentionality, and delving into the “Hard Problem” of subjective experience.
Consciousness in Robots: The Hard Problem and Some Less Hard Problems, authored by Bruce J. MacLennan, provides a comprehensive framework for addressing consciousness in robots through evolutionary psychology, cognitive functions, and the theory of protophenomena.
Key Highlights
- The Hard Problem: Explores the challenge of reconciling physical processes with subjective awareness, highlighting the epistemological barriers to studying consciousness scientifically.
- Intrinsic Intentionality: Differentiates between derived intentionality (e.g., data in computer systems) and intrinsic intentionality, which arises from representations meaningful to the agent itself.
- Functions of Consciousness: Discusses practical roles of consciousness in autonomous robots, such as deliberate control, self-awareness, and metacognition, inspired by evolutionary adaptations in animals.
- Protophenomena Theory: Introduces the concept of protophenomena as theoretical entities underlying conscious experiences, emphasizing their relevance to both human and robotic consciousness.
Connection to ACM
The Artificial Consciousness Module (ACM) aligns with this study by:
- Modeling Consciousness: Incorporating concepts of intrinsic intentionality and protophenomena to simulate conscious-like processes in AI agents.
- Adaptive Functions: Utilizing insights into deliberate control and metacognition to enhance adaptability and decision-making in complex environments.
- Philosophical Frameworks: Drawing from the exploration of the Hard Problem to refine ethical and theoretical approaches to artificial consciousness.
For a detailed exploration of these ideas and their implications for robotics and AI, access the full paper here.
