ACM Project - Artificial Consciousness Research Developing Artificial Consciousness Through Emotional Learning of AI systems
Zae Project on GitHub

From Focused Thoughts to Reveries: ACM's Path to Self-Generated Experiences

How can robots develop an “inner world” for consciousness? This paper by Christian Balkenius, Trond A. Tjøstheim, Birger Johansson, and Peter Gärdenfors provides a roadmap for implementing a computational memory system that enables robots to simulate experiences and learn through cognitive processes.

Emotion Expression in Robots Through Artificial Consciousness

How can robots effectively express emotions? This paper by Atsushi Ogiso and colleagues presents a system where artificial consciousness enables a humanoid robot to display emotions through realistic facial expressions, enhancing human-robot communication.

Emotions in Robot Psychology: Implications for ACM Development

How can robots simulate and use emotions effectively? This paper by Verena Nitsch and Martin Popp investigates the role of emotions in robotics, focusing on their impact on human-robot interaction, cognitive processing, and behavioral adaptability.

Emotional Learning in Developing Robots: ACM Applications

How do emotions contribute to robotic learning? This paper by Riccardo Manzotti, Giorgio Metta, and Giulio Sandini highlights the cognitive and phenomenological roles of emotions in robotic development, demonstrating their potential as reinforcement mechanisms for decision-making and behavior adaptation.

Differentiating AI and Consciousness: IIT Perspectives for ACM

What differentiates artificial intelligence from artificial consciousness? This paper by Graham Findlay and colleagues explores this question using Integrated Information Theory (IIT), a leading framework for evaluating consciousness.

Developing Self-Aware Robots: Kawamura's Framework and ACM Integration

How can robots achieve a sense of self? This paper by Kazuhiko Kawamura and colleagues outlines the development of a robot with self-awareness through a multiagent-based cognitive architecture and adaptive working memory systems.

Self-Conscious Agent Design: Chella's Approach for Robotics

Robots capable of adapting to unstructured environments require innovative methodologies. This paper, authored by Antonio Chella, Massimo Cossentino, Valeria Seidita, and Calogera Tona, explores the design of self-conscious robotic systems through the perception loop, fostering autonomous learning and adaptive decision-making.

Credition and Belief Formation: Implications for ACM Development

How does the brain shape beliefs unconsciously? This paper, authored by Hans-Ferdinand Angel and Rüdiger J. Seitz, introduces “credition” as a central brain function, interlinking cognitive and emotional processes to drive human behavior and worldview formation.

Natural and Artificial Consciousness: MacLennan's Framework

What role can consciousness play in autonomous robots? This paper by Bruce J. MacLennan explores functions of consciousness, including deliberate control, self-awareness, and metacognition, while addressing the “Hard Problem” of subjective awareness in robots from the perspective of protophenomena theory.

The Hard Problem of Robot Consciousness: ACM Perspectives

What are the philosophical and functional challenges of consciousness in robots? 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.

Zae Project on GitHub