Project details
Developing a Modular Framework for Motivation Systems in AGI Architectures Based on Integrated Multi-Task Agent Architecture with Affect-Like Guided Behavior
Executive Summary
This proposal aims to develop a modular and extensible framework for integrating various motivational systems into Artificial General Intelligence (AGI) architectures, supporting both human-like and alien digital intelligences. Building upon the “Integrated Multi-Task Agent Architecture with Affect-Like Guided Behavior” by James B. Worth and Mei Si, we propose to extend and generalize the existing architecture by incorporating affective agents and meta-control mechanisms to drive the motivational needs of the Hyperon AGI framework. The current proposal involves applying a currently functional meta-control algorithm into the Hyperon framework to enhance its capabilities. By integrating models inspired by human emotional and cognitive systems alongside meta-control systems that monitor and guide agent behavior, we enable dynamic adjustment of motivational priorities based on internal states and external inputs. The project includes conceptual and mathematical specifications of the framework and a limited-scope software prototype demonstrating its functionality in use cases like chatbot systems and virtual agents in metaverse environments such as Sophiaverse.
Introduction and Background
Context and Motivation
The SingularityNET Foundation, in collaboration with partners like the OpenCog Foundation and TrueAGI, is advancing scalable AGI systems through the Hyperon AGI framework and the PRIMUS cognitive architecture. A crucial aspect of AGI development is the integration of robust motivational systems that ensure adaptability, ethical alignment, and scalability in dynamic environments.
Existing Work
The “Integrated Multi-Task Agent Architecture with Affect-Like Guided Behavior” introduces a limbic-system-inspired design for adaptive multi-task learning and execution. Further advancements have enhanced reasoning capabilities, memory architectures, and integrations with external systems, enabling complex decision-making and learning.
Proposal Overview
We propose a modular framework for motivational systems in AGI architectures, focusing on:
• Affective Reinforcement Learning (RL) Agents: Modeling emotional and arousal states to influence goal selection and behavior.
• Meta-Control Mechanisms: Monitoring and guiding agent actions to align with objectives and ethical guidelines.
Objectives
1. Develop a Modular Framework: Integrate affective RL agents and meta-control mechanisms to manage motivational priorities.
2. Support Diverse Intelligences: Adapt to both human-like and alien AGI systems.
3. Dynamic Motivation: Adjust priorities based on internal states and external stimuli.
4. Ensure Ethical Alignment: Embed ethical compliance into core functionalities.
5. Scalability: Ensure the framework integrates seamlessly with large AGI systems like Hyperon.
6. Demonstrate Use Cases: Validate through applications in chatbots and virtual agents.
Proposed Framework
Key Features
• Affective Systems: Emotive models influencing decision-making.
• Meta-Control Mechanisms: Monitoring ethical compliance and dynamic adjustment of motivations.
• Goal Management: Selecting goals based on affective states and meta-control.
• Attention Mechanisms: Focusing on relevant features guided by affective and meta-control inputs.
• Ethical Alignment: Ensuring actions align with human values.
• Scalability: Integrating efficiently with Hyperon components (ECAN, DAS, MeTTa).
Methodology
Phase 1: Conceptual Design
• Develop mathematical models for affective RL agents and meta-control.
• Define components and their integration within Hyperon.
Phase 2: Prototype Development
• Create a limited-scope prototype demonstrating functionality in controlled use cases.
Phase 3: Generalization and Extension
• Evaluate scalability and refine ethical mechanisms.
Use Cases
Customer Support Chatbot
• Empathetic interactions with customers while maintaining ethical standards and preventing misuse.
HR Support Chatbot
• Internal HR assistance ensuring confidentiality and policy compliance.
Scalability and Ethical Alignment
• Ensure adaptability to large-scale systems.
• Embed real-time ethical evaluations in agent behavior.
Team Composition
• Project Lead: Bren Worth, CTO of Substrate AI/SubGen AI, with 20+ years of experience in machine learning, cognitive architectures, and motivational systems. Holder of multiple patents in AGI-related innovations.
• AI Researcher: Mei Si, Associate Professor specializing in reinforcement learning and affective computing.
• Integration Engineer: Graduate student with expertise in Hyperon framework components.
Timeline
1. Month 1: Conceptual framework development.
2. Month 2: Prototype implementation and testing.
3. Month 3: Agent testing and validation.
4. Month 4: Analysis and reporting.
Budget Estimate
• Personnel, software, testing platforms, and computing resources estimated at $30,000.
Evaluation Criteria
• Alignment with project objectives.
• Ethical compliance and scalability.
• Demonstrated competence in related research.
Final Review
This project will create a novel motivational framework for AGI systems, enabling dynamic, ethical, and scalable intelligence for diverse applications. The framework will contribute significantly to the Hyperon AGI framework and PRIMUS cognitive architecture, advancing the development of intelligent systems aligned with human values.
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