Domain-Independent Lifelong Problem Solving Through Distributed ALife Actors.

Area of Science:

• Artificial Life
• Computational Intelligence
• Problem Solving Systems

Background:

• Traditional problem-solving systems often lack adaptability and require extensive tuning for new domains.
• Domain-specific solutions limit scalability and generalizability in artificial intelligence.
• Integrating principles from Artificial Life offers a novel approach to creating more robust AI.

Purpose of the Study:

• Introduce DIAS, a domain-independent problem-solving system leveraging Artificial Life principles.
• Evaluate DIAS's performance across various problem domains and complexities.
• Demonstrate DIAS's capabilities in lifelong learning and adaptability.

Main Methods:

• DIAS utilizes a spatial medium to represent domain input/output dimensions.
• A population of actors collectively solves problems, with each actor perceiving a limited view.
• The system's domain-independent nature allows implementation with diverse actor types.

Main Results:

• DIAS successfully solves problems of varying dimensionality and complexity.
• The system demonstrates lifelong learning, rapidly adapting to runtime domain changes.
• DIAS shows superior performance compared to standard, non-collective approaches.
• No hyperparameter tuning was required for new problem domains.

Conclusions:

• DIAS offers a scalable, general, and adaptive problem-solving framework.
• Artificial Life principles provide a viable foundation for advanced AI systems.
• The collective, adaptive nature of DIAS enhances its robustness and efficiency.