Project Overview
This research explores the development and application of AI-driven intelligent systems to enhance problem-solving efficiency and effectiveness in complex problem domains.
The study focuses on designing novel frameworks and algorithms that leverage multi-agent systems, and intelligent agents to address large-scale and computationally challenging problems. One key area of interest is Distributed Constraint Satisfaction Problems (DCSPs), which model real-world challenges such as train scheduling. The research will develop AI-based solutions to optimize performance in dynamic environments, ensuring improved accuracy, efficiency, and resource utilization.
The project will introduce new algorithms for solving DCSPs using a combination of centralized and distributed multi-agent approaches, heuristic searches, and structured agent interactions. To enhance scalability, the research will address computational complexity and communication overhead, reducing time costs and memory usage to enable the application of these methods to large-scale problems.
By evaluating and benchmarking the proposed intelligent systems against traditional approaches, this research will contribute to the development of more efficient, scalable, and practical AI-driven problem-solving methods.
Project Aims
• Develop AI-driven intelligent systems to enhance problem-solving efficiency and effectiveness.
• Design novel frameworks and algorithms for solving complex problems like DCSPs.
• Model real-world challenges such as train scheduling using AI techniques.
• Evaluate and benchmark AI solutions against traditional methods for accuracy and efficiency.
• Reduce computational complexity to improve scalability for large-scale problem-solving.
Period: January 2024-December 2027
Impact: This research enhances AI-driven problem-solving by improving efficiency, scalability, and real-world applicability. It reduces computational costs, making AI solutions feasible for large-scale challenges.
Project Team
Collaborators
Swansea University