Domain Specific Heuristic

Domain-specific heuristics are rules of thumb tailored to particular problem types to improve the efficiency of algorithms like search and planning. Current research focuses on learning these heuristics automatically using machine learning techniques, such as graph neural networks and inductive logic programming, often integrated with frameworks like Monte Carlo Tree Search or Answer Set Programming. This work aims to improve the performance of various AI systems, from game playing agents to automated theorem provers, by leveraging problem-specific knowledge to overcome the limitations of general-purpose methods and enhance scalability to complex real-world problems.