Backward Chaining

Backward chaining is a reasoning method that works backward from a goal to find the necessary conditions for its achievement, contrasting with forward chaining which starts from known facts. Current research focuses on improving the efficiency and accuracy of backward chaining, particularly within large language models (LLMs), using techniques like bidirectional chaining and symbolic solvers to manage complex reasoning tasks and enhance interpretability. These advancements are significant for improving the reliability and explainability of AI systems in various applications, including natural language processing, supply chain optimization, and automated reasoning.