Goal Reasoning

Goal reasoning focuses on developing computational models that allow agents, whether robots or AI systems, to select and pursue goals effectively, especially in complex or dynamic environments. Current research explores diverse approaches, including rule-based systems enhanced by machine learning (e.g., Ripple-Down Rules), the integration of large language models for creative goal achievement in under-specified scenarios, and the use of formal logic frameworks like Horn clauses and their duals to enable explainable reasoning and plan generation. These advancements are significant for improving the autonomy and adaptability of AI systems across various applications, from smart homes and emergency response to multi-agent collaboration in robotics.