We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.

翻译：我们提出了一种新的目标识别方法，该方法通过比较观测事实与其期望概率来实现。这些概率取决于指定的目标g和初始状态s0。我们的方法将这些概率和观测事实映射到实数向量空间中，以计算潜在目标的启发式值。这些值用于估计给定目标是观测智能体真实目标的可能性。由于在实际中获取观测序列中观测事实的精确期望概率通常不可行，我们提出并通过实验验证了一种近似这些概率的方法。实验结果表明，与现有先进技术相比，所提出的方法在降低计算复杂度的同时，提高了目标识别的精确度。