Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment. Previous AI research has shown the importance of fundamental and indispensable knowledge of actions, i.e., preconditions and effects. However, traditional methods rely on logical formalization which hinders practical applications. With recent transformer-based language models (LMs), reasoning over text is desirable and seemingly feasible, leading to the question of whether LMs can effectively and efficiently learn to solve RAC problems. We propose four essential RAC tasks as a comprehensive textual benchmark and generate problems in a way that minimizes the influence of other linguistic requirements (e.g., grounding) to focus on RAC. The resulting benchmark, TRAC, encompassing problems of various complexities, facilitates a more granular evaluation of LMs, precisely targeting the structural generalization ability much needed for RAC. Experiments with three high-performing transformers indicates that additional efforts are needed to tackle challenges raised by TRAC.

翻译：关于行动和变化的理由,对于理解和与不断变化的环境互动至关重要。以前大赦国际的研究表明,对行动的基本和不可或缺的知识,即先决条件和效果的重要性。然而,传统方法依赖于逻辑正规化,这阻碍了实际应用。最近采用以变压器为基础的语言模式,对文本的推理是可取的,似乎可行,导致液压电流是否能够有效和高效地学习解决冷压问题的问题。我们建议将四项基本RAC任务作为综合文本基准,并产生问题,从而最大限度地减少其他语言要求(例如基础)的影响,以注重液压电流。由此产生的基准TRAC包含各种复杂问题,有利于对液压电流电流进行更为细微的评估,精确地针对液压电流电流的结构性普遍化能力,与三个高性变压器的实验表明,需要进一步努力应对核心预算资源调拨目标所提出的挑战。