By exchanging information, multiple robots can compensate for the limitations in their individual perceptions of their surroundings. Our previous study proposed a two-step cooperative inference model using foundation models to assess diverse robot actions based on the information exchanged in natural language with other robots and humans. The inference model classifies executed actions into three categories—accomplished, not accomplished, and unclear—using the fuzzy inference system and Dempster-Shafer theory. However, the inference model’s reliance on a single rule in fuzzy inference raises concerns about the accuracy of its action classification. Additionally, evaluating the model’s reliability, validity, and efficiency remains unsatisfactory. In this paper, we first enhance the inference model to generate multiple fuzzy rules and perform a comparative evaluation of reliability, validity, and efficiency using a dataset from a real-world task. Then, we discuss the evaluation results of the first and second steps, the effect of introducing the unclear category in preventing incorrect judgments, and the effect of threshold adjustments in the second step on the inference results.