Humans possess an innate ability to infer others’ intentions from ambiguous utterances based on the observation of contextual cues and past actions. Conversely, machines typically necessitate explicit instructions, thereby increasing the temporal cost of human-AI interaction. To mitigate this, we propose the Intuition Estimation and Knowledge-Based Planning (IEKP) method, which augments human-AI collaboration under ambiguous directives. IEKP encompasses three principal components: 1) Associative Reasoning based Goal Recognition (ARGoal) utilizes large language model to form an initial estimation of human goals and refines this estimation through associative mechanisms; 2) Finite State Machine Guided Decision Pruning (FDPrune) constructs state machines based on task types, pruning illegitimate action outputs to enhance the robustness of language models in long-term decision processes; 3) Knowledge-Enhanced Searching System (K-Search) leverages co-occurrence relationships between objects and environments to improve the agent’s efficiency in environmental searches. Our approach markedly enhances performance on the HandMeThat task, increasing the success rate by 65.42% and the average score by 88.03 compared to previous state-of-the-art methods, even surpassing human performance. This underscores the efficacy of IEKP in advancing human-AI collaboration through superior comprehension and execution of under-specified instructions.