Choosing socially coherent and rational actions is essential in multiagent problem solving. In some domains, exchanging agents' plans are helpful for understanding what are rational actions. If they has little shared knowledge or environment, however, it is hard to understand other agents' plans. This paper discusses the utility-based cooperation for this situation. A utility matrix are created based on the local plans and through communications with other agents instead of exchanging plans. Utility numbers are calculated according to action benefits and plan certainties. Intuitively, an action benefit expresses the importance of performing or verifying the current plan, and a plan certainty expresses how strongly the agent making the plan believes that it is correct or effective for the current problem solving. Actions based on a plan supported by many proofs have high utility-numbers and so are priority over other actions. Finally, we will show how the performance can be improved by our method through experiments.