Cooperation among autonomous agents involves an inherent degree of uncertainty. Agents determine for themselves when to initiate cooperation or to assist others, when to rescind commitments, and how to conduct cooperative tasks. For example, an agent may delay the execution of a cooperative task, execute it to a reduced quality, or simply fail to complete it. In this paper, we describe how experience-based trust can be used to minimise the risk associated with cooperation. In particular we propose a mechanism, called multi-dimensional trust, which allows agents to model the trustworthiness of others according to various criteria. This trust information is combined with other factors to enable the selection of cooperative partners. Agents' preferences are represented by a set of factor weightings, which allow trust information to be tailored to the current cooperative priorities. We also describe the experimental validation of our proposed approach.