Agent decision-making is an information-intensive activity. Its performance is affected by the availability of relevant information. Bayesian networks have provided a probabilistic estimate for uncertain information. However, for those decision problems where information is represented in predicates, Bayesian inferences are required to process the variable-bound relations across predicates. Multi-Layer Bayesian Network (MLBN) is an extension of the classical model of Bayesian networks with multiple layers of conditional probability tables, each corresponding to one specific variable binding. The MLBN has been implemented based on an agent architecture. Experiments have shown its capability of improving performance in an experience-based decision-making framework.