This paper presents a study on a rule induction application for generating an agent strategy. It is a new approach in multi-agent systems, where reinforcement learning and evolutionary computation is broadly used for this purpose. Experimental results show that rule induction improves agent performance very quickly. What is more, rule-based knowledge representation has many advantages. It is comprehensive and clear. It allows for the examination of the learned knowledge by humans. Because of modularity of the knowledge, it also allows for the implementation of the knowledge exchange in a natural way -- only necessary set of rules can be sent. Rule induction is tested in two domains: Fish Banks game, in which agents run fishing companies and learn how to allocate ships, and Predator-Prey domain, in which predator agents learn how to capture preys. The proposed learning mechanism should be beneficial in all domains, in which agents can determine the results of their actions.