Recently, we proposed an intelligent ubiquitous computing (ubicomp) environment where sensors and/or their stations/servers have CPUs to cooperatively learn generalized series of sensed events that are involved in human activities. This can be regarded as a multi-agent application. Because ubicomp applications target support for daily-life activities, one of their characteristics is that the same/similar series of events occurs frequently. Multi-agent plans in applications of this type are used to foresee human activities and generate programs to assist them. Therefore, the same planning processes for conflict detection and resolution recur. This paper proposes a learning method in which past plans are exploited for problem solving in an environment where the same/similar problems appear repeatedly. We discuss how the plan is stored and reused using as an example the exploration of conflict-free routes in a room and then describe experimental results.