Using CBR in the exploration of unknown environments with an autonomous agent

Exploration involves selecting and executing sequences of actions so that the knowledge of the environments is acquired. In this paper we address the problem of exploring unknown, dynamic environments populated with both static and non-static entities (objects and agents) by an autonomous agent. The agent has a case-base of entities and another of plans. This case-base of plans is used for a case-based generation of goals and plans for visiting the unknown entities or regions of the environment. The case-base of entities is used for a case-based generation of expectations for missing information in the agent's perception. Both case-bases are continuously updated: the case-base of entities is updated as new entities are perceived or visited, while the case-base of plans is updated as new sequences of actions for visiting entities/regions are executed successfully. We present and discuss the results of an experiment conducted in a simulated environment in order to evaluate the role of the size of the case-base of entities on the performance of exploration.