In this paper, we address the question of how to create episodic memory based long-term affective interactions with a human-like robot. A key challange for long-term interaction is the recall of past important events during conversation. We suggest that episodic memory is a core concept for realizing this intelligence. In this paper, we propose a model of episodic memory and integrate it with a decision making module based on a Hierarchical Task Network (HTN) planner. Plans generated by the HTN planner are executed by a Finite-state-machine (FSM) based dialogue system in order to produce appropriate responses. Finally, we present a prototype implementation demonstrating the preliminary results we obtained.