Let’s Compete! The Influence of Human-Agent Competition and Collaboration on Agent Learning and Human Perception

Abstract

In interactive agent learning, the human may teach in a collaborative or adversarial manner. Past research has been focusing on collaborative teaching styles as these are common in human education settings, while overlooking adversarial ones despite promising results in recent research. Moreover, agent performance has been the main focal point while neglecting the perspective of the human teacher, who is crucial to the instructional process. In this work, we examine the impact of competitive and collaborative teaching styles on agent learning and human perception. We conducted a study (N=40) for participants to demonstrate a task in different interaction modes for teaching a computer agent: collaboratively, competitively, or without interacting with the agent. Most participants reported that they preferred competing against the computer agent to the other two modes. Despite smaller numbers of demonstrations given from the user, the agent performance from the interactive modes (collaborative and competitive) was comparable to the non-interactive mode (solo). The agent was perceived as being more competent in the competitive mode than the collaborative mode despite the marginally worse in-task performance. These preliminary findings suggest that competitive types of interaction, when agents or robots learn from humans, lead to better human perception of the agent’s learning when compared to collaborative, and better user engagement when compared to non-interactive learning from demonstrations.