Play is a common activity, providing not only pleasure but also physical and cognitive development. In the quest for new playing experiences, there is an increasing tendency to develop robots playing with people. Making believable playing robots able to keep human players engaged and satisfied by the playing experience is the main challenge.In this work, we investigate the possibilities of a playful interaction between a human player and a mobile robot. In particular, this paper focuses on the applicability of deception as a means to support engagement and the attribution of rationality to playing robotic agents. By analyzing the interaction situation between the human and robot players, by identifying the need for deception, and by deciding whether and how to deceive, we aim at increasing self-reported engagement and fun, which are also related to the perception of the robotic opponent as smart enough to compete at an appropriate level.Experiments were conducted on a sample of 78 subjects facing two different deceptive behaviors and a basic behavior without any deception. All participants responded to a post-interaction questionnaire from which it was possible to observe a positive acceptance of the perception of the robot as a rational agent aimed at winning. In general, deception was perceived by most of the players as one of the robot's abilities, when actuated, and contributed to the reported fun.

A key issue for a device involved in a competitive game is to be able to match the ability of the opponent, thus making the game interesting. When the game is played by a human player against a machine, this should be able to adapt online to the opponent's ability, which may change during the game. When the playing machine is a robot, and the game involves physical activity, adaptation should consider the player's ability, but also other aspects directly related to the physical nature of the game. Adaptation could be obtained either intrinsically, by designing the interaction, or explicitly, by modeling aspects of the opponent that can enable to estimate performance, behaviors, and strategies. We present in this paper different approaches to design the two types of adaptation, together with two competitive, physically interactive robogames exploiting them.