Interaction quality improvement in a social robotic platform can be achieved through intention detection/prediction of the user. In this research, we tried to study the effect of intention prediction during a human-robot game scenario. We used our humanoid robotic platform, RASA. Rock-Paper-Scissors was chosen as our game scenario. In the first step, a Leap Motion sensor and a Multilayer Perceptron Neural Network is used to detect the hand gesture of the human-player. On the next level, in order to study the intention prediction’s effect on our human-robot gaming platform, we implemented two different playing strategies for RASA. One of the strategies was to play randomly, while the other one used Markov Chain model, to predict the next move. Then 32 players with the ages between 20 to 35 were asked to play Rock-Paper-Scissors with RASA for 20 rounds in each strategy mode. Participants did not know about the difference in the robot’s decision-making strategy in each mode and the intelligence of each strategy modes as well as the Acceptance/Attractiveness of the robotic gaming platform were assessed quantitatively through a questionnaire. Finally, paired T-tests indicated a significant difference between the random playing strategy and the other strategy predicting players’ intention during the game.