To investigate, how a Robot’s communication ability in terms of explanations can cultivate better trust relationships between a Robot and it’s human teammates. We opted a partial information game-playing environment, to immerse interaction between humans and a Robotic Agent. We designed our Robotic Agent as a Knowledge-Based (KB) Robotic Agent that does not play perfectly, but plays with significant expertise and approximates well enough by updating it’s belief all the time in a partially observable environment. We developed the explanation-generation mechanism on top of the game that generates meaningful explanations for the strategy of a game at a level that the human teammates appreciate and understand. The generated explanations adapt according to the game situation that can increase human’s overall understanding of the task domain. We evaluated the individual effectiveness of our KB Robotic Agent, by developing a Case Study with the partial information game Domino. In a computational experiment, our KB Robotic Agent played 10,000 game matches with other agents and exhibited a reasonable winning rate. With this victory proportion, we can conclude that our KB Robotic Agent captured and analysed all available information intelligently and forecast the possible moves of the opponents correctly.