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A paradigm shift towards human-centered intelligent gaming systems is gradually setting in. Such intelligent gaming systems with embedded machine learning algorithms would explain player motivations, help design more personalized single and collaborative player experiences, transfer and generalize the learning from game to game. The multi-modal user behavior trajectories, both in-game and across various platforms, incorporate heterogeneous information and graph structures. These gaming modalities range from text, audios, video demos, activity replays, and social networks to psychological questionnaires. Identifying decision-making patterns and strategies by observing in-game behavior actions and mining heterogeneous sources could construct a more holistic representation of the gaming community. Human priors publicly available on the World Wide Web would inspire the modeling for human-like non-player characters, adaptive recommendation systems, automatic game design, testing, and human-AI collaborations. My doctoral research goal is to mine, represent, and learn from human priors existing in the interactive entertainment community's heterogeneous sources and introduce ways to model single and multi-agent interactive behavior patterns.