Mixed reality games are those in which virtual graphical assets are overlaid on the physical world. We explore the use of procedural content generation to enhance the gameplay experience in a prototype mixed reality game. Procedural content generation is used to design levels that make use of the affordances in the player's physical environment. Levels are tailored to gameplay difficulty and to affect how the player moves their physical body in the real world.

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories.

This paper presents a new model of cooperative behavior based on the interaction of plan recognition and automated planning. Based on observations of the actions of an "initiator" agent, a "supporter" agent uses plan recognition to hypothesize the plans and goals of the initiator. The supporter agent then proposes and plans for a set of subgoals it will achieve to help the initiator. The approach is demonstrated in an open-source, virtual robot platform.

A transfer learning approach is presented to address the challenge of training video game agents with limited data. The approach decomposes games into objects, learns object models, and transfers models from known games to unfamiliar games to guide learning. Experiments show that the approach improves prediction accuracy over a comparable control, leading to more efficient exploration. Training of game agents is thus accelerated by transferring object models from previously learned games.

Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.