CAPIR: Collaborative Action Planning with Intention Recognition
This addresses the challenge of scalable AI assistance in collaborative gaming, though it is incremental as it builds on existing Markov decision process methods.
The paper tackles the problem of creating non-player characters that assist human players in collaborative games by using decision-theoretic techniques and intention recognition, achieving near-human level performance in experiments.
We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.