6533b832fe1ef96bd129a24c
RESEARCH PRODUCT
Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning
Alexander DockhornDaan Apeldoornsubject
Context modelComputer sciencebusiness.industryComputingMilieux_PERSONALCOMPUTINGApproximation algorithmContext (language use)Belief revisionKnowledge-based systemsArtificial IntelligenceControl and Systems EngineeringSearch algorithmReinforcement learningArtificial intelligenceElectrical and Electronic EngineeringbusinessVideo gameSoftwaredescription
This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-09-01 | IEEE Transactions on Games |