论文标题
与常识一起玩基于文本的游戏
Playing Text-Based Games with Common Sense
论文作者
论文摘要
基于文本的游戏是模拟，其中代理人纯粹通过自然语言与世界互动。它们通常包括许多与常见的日常对象和位置相互作用的难题。深厚的增强学习者可以学会解决这些难题。但是，与环境的日常互动虽然对人类玩家来说是微不足道的，但作为代理商的其他难题。我们探索了将常识性知识纳入代理商的两种技术。通过常识推理模型彗星或语言模型BERT推断世界状态的隐藏状态。根据语言模型认可的共同模式对代理探索的偏见。我们在9to05游戏中测试了我们的技术，这是基于文本的游戏的极端版本，它需要与常见的日常，日常情况的常见对象进行大量互动。我们得出的结论是，通过常识推论增强他们对世界国家的信念的代理人对于观察到的错误和从文本描述中遗漏的遗漏更为强大。
Text based games are simulations in which an agent interacts with the world purely through natural language. They typically consist of a number of puzzles interspersed with interactions with common everyday objects and locations. Deep reinforcement learning agents can learn to solve these puzzles. However, the everyday interactions with the environment, while trivial for human players, present as additional puzzles to agents. We explore two techniques for incorporating commonsense knowledge into agents. Inferring possibly hidden aspects of the world state with either a commonsense inference model COMET, or a language model BERT. Biasing an agents exploration according to common patterns recognized by a language model. We test our technique in the 9to05 game, which is an extreme version of a text based game that requires numerous interactions with common, everyday objects in common, everyday scenarios. We conclude that agents that augment their beliefs about the world state with commonsense inferences are more robust to observational errors and omissions of common elements from text descriptions.
