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- W2808032551 abstract "We envision an intelligent agent that analyzes conversations during human team meetings in order to infer the team’s plan, with the purpose of providing decision support to strengthen that plan. We present a novel learning technique to infer teams' final plans directly from a processed form of their planning conversation. Our method employs reinforcement learning to train a model that maps features of the discussed plan and patterns of dialogue exchange among participants to a final, agreed-upon plan. We employ planning domain models to efficiently search the large space of possible plans, and the costs of candidate plans serve as the reinforcement signal. We demonstrate that our technique successfully infers plans within a variety of challenging domains, with higher accuracy than prior art. With our domain-independent feature set, we empirically demonstrate that our model trained on one planning domain can be applied to successfully infer team plans within a novel planning domain." @default.
- W2808032551 created "2018-06-21" @default.
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- W2808032551 date "2018-07-01" @default.
- W2808032551 modified "2023-09-26" @default.
- W2808032551 title "Learning to Infer Final Plans in Human Team Planning" @default.
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- W2808032551 doi "https://doi.org/10.24963/ijcai.2018/663" @default.
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