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- W1480716215 abstract "Interacting with a computer requires adopting a metaphor to guide our actions and expectations. When we choose to build and use agents we are committing to interact with a domain indirectly. Whether this is a good choice or not will depend on the ease and accuracy with which we can instruct our agents. Current approaches range from specialized agents programmed to perform specific tasks, to learning programs which get on-the-job training looking over a user’s shoulder. In the expert case very little communication is needed because the agent already knows what it is going to do. In the novice case the raison d’etre of agent learning is to relieve the user of the tedium of instructing it. The vast middle ground of tasks of moderate complexity too infrequent for targeted implementations or empirical learning goes largely untouched. Three current projects at our laboratory address aspects of this problem. The first, a series of experiments in which subjects are aided by fallible agents examines the role of trust in providing a context for communication. The second project compares the effectiveness of communication to adapt an agent plan with human planning which is critiqued by an equally informed agent. The third project uses a variety of learning and interaction techniques to help a user communicate the information needed to access and extract information on a subsequent autonomous visit." @default.
- W1480716215 created "2016-06-24" @default.
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- W1480716215 date "1999-01-01" @default.
- W1480716215 modified "2023-09-28" @default.
- W1480716215 title "Anticipation Delegation, and Demonstration: Why Talking to Agents Is Hard" @default.
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- W1480716215 doi "https://doi.org/10.1007/3-540-48414-0_25" @default.
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