Bryan McEleney

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We describe a set of dialogue simulation experiments, in which a probabilistic nested user model is employed in deciding between speech acts for a collaborative planning task, finding that a gain in utility can be obtained by using a probabilistic rather than a logical model. Given a set of ordinary dialogue plan rules, our system generates a gametree(More)
The taking of initiative has significance in spoken language dialogue systems and in human-computer interaction. A system that takes no initiative may fail to seize opportunities that are important, but a system that always takes the initiative may not allow the user to take the actions he favours. We have implemented a mixed-initiative planning system that(More)
The development of speech tools suitable for use in real world environments requires collaboration between computational linguistics and new implementation fields e.g. robotics, and the incorporation of new AI techniques to improve overall system performance. In this paper we present the core development concepts of SAID (Speaking Autonomous Intelligent(More)
In 1993, the U.S. Army and U.S. Air Force undertook long-term joint staffing of Landstuhl Regional Medical Center, a 180-bed facility in Germany. With little historical precedence, the Nurse Corps from each service worked diligently to integrate. Initial collaboration suffered because of confusion, ignorance of the other service's capabilities, and(More)
A design is presented for a negotiating agent that can construct coherent joint plans with human or artificial agents. In negotiation there is always a trade-off between plan quality and dialogue length. In dynamic conditions and with human partners, length becomes critical. The approach to efficient negotiation is to use an acquaintance model that predicts(More)
An important tradeoff in error-prone dialogue is between the cost of using more robust dialogue strategies and the cost of recovering from failed understanding without using them. A strategy has to be quantitatively planned for each dialogue state, since too robust a strategy might not have a worthwhile effect on the failure rate. A dialogue manager is(More)
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