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      Generating Dialogue Agents via Automated Planning

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          Abstract

          Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.

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          Most cited references 5

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          Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

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            Training a real-world POMDP-based dialogue system

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              Affective Dialogue Management Using Factored POMDPs

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                Author and article information

                Journal
                02 February 2019
                Article
                1902.00771

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Accepted at the AAAI-2019 DEEP-DIAL workshop
                cs.AI

                Artificial intelligence

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