Dialogue systems deliver a more natural mean of communication between humans and machines when compared to traditional systems. Beyond input/output components that understand and generate natural language utterances, the core of a dialogue system is the dialogue manager. The aim of the dialogue manager is to mimic all cognitive aspects related to a natural conversation and it is responsible for identifying the current state of the dialogue and for deciding the next action to be taken by a dialogue system. Artificial intelligence (AI) planning is one of the techniques available in the literature for dialogue management. In a dialogue system, AI planning deals with the action selection problem by treating each utterance as an action and by choosing the actions that get closer to the dialogue goal. This work aims to provide a systematic literature review (SLR) that investigates recent contributions to plan-based dialogue management. This SLR aims at answering research questions concerning: (i) the types of AI planning exploited for dialogue management; (ii) the planning characteristics that justify its adoption in dialogue system; (iii) and, the challenges posed on the development of plan-based dialogue managers. The present SLR was performed by querying four scientific repositories, followed by a manual search on works from the most eminent authors in the field. Further works that were cited by the retrieved papers were also considered for inclusion. Our final corpus is composed of forty works, including only works published since 2014. The results indicate that AI planning is still an emerging strategy for dialogue management. Although AI planning can offer a strong contribution to dialogue systems, especially to those that require predictability, some relevant challenges might still limit its adoption. Our results contributed to discussions in the field and they highlight some research gaps to be addressed in future studies.

A Review of Plan-Based Approaches for Dialogue Management

Milene Santos Teixeira;Mauro Dragoni
2022

Abstract

Dialogue systems deliver a more natural mean of communication between humans and machines when compared to traditional systems. Beyond input/output components that understand and generate natural language utterances, the core of a dialogue system is the dialogue manager. The aim of the dialogue manager is to mimic all cognitive aspects related to a natural conversation and it is responsible for identifying the current state of the dialogue and for deciding the next action to be taken by a dialogue system. Artificial intelligence (AI) planning is one of the techniques available in the literature for dialogue management. In a dialogue system, AI planning deals with the action selection problem by treating each utterance as an action and by choosing the actions that get closer to the dialogue goal. This work aims to provide a systematic literature review (SLR) that investigates recent contributions to plan-based dialogue management. This SLR aims at answering research questions concerning: (i) the types of AI planning exploited for dialogue management; (ii) the planning characteristics that justify its adoption in dialogue system; (iii) and, the challenges posed on the development of plan-based dialogue managers. The present SLR was performed by querying four scientific repositories, followed by a manual search on works from the most eminent authors in the field. Further works that were cited by the retrieved papers were also considered for inclusion. Our final corpus is composed of forty works, including only works published since 2014. The results indicate that AI planning is still an emerging strategy for dialogue management. Although AI planning can offer a strong contribution to dialogue systems, especially to those that require predictability, some relevant challenges might still limit its adoption. Our results contributed to discussions in the field and they highlight some research gaps to be addressed in future studies.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/334572
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