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People are able to interact with domain-specific intelligent assistants (IAs) and get help with tasks. But sometimes user goals are complex and may require interactions with multiple applications. However current IAs are limited to specific applications and users have to directly manage execution spanning multiple applications in order to engage in more complex activities. An ideal personal agent would be able to learn, over time, about tasks that span different resources. This paper addresses the problem of cross-domain task assistance in the context of spoken dialogue systems. We propose approaches to discover users' high-level intentions and using this information to assist users in their task. We collected real-life smartphone usage data from 14 participants and investigated how to extract high-level intents from users' descriptions of their activities. Our experiments show that understanding high-level tasks allows the agent to actively suggest apps relevant to pursuing particular user goals and reduce the cost of users' self-management.
Sun et al. (Thu,) studied this question.
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