Toward conversational human-computer interaction
AI Magazine
Taking email to task: the design and evaluation of a task management centered email tool
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What a to-do: studies of task management towards the design of a personal task list manager
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Collecting paraphrase corpora from volunteer contributors
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AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Towards intelligent assistance for to-do lists
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An analysis of knowledge collected from volunteer contributors
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A scientific workflow construction command line
Proceedings of the 14th international conference on Intelligent user interfaces
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CHI '09 Extended Abstracts on Human Factors in Computing Systems
TellMe: learning procedures from tutorial instruction
Proceedings of the 16th international conference on Intelligent user interfaces
Want world domination? win at risk!: matching to-do items with how-tos from the web
Proceedings of the 16th international conference on Intelligent user interfaces
Review: interactive knowledge capture in the new millennium: How the semantic web changed everything
The Knowledge Engineering Review
Capturing Common Knowledge about Tasks: Intelligent Assistance for To-Do Lists
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
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To-do lists have been found to be the most popular personal information management tools, yet there is no automated system to interpret and act upon them when appropriate on behalf of the user. Automating to-do lists is challenging, not only because they are specified as free text but also because most items contain abbreviated tasks, many do not specify an action to be performed, and often refer to unrelated (personal) items. This paper presents our approach and an implemented system to process to-do list entries and map them to tasks that can be automated for the user by a set of agents. Since the format of to-do entries is not very amenable to natural language processing tools that can parse and create a structured interpretation, our approach is to exploit paraphrases of the target tasks that the agents can perform and that specify how the free-text maps to the task arguments. As users manually assign to-do to agents for automation, our system improves its performance by learning new paraphrases. We show an evaluation of our approach in a corpus of 2100 to-do entries collected from users of an office assistant multi-agent system.