We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.
Aliod, Diego Molla, Alonso, Eduardo, Bangalore, Srinivas, Beck, Joseph, Bhanu, Bir, Blythe, Jim, Boddy, Mark, Cesta, Amedeo, Grobelink, Marko, Hakkani-Tur, Dilek, Harabagiu, Sanda, Lege, Alain, McGuinness, Deborah L., Marsella, Stacy, Milic-Frayling, Natasha, Mladenic, Dunja, Oblinger, Dan, Rybski, Paul, Shvaiko, Pavel, Smith, Stephen, Srivastava, Biplav, Tejada, Sheila, Vilhjalmsson, Hannes, Thorisson, Kristinn, Tur, Gokhan, Vicedo, Jose Luis, Wache, Holger
The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.
The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be trade-offs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.