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Publication Details

A Constraint Based Approach to Scheduling an Individual's Activities

by Refanidis, I. and Yorke-Smith, N.

ACM Transactions on Intelligent Systems and Technology, vol. 1, no. 2, pp. 12:1-12:32, November 2010.


The goal of helping to automate the management of an individual’s time is ambitious in terms both of knowledge engineering and of the quality of the plans produced by an AI system. Modeling an individual’s activities is itself a challenge, due to the variety of activity, constraint, and preference types involved. Activities might be simple or interruptible; they might have fixed or variable durations, constraints over their temporal domains, and binary constraints between them. Activities might require the individual being at specific locations in order, whereas traveling time should be taken into account. Some activities might require exclusivity, whereas others can be overlapped with compatible concurrent activities. Finally, while scheduled activities generate utility for the individual, extra utility might result from the way activities are scheduled in time, individually and in conjunction. This article presents a rigorous, expressive model to represent an individual’s activities, that is, activities whose scheduling is not contingent on any other person. Joint activities such as meetings are outside our remit; it is expected that these are arranged manually or through negotiation mechanisms and they are considered as fixed busy times in the individual’s calendar. The model, formulated as a constraint optimization problem, is general enough to accommodate a variety of situations. We present a scheduler that operates on this rich model, based on the general squeaky wheel optimization framework and enhanced with domain-dependent heuristics and forward checking. Our empirical evaluation demonstrates both the efficiency and the effectiveness of the selected approach. Part of the work described has been implemented in the SelfPlanner system, a Web-based intelligent calendar application that utilizes Google Calendar.

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Copyright ©2010 ACM

Associated Projects


Cognitive Assistant that Learns and Organizes

As part of DARPA’s Personalized Assistant that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).



A user-centric, integrated planning and scheduling system that assists the user in exploring the rich space of plans and associated resource assignment options in complex, real-world domains. Each component of the system (user, planner, and scheduler) reacts to the actions of the other, resolving conflicts, and iteratively refining the solution until acceptable.

AIC Personnel

Name Title E-mail
Refanidis, Ioannis A. International Fellow
Yorke-Smith, Neil Computer Scientist

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