The Effect Of Location, Schedules, And To-Do Lists On Unscheduled Time
Gary Look, Robert Laddaga & Howard Shrobe
Many people use day planners as scheduling aids. However, no one formally schedules every minute of their day. So how do people spend those parts of their day that aren't explicitly accounted for in a day planner? We are interested in using a person's typical activity patterns, the unscheduled items on his to-do list, and his personal preferences to predict the places he visits during his unscheduled time, and to make relevant suggestions regarding how to spend this time, given his current location and schedule.
Three different scenarios illustrate the sorts of inference we would like to be able to do.
To make the sorts of inferences described in the above scenarios, we break the problem into the following parts. First, we need a descriptive location representation that can be used to describe not only where a person is, but also what he can do at nearby places. We also need to be able to learn a person's usual activity patterns and reason about where a person would go to carry out to-dos, if this information is not included in the to-do. Finally, if, as in the case of the second scenario, there are multiple places a person is likely to go next, we need to be able to rank the likelihood that he would go to each place.
We address these sub-problems as follows. We have created LAIR, an ontology that models both the geographical relationships between spaces as well as what can be done at a given space . We intend to use work by Don Patterson  to learn a person's typical activity patterns and the common-sense knowledge in ConceptNet  to infer where different items on a to-do list can be done.
Once we have identified a person's usual activity patterns and the places at which he can complete his to-dos, we want to generate a set of rules that reflect both his preferences (as demonstrated by which places he has visited in the past) and certain decision-making heuristics. One such rule could state that if a person has an appointment far from where she spends most of her time, then she could use that appointment as an opportunity to visit other places that she would not otherwise be near. Such a rule could be used in Scenario 1 to infer that June was headed to the mall before her 3pm class.
Another rule could use the frequency at which a person visits a certain place to predict whether or not he will visit that place in the near future. Such a rule could be used in Scenario 2 to predict whether Gary will get on the subway and then go grocery shopping or whether he will catch the bus or buy shaving cream at the nearby drug store. A third rule could be used to distinguish between situations in which a person is willing to entertain a suggestion to visit a certain place and perform a to-do, versus disregarding the suggestion because it was made at an inconvenient time (such as a suggestion to buy shaving cream in Scenario 3).
While we initially plan to hand generate these rules, we would like to be able to eventually learn these rules by using past behavior as input to machine learning algorithms and by incorporating explicit user feedback.
This research is supported by MIT's Project Oxygen.
 Gary Look, Buddhika Kottahachchi, Robert Laddaga, and Howard Shrobe. A Location Representation for Generating Descriptive Walking Directions. In IUI '05: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 122-129, San Diego, CA, USA, January 2005.
 Donald J. Patterson, Lin Liao, Krzysztof Gajos, Michael Collier, Nik Livic, Katherine Olson, Shiaokai Wang, Dieter Fox, and Henry Kautz. Opportunity Knocks: a System to Provide Cognitive Assistance with Transportation Services. In The Proceedings of UBICOMP 2004: The Sixth International Conference on Ubiquitous Computing , pp. 433-450, Nottingham, England, October 2004.