This paper describes an intelligent, mixed-initiative scheduler which supports users in the organization and revision of their calendars, helping them to allocate personal and shared activities involving other collaborators. Our scheduler exploits well-known temporal reasoning techniques to suggest complete schedules, as well as to guide the user in the exploration of the possible changes to the timing of tasks, in order to enable her/him to modify the calendar in an informed way. We have integrated our scheduler into a Collaborative Task Manager service supporting the management of projects and distributed tasks. 1

When multiple agents want to maintain temporal informa-tion, they can employ a Multiagent Simple Temporal Net-work (MaSTN). Recent work has shown that the constraints in a MaSTN can be efficiently propagated by enforcing partial path consistency (PPC) with a distributed algorithm. How-ever, new temporal constraints may arise continually due to ongoing plan construction or execution, the decisions of other agents, and other exogenous events. For these new constraints, propagation is again required to re-establish PPC. Because the affected part of the network may be small, one typically wants to exploit the similarities between the new and pre-vious version of the MaSTN. To this end, we propose two new distributed algorithms for incrementally maintaining PPC. The first is inspired by4STP, the seminal PPC algorithm for STNs; the second is a distributed version of IPPC, which rep-resents the current state of the art for incrementally enforcing PPC in a centralized setting. The worst-case time performance of these algorithms is similar to their centralized counterparts. We empirically compare our distributed algorithms, analyzing their performance under various assumptions, and demonstrate significant speedup over their centralized counterparts.

Abstract All-pairs shortest paths (APSP) can eliminate the need to search in a graph, providing optimal moves very fast. A major challenge is storing pre-computed APSP data efficiently. Recently, compression has successfully been employed to scale the use of APSP data to roadmaps and gridmaps of realistic sizes. We develop new techniques that improve the compression power of state-of-the-art methods by up to a factor of 5. We demonstrate our ideas on game gridmpaps and the roadmap of Australia. Part of our ideas have been integrated in the Copa CPD system, one of the two best optimal participants in the grid-based path planning competition GPPC.

Scheduling support is very important for calendar man-agement in order to automatize the execution of pos-sibly complex reasoning tasks. However, an interactive approach is desirable to enable the user to steer the allo-cation of events, which is a rather personal and critical kind of activity. This paper proposes a mixed-initiative scheduling model supporting the user’s awareness dur-ing the exploration of the solution space. The paper de-scribes the temporal reasoning techniques underlying MARA (Mixed-initiAtive calendaR mAnager), focus-ing on the generation of scheduling options and on the characterization of their properties, needed to present the pros and cons of each possible solution to the user.