Solving the multiagent selection and scheduling problem

Abstract

Consider Ann’s morning scheduling problem. Ann is a graduate student, who, among many other objectives, would like to both exercise and work on research during her morning. I summarize possible morning activities in Table 1. Even for these two simple objectives, selecting a feasible schedule from the many possible schedules (run then generate experimental results, write then bike, swim then read some related work, etc.) may be non-trivial. However, suppose Ann wants to run with her friend Bill, who notoriously oversleeps his alarm. Additionally, suppose also that Ann must coordinate the use of her lab’s computational cluster with her lab mate Claire. Without further information from Bill and Claire, it is impossible for Ann to determine which candidate schedules will successfully achieve her morning goals. One option for Ann would be to myopically select her schedule anyway, with the risk that her attempt to run with Bill or to use the computational cluster could result in a failed goal. As another option, Ann could also volunteer to collect the scheduling constraints of both Bill and Claire and generate a single joint morning schedule. However, this puts additional scheduling burden on Ann while requiring both Bill and Claire to reveal other scheduling commitments they may prefer to keep private. Even if Ann employed a centralized computational agent to solve this global scheduling problem, the resulting combinatorics may limit the scalability of such a centralized approach. Instead, the pervasiveness of personal computational devices, coupled with desires for scalability and privacy, argue for decentrally solving such problems using multiagent algorithms. My thesis focuses on providing scalable, multiagent algorithms for solving rich, complex multiagent activity scheduling and selection problems, while retaining as much privacy as possible on behalf of the human users. My approach is distinct from other recent multiagent scheduling approaches (Hunsberger 2002; Smith et al. 2007; Shah, Conrad, and Williams 2009) in that it uses a constraint-based representation of selection (finite-domain) aspects of scheduling problems in addition to the scheduling aspects. I proceed by introducing the Multiagent Selection and Scheduling Problem