The Academic Advising Planning Domain

The International Probabilistic Planning Competition is a leading showcase for fast stochastic planners. The current domains used in the competition have raised challenges that the leading deterministic-planner-based MDP solvers have been able to meet. We argue that in order to continue to raise challenges and match real world applications, domains must be generated that exhibit true stochasticity, multi-valued domain variables, and concurrent actions. In this paper we propose the academic advising domain as a planning competition domain that exhibits these characteristics. We believe that this domain can build upon the success of previous contests in pushing the limits of MDP planning research. Introduction Generating new problem sets to challenge state-of-the-art probabilistic planners is often difficult. Planners have evolved in leaps and bounds in a very short time, opening the door to whole new classes of problems that can realistically be solved by computer programs. As a driver in planning research, the International Probabilistic Planning Competition (IPPC) has presented a number of domains meant to continue pushing the field in new and exciting directions. As the IPPC has continued, domains have become increasingly complex and stochastic. In recent contests, strong planners that solve deterministic instances of stochastic domains (or “replanners” (Little and Thiébaux 2007)) have continued to outstrip their inherently probabilistic counterparts. This indicates to us that the domains that have been used are still not stochastic enough to closely model the complexities of real world planning problems. We suggest that current real world domains can be modified to present more difficult problems, and that new domains requiring different problem solving strategies can help provide more challenges for planners. Current competition domains have been designed to represent real world scenarios for probabilistic planners. In the Elevator domain, for example, a controller coordinates a bank of elevators to pick up passengers when requests are received, and delivers them to the destination floor. This domain assumes that each elevator is acting separately and actions are performed sequentially. This makes the state space less complex, allowing deterministic replanners to produce strong solutions. In most real world problems, however, the most comprehensive models are highly stochastic and consider actions taken concurrently. This domain can be modified by assuming that the controller is directing elevators in concurrent pairs or groups, instead of individually and sequentially. This introduces joint actions that greatly increase the size and complexity of the state space. As the state space grows more complex, it becomes more difficult for deterministic planners to produce a good solution, and probabilistic planners using concurrent action planning become better choices due to a more complete consideration the domain (Sanner 2008). We believe that many other competition domains could be represented in this fashion, and that continuing to introduce domains requiring different planning methods provides interesting and valuable challenges to competitive planners. Our domain, the academic advising domain, represents a real world model in which concurrent actions must be considered in order to reach an optimal solution. Academic advising, when treated as a probabilistic planning domain, is rich with the qualities used to classify domains as probabilistically interesting (Little and Thiébaux 2007). The presence of avoidable dead-ends, near-identical trajectories with distinct outcomes, multiple distinct trajectories, and mutually exclusive actions, as well as a high degree of unpredictable outside influences, makes for a challenging environment for state-of-the-art planners today. Planners in this domain must consider factors such as past performance history, courses completed, and above all else the student’s ability to take multiple courses concurrently — all with the aim of maximizing the student’s GPA as well as satisfying the requirements for graduation with a specific area of study. This domain presents a large, complex, and easily scalable state space, ideal for a challenging competition. Most importantly, while deterministic planning may eventually produce a solution, optimal solutions can only be found using stochastic, concurrent-action planning. We believe that this quality could make the academic domain a good addition to the domains already used in competition, and help introduce even more “spice” into an already challenging problem set (Sanner 2008). Competitive domains have always been used to test the very best in planners, and it is our hope that the academic domain might be able to continue in this tradition. In the section “The Academic Domain”, we describe the 1 ICAPS 2012 Proceedings of the 3rd Workshop on the International Planning Competition