A Survey of the Seventh International Planning Competition

Abstract

A utomated planning is the process of finding an ordered sequence of actions that, starting from a given initial state, allows the transition to a state where a series of objectives are achieved. Actions are usually expressed in terms of preconditions and effects; that is, the requirements a state must meet for the action to be applied, and the changes subsequently made. Domain-independent planning relies on general problem-solving techniques to find an (approximately) optimal sequence of actions and has been the focus of numerous International Planning Competitions (IPCs) over the years.The first IPC was organized by Drew McDermott in 1998. For the following 10 years it was a biennial event and remains a keystone in the worldwide planning research community: the most recent, seventh, IPC took place in 2011. The major important contribution of the first competition was to establish a common standard language for defining planning problems -the planning domain definition language (PDDL) (McDermott 1998) -which has been developed and extended throughout the competition series. Today, the extended PDDL is still widely used and is key in allowing fair benchmarking of planners. Participation has increased dramatically over the years and a growing number of tracks have formed, representing the broadening community -see figure 1 for details. The three main tracks now operating are the deterministic, learning, and uncertainty tracks.The IPC has two main goals: to produce new benchmarks,

A Guide to Portfolio-Based Planning

“…In the multicore track of the last IPC [2] there were several planners based on the idea of running simultaneously different planning algorithms. For the purposes of this paper they are all similar, and we selected only ArvandHerd, the winner of the track, for representing the category.…”

“…The comparison can be done by a statistical analy-sis, like in PbP and PbP2, or by evaluating the performances of planners using some metrics like, for instance, the IPC scores ( [2]). …”

“…The only existing system that is able to configure a domainspecific portfolio (also considering additional domain-related knowledge) for minimizing runtime is PbP (and its latest version, PbP2). Analyzing the runtimesconfigured portfolios that PbP generated for IPC6-7 benchmark domains [3,2], it is easy to note that usually a single planner (possibly with additional knowledge extracted from the domain under the form of macro-actions) is selected. It would be interesting, for all the Automated Planning community, to offer an in-depth analysis for better understanding this behaviour.…”

“…Very recently, a number of planners based on portfolio approach have been developed, and achieved impressive results in the last editions of the International Planning Competition (IPC6-7) [3,2]: they won, or got very close to, in every track they took part. These include the deterministic track, learning track and multicore track.…”

Goal Probability Analysis in Probabilistic Planning: Exploring and Enhancing the State of the Art

“…One important objective then is MaxProb, determining the maximal probability with which the goal can be reached (and identifying a policy achieving that probability). MaxProb also partly underlies the International Probabilistic Planning Competition (IPPC) (Younes, Littman, Weissman, & Asmuth, 2005;Bryce & Buffet, 2008;Coles, Coles, García Olaya, Jiménez, Linares López, Sanner, & Yoon, 2012), when planners are evaluated by how often they reach the goal in online policy execution. (The time limit in the IPPC setting mixes MaxProb with a bias towards policies reaching the goal quickly.…”