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,
Recently there has been renewed interest in bidirectional heuristic search. New algorithms, e.g., MM, MMe, and NBS, have been introduced which seem much closer to refuting the accepted wisdom that "any front-to-end bidirectional heuristic search algorithm will likely be dominated by unidirectional heuristic search or bidirectional brute-force search". However, MM and MMe can still be dominated by their bidirectional brute-force versions, i.e., they can show a "hump-in-the-middle". We introduce a novel general breadth-first heuristic search algorithm, GBFHS, that unifies both unidirectional and bidirectional search into a single algorithm. It uses knowledge of the edge cost in unit cost domains to stop on first-collision in unidirectional search and in bidirectional search, unlike MM, MMe, and NBS. With no heuristic it expands fewer nodes bidirectionally than Nicholson's blind bidirectional search algorithm. GBFHS expands substantially fewer nodes than MM0, MM, MMe, and NBS. Additionally, GBFHS does not show a "hump-in-the-middle". GBFHS run bidirectionally is not dominated by bidirectional brute-force search, likewise, GBFHS run unidirectionally is not dominated by A*.
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