Incremental A*
Authors: S. Koenig, M. Likhachev
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Incremental search techniques ﬁnd optimal solutions to series of similar search tasks much faster than is possible by solving each search task from scratch. While researchers have developed incremental versions of uninformed search methods, we develop an incremental version of A. The ﬁrst search of Lifelong Planning A is the same as that of A* but all subsequent searches are much faster because it reuses those parts of the previous search tree that are identical to the new search tree. We then present experimental results that demonstrate the advantages of Lifelong Planning A* for simple route planning tasks.
1 Overview Artiﬁcial intelligence has investigated knowledge-based search techniques that allow one to solve search tasks in large domains. Most of the research on these methods has studied how to solve one-shot search problems. However, search is often a repetitive process, where one needs to solve a series of similar search tasks, for example, because the actual situation turns out to be slightly different from the one initially assumed or because the situation changes over time. An example for route planning tasks are changing trafﬁc conditions. Thus, one needs to replan for the new situation, for example if one always wants to display the least time-consuming route from the airport to the conference center on a web page. In these situations, most search methods replan from scratch, that is, solve the search problems independently. Incremental search techniques share with case-based planning, plan adaptation, repair-based planning, and learning search-control knowledge the property that they ﬁnd solutions to series of similar search tasks much faster than is possible by solving each search task from scratch. Incremental search techniques, however, differ from the other techniques in that the quality of their solutions is guaranteed to be as good as the quality of the solutions obtained by replanning from scratch.
Although incremental search methods are not widely known in artiﬁcial intelligence and control, different researchers have developed incremental search versions of uninformed search methods in the algorithms literature. An overview can be found in [FMSN00]. We, on the other hand, develop an incremental version of A, thus combining ideas from the algorithms literature and the artiﬁcial intelligence literature. We call the algorithm Lifelong Planning A (LPA*), in analogy to “lifelong learning” [Thr98], because it reuses
 We thank Anthony Stentz for his support. The Intelligent Decision-Making Group is partly supported by NSF awards under contracts IIS-
9984827, IIS-0098807, and ITR/AP-0113881. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the ofﬁcial policies, either expressed or implied, of the sponsoring organizations and agencies or the U.S. government.

information from previous searches. LPA* uses heuristics to focus the search and always ﬁnds a shortest path for the current edge costs. The ﬁrst search of LPA* is the same as that of A* but all subsequent searches are much faster. LPA* produces at least the search tree that A* builds. However, it achieves a substantial speedup over A* because it reuses those parts of the previous search tree that are identical to the new search tree.