Goto

Collaborating Authors

 start distance


Incremental Heuristic Search in AI

Koenig, Sven, Likhachev, Maxim, Liu, Yaxin, Furcy, David

AI Magazine

Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. In this article, we give an overview of incremental search, focusing on LIFELONG PLANNING A*, and outline some of its possible applications in AI.


Incremental A*

Koenig, S., Likhachev, M.

Neural Information Processing Systems

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.


Incremental A*

Koenig, S., Likhachev, M.

Neural Information Processing Systems

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.


Incremental A*

Koenig, S., Likhachev, M.

Neural Information Processing Systems

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.