Incremental search is a search technique for continual planning (or, synonymously, replanning, plan reuse, and lifelong planning) that 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 property suggests that a complete recomputation of the best plan for the new search problem is unnecessary because some of the previous search results can be reused, which is what incremental search does.
Incremental search solves dynamic shortest-path problems, where shortest paths have to be found repeatedly as the topology of a graph or its edge costs change (Ramalingam and Reps 1996b).
The idea of incremental search has also been pursued in AI for problems other than path finding.
We believe that four achievements are necessary to make incremental search more popular in AI.
Having defined incremental search, we can now examine Lindblom's argument about how it is supposed to affect policy processes.
It is reasonable to assume that incremental search is less risky: nonincremental search should be more likely than more local search to generate extremely bad and extremely good policies.
Hereafter, variables with prime superscripts will always be associated with less incremental search.
It is straightforward to show that given the theorem's assumptions about alternative generation and judgment, weak competence is necessary for radical search to be superior to incremental search.