Incremental heuristic search algorithms combine both incremental and heuristic search to speed up searches of sequences of similar search problems, which is important in domains that are only incompletely known or change dynamically.[1] Incremental search has been studied at least since the late 1960s. Incremental search algorithms reuse information from previous searches to speed up the current search and solve search problems potentially much faster than solving them repeatedly from scratch.[2] Similarly, heuristic search has also been studied at least since the late 1960s.
Heuristic search algorithms, often based on A*, use heuristic knowledge in the form of approximations of the goal distances to focus the search and solve search problems potentially much faster than uninformed search algorithms.[3] The resulting search problems, sometimes called dynamic path planning problems, are graph search problems where paths have to be found repeatedly because the topology of the graph, its edge costs, the start vertex or the goal vertices change over time.[4]
So far, three main classes of incremental heuristic search algorithms have been developed:
The first class restarts A* at the point where its current search deviates from the previous one (example: Fringe Saving A*[5]).
The second class updates the h-values (heuristic, i.e. approximate distance to goal) from the previous search during the current search to make them more informed (example: Generalized Adaptive A*[6]).
The third class updates the g-values (distance from start) from the previous search during the current search to correct them when necessary, which can be interpreted as transforming the A* search tree from the previous search into the A* search tree for the current search (examples: Lifelong Planning A*,[7] D*,[8] D* Lite[9]).
All three classes of incremental heuristic search algorithms are different from other replanning algorithms, such as planning by analogy, in that their plan quality does not deteriorate with the number of replanning episodes.
^S. Koenig, M. Likhachev, Y. Liu and D. Furcy. Incremental Heuristic Search in Artificial Intelligence. Artificial Intelligence Magazine, 25(2), 99-112, 2004.
^N. Deo and C. Pang. Shortest-path algorithms: Taxonomy and Annotation. Networks 14, 275–323, 1984.
^P. Hart, N. Nilsson and B. Raphael, A Formal Basis for the Heuristic Determination of Minimum Cost Paths, IEEE Trans. Syst. Science and Cybernetics, SSC-4(2), 100-107, 1968.
^N. Deo and C. Pang. Shortest-path algorithms: Taxonomy
and Annotation. Networks 14, 275–323, 1984.
^X. Sun and S. Koenig. The Fringe-Saving A* Search Algorithm - A Feasibility Study. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2391-2397, 2007.
^X. Sun, S. Koenig and W. Yeoh. Generalized Adaptive A*. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 469-476, 2008.
^S. Koenig, M. Likhachev and D. Furcy. Lifelong Planning A*. Artificial Intelligence, 155, (1-2), 93-146, 2004.
^6. A. Stentz. The Focussed D* Algorithm for Real-Time Replanning. Proceedings of the International Joint Conference on Artificial Intelligence, 1652–1659, 1995.
^S. Koenig and M. Likhachev. Fast Replanning for Navigation in Unknown Terrain. Transactions on Robotics, 21, (3), 354-363, 2005.
and 22 Related for: Incremental heuristic search information
Incrementalheuristicsearch algorithms combine both incremental and heuristicsearch to speed up searches of sequences of similar search problems, which...
In computer science, Monte Carlo tree search (MCTS) is a heuristicsearch algorithm for some kinds of decision processes, most notably those employed...
Dijkstra's algorithm A* search algorithm, a special case of the Dijkstra's algorithm D* a family of incrementalheuristicsearch algorithms for problems...
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. In many problems, a...
computationally inefficient in many applications D*: an incrementalheuristicsearch algorithm Depth-first search: traverses a graph branch by branch Dijkstra's...
appropriate entry in the table is incremented, such as by adding d² or 2d where d is the current search depth. Beyond an incremental depth of about 2, history...
20078. Menczer, F. (1997). ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery Archived 21 December 2012 at the...
Search optimization may refer to: Local search (optimization), a heuristic method for solving computationally hard optimization problems Location search...
Guided local search is a metaheuristic search method. A meta-heuristic method is a method that sits on top of a local search algorithm to change its behavior...
Luger & Stubblefield (2004, pp. 79–121) Nilsson (1998, chpt. 8) Heuristic or informed searches (e.g., greedy best first and A*): Russell & Norvig (2021, s§3...
to efficiently search nonconvex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally from samples drawn...
ability, entity theorists performed better than incremental theorists. In certain situations, the incremental theorists studied were self-critical about the...
better solution by making an incremental change to the solution. If the change produces a better solution, another incremental change is made to the new...
Ichigeki Sacchu!! HoiHoi-san IJCAI Computers and Thought Award Incrementalheuristicsearch Industrial robot The Industries of the Future (book) Informatics...
HeuristicLab is a software environment for heuristic and evolutionary algorithms, developed by members of the Heuristic and Evolutionary Algorithm Laboratory...
that it is both simpler to implement and faster than incremental garbage collection. Incremental and concurrent garbage collectors are designed to reduce...
practiced by Alan Turing with terms such as "memory", "search" and "stimulus". In contrast, a heuristic is an approach to problem solving that may not be fully...
intractable and so heuristic methods must be used instead. For example, tabu search can be used to search for solutions to ILPs. To use tabu search to solve ILPs...
propagation, but may use customized code like a problem-specific branching heuristic. Constraint programming takes its root from and can be expressed in the...
heaviest common subsequence height height-balanced binary search tree height-balanced tree heuristic hidden Markov model highest common factor Hilbert curve...
These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best...
Global Information