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Casebased subgoaling in realtime heuristic search for video game pathfinding
 J. Artif. Intell. Res
, 2010
"... Realtime heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must ..."
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Cited by 17 (4 self)
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Realtime heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents ’ actions. On the downside, realtime search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by revisiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA * is well poised for video games, except it has a complex and memorydemanding precomputation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memoryefficient way of precomputing subgoals thereby eliminating the main obstacle to applying stateoftheart realtime search methods in video games. The new algorithm solves a number of randomly chosen problems offline, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem online, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14 % less precomputation time. 1.
A Comparison of HighLevel Approaches for Speeding up Pathfinding
 In Proceedings of the 4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE
"... Most games being shipped today use some form of highlevel abstraction such as a navmesh or waypoint graph for path planning. These structures can generally be represented in a form which is compact enough to meet the tight memory constraints in a game. But, when such a graph grows too large, findi ..."
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Cited by 15 (3 self)
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Most games being shipped today use some form of highlevel abstraction such as a navmesh or waypoint graph for path planning. These structures can generally be represented in a form which is compact enough to meet the tight memory constraints in a game. But, when such a graph grows too large, finding paths can still be a complex task. This challenge was faced in Dragon Age: Origins and solved by adding an additional level of abstraction. In the last few years a variety of novel approaches have been developed for finding optimal paths through graphs with specific design applications for road networks. Currently these techniques cannot be feasibly applied to the lowest detail of movement possible in a game map, but can be applied to the highlevel abstractions which are commonly found in games. In this paper we de
A.: Potential search: a boundedcost search algorithm
 In: Proceedings of the International Conference on Automated Planning and Scheduling
, 2011
"... Abstract In this paper we address the following search task: find a goal with cost smaller than or equal to a given fixed constant. This task is relevant in scenarios where a fixed budget is available to execute a plan and we would like to find such a plan while minimizing the search effort. We int ..."
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Cited by 13 (8 self)
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Abstract In this paper we address the following search task: find a goal with cost smaller than or equal to a given fixed constant. This task is relevant in scenarios where a fixed budget is available to execute a plan and we would like to find such a plan while minimizing the search effort. We introduce an algorithm called Potential search (PTS) which is specifically designed to solve this problem. PTS is a bestfirst search that expands nodes according to the probability that they will be part of a plan whose cost is less than or equal to the given budget. We show that it is possible to implement PTS even without explicitly calculating these probabilities, when a heuristic function and knowledge about the error of this heuristic function are given. In addition, we also show that PTS can be modified to an anytime search algorithm. Experimental results show that PTS outperforms other relevant algorithms in most cases, and is more robust.
AbstractionBased Heuristics with True Distance Computations
"... Pattern Databases (PDBs) are the most common form of memorybased heuristics, and they have been widely used in a variety of permutation puzzles and other domains. We explore the truedistance heuristics (TDHs) (also appeared in [Sturtevant et al., 2009]) which are a different form of memorybased h ..."
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Cited by 7 (6 self)
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Pattern Databases (PDBs) are the most common form of memorybased heuristics, and they have been widely used in a variety of permutation puzzles and other domains. We explore the truedistance heuristics (TDHs) (also appeared in [Sturtevant et al., 2009]) which are a different form of memorybased heuristics, designed to work in problem states where there isn’t a fixed goal state. Unlike PDBs, which build a heuristic based on distances in an abstract state space, TDHs store distances which are computed in the actual state space. We look in detail at how TDHs work, providing both theoretical and experimental motivation for their use. 1
Euclidean Heuristic Optimization
 PROCEEDINGS OF THE TWENTYFIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2011
"... We pose the problem of constructing good search heuristics as an optimization problem: minimizing the loss between the true distances and the heuristic estimates subject to admissibility and consistency constraints. For a wellmotivated choice of loss function, we show performing this optimization i ..."
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Cited by 7 (2 self)
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We pose the problem of constructing good search heuristics as an optimization problem: minimizing the loss between the true distances and the heuristic estimates subject to admissibility and consistency constraints. For a wellmotivated choice of loss function, we show performing this optimization is tractable. In fact, it corresponds to a recently proposed method for dimensionality reduction. We prove this optimization is guaranteed to produce admissible and consistent heuristics, generalizes and gives insight into differential heuristics, and show experimentally that it produces strong heuristics on problems from three distinct search domains.
Search space reduction using swamp hierarchies
 In AAAI
, 2010
"... In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include “swamps”—areas that cannot lie on the only available shortest path, and can thus sa ..."
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In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include “swamps”—areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an algorithm for detecting hierarchies of swamps, and exploiting them. Experiments support our claims of improved efficiency, showing significant reduction in search time.
A * Search with Inconsistent Heuristics
"... Early research in heuristic search discovered that using inconsistent heuristics with A * could result in an exponential increase in the number of node expansions. As a result, the use of inconsistent heuristics has largely disappeared from practice. Recently, inconsistent heuristics have been shown ..."
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Cited by 6 (3 self)
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Early research in heuristic search discovered that using inconsistent heuristics with A * could result in an exponential increase in the number of node expansions. As a result, the use of inconsistent heuristics has largely disappeared from practice. Recently, inconsistent heuristics have been shown to be effective in IDA*, especially when applying the bidirectional pathmax (BPMX) enhancement. This paper presents new worstcase complexity analysis of A*’s behavior with inconsistent heuristics, discusses how BPMX can be used with A*, and gives experimental results justifying the use of inconsistent heuristics in A * searches. 1
Subgoal Graphs for Optimal Pathfinding in EightNeighbor Grids ∗
"... Grids are often used to represent maps in video games. In this paper, we propose a method for preprocessing eightneighbor grids to generate subgoal graphs and show how subgoal graphs can be used to find shortest paths fast. We place subgoals at the corners of obstacles (similar to visibility graphs ..."
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Cited by 6 (2 self)
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Grids are often used to represent maps in video games. In this paper, we propose a method for preprocessing eightneighbor grids to generate subgoal graphs and show how subgoal graphs can be used to find shortest paths fast. We place subgoals at the corners of obstacles (similar to visibility graphs) and add those edges between subgoals that are necessary for finding shortest paths, while ensuring that each edge connects only subgoals that are easily reachable from one another. We describe a method for finding shortest paths by first finding highlevel paths through subgoals and then shortest lowlevel paths between consecutive subgoals on the highlevel path. Our method was one of ten entries in the GridBased Path Planning Competition 2012. Among all optimal path planners, ours was the fastest to find complete paths and required the least amount of memory.
TRANSIT Routing on Video Game Maps
"... TRANSIT (Bast, Funke, and Matijevic 2006) is a fast and optimal technique for computing shortest path costs in road networks. It is attractive for its usually modest memory requirements and impressive running times. In this paper we give a first analysis of TRANSIT routing on a set of popular gridb ..."
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Cited by 5 (2 self)
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TRANSIT (Bast, Funke, and Matijevic 2006) is a fast and optimal technique for computing shortest path costs in road networks. It is attractive for its usually modest memory requirements and impressive running times. In this paper we give a first analysis of TRANSIT routing on a set of popular gridbased videogame benchmarks taken from the AI pathfinding literature. We show that in the presence of path symmetries, which are inherent to most grids but normally not road networks, TRANSIT is strongly and negatively impacted, both in terms of performance and memory requirements. We address this problem by developing a new general symmetry breaking technique which adds small random ɛvalues to edges in the search graph, reducing the size of the TRANSIT network by up to 4 times while preserving optimality. Using our enhancements TRANSIT achieves up to four orders of magnitude speed improvement vs. A * search and uses in many cases only a small ( ≤ 10MB) or modest ( ≤ 50MB) amount of memory. We also compare TRANSIT with CPDs, a recent and very fast databasedriven pathfinding approach. We find the algorithms have complementary strengths but also identify a class of problems for which TRANSIT is up to two orders of magnitude faster than CPDs using a comparable amount of memory.