Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Heuristic search in restricted memory (research note)
Artificial Intelligence
Artificial Intelligence
Journal of the ACM (JACM)
Linear-space best-first search
Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Movement behavior for soldier agents on a virtual battlefield
Presence: Teleoperators and Virtual Environments - Fourth international workshop on presence
RTTES: Real-time search in dynamic environments
Applied Intelligence
Efficient triangulation-based pathfinding
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Deploying embodied AI into virtual worlds
Knowledge-Based Systems
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Explaining how to play real-time strategy games
Knowledge-Based Systems
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
Real-Time Edge Follow: A Real-Time Path Search Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A new hybrid navigation algorithm for mobile robots in environments with incomplete knowledge
Knowledge-Based Systems
Single-player Monte-Carlo tree search for SameGame
Knowledge-Based Systems
A Multi-objective Incremental Path Planning Algorithm for Mobile Agents
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Bi-level programming based real-time path planning for unmanned aerial vehicles
Knowledge-Based Systems
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Pathfinding algorithms used in todays computer games consider the path length or a similar criterion as the only measure of optimality. However, these games usually involve opposing parties, whose agents can inflict damage on those of the others. Therefore, the shortest path in such games may not always be the safest one. Consequently, a new suboptimal offline path search algorithm based on the A^* algorithm was developed, which takes the threat zones in the game map into consideration. Given an upper limit as the tolerable amount of damage for an agent, this algorithm searches for the shortest path from a starting location to a destination, where the agent may suffer damage less than or equal to the specified limit. Due to its behavior, the algorithm is called Limited-Damage A^* (LDA^*). Performance of LDA^* was tested in randomly-generated maze-like grid-based environments of varying sizes, and in hand-crafted fully-observable environments, in which 8-way movement is utilized. Results obtained from LDA^* are compared with those obtained from Multiobjective A^* (MOA^*), which is a complete and optimal algorithm that yields exact (best) solutions for every case. LDA^* was found to perform much faster than MOA^*, yielding acceptable sub-optimality in path length.