AI Game Programming Wisdom
AI Game Programming Wisdom
Introduction to Algorithms
Moving-Target Search: A Real-Time Search for Changing Goals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Controlling the learning process of real-time heuristic search
Artificial Intelligence
A Comparison of Fast Search Methods for Real-Time Situated Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Strategic team AI path plans: probabilistic pathfinding
International Journal of Computer Games Technology - Joint International Conference on Cyber Games and Interactive Entertainment 2006
Learning in real-time search: a unifying framework
Journal of Artificial Intelligence Research
Improving the learning efficiencies of realtime search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
CA Models for Target Searching Agents
Electronic Notes in Theoretical Computer Science (ENTCS)
Efficient minimal routing in the triangular grid with six channels
PaCT'11 Proceedings of the 11th international conference on Parallel computing technologies
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The design of appropriate moving target search (MTS) algorithms for computer-generated bots poses serious challenges as they have to satisfy stringent requirements that include computation and execution efficiency. In this paper, we investigate the performance and behaviour of existing moving target search algorithms when applied to search-and-capture gaming scenarios. As part of the investigation, we also introduce a novel algorithm known as abstraction MTS. We conduct performance simulations with a game bot and moving target within randomly generated mazes of increasing sizes and reveal that abstraction MTS exhibits competitive performance even with large problem spaces.