Artificial Intelligence
Journal of the ACM (JACM)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Eighteenth national conference on Artificial intelligence
Artificial Intelligence
Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
International Journal of Applied Mathematics and Computer Science
Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion
Knowledge-Based Systems
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Path planning is a crucial issue in unknown environments where an autonomous mobile agent has to reach a particular destination from some initial location. There are several incremental algorithms such as D* [1], D* Lite [2] that are able to ensure reasonable paths in terms of path length in unknown environments. However, in many real-world problems we realize that path length is not only the sole objective. For example in computer games, a non-player character needs to not only find a minimum cost path to some target location but also minimize threat exposure. This means that path planning/finding activity of an agent in a multi-agent environment has to consider more than one objective to be achieved. In this paper, we propose a new incremental search algorithm called MOD* Lite extending Koenig's D* Lite algorithm and show that MOD* Lite is able to optimize path quality in more than one criteria that cannot be transformed to each other. Experimental results show that MOD* Lite is able to find optimal solutions and is fast enough to be used in real-world multi-agent applications such as robotics, computer games, or virtual simulations.