Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Artificial Intelligence
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
The power of a pebble: exploring and mapping directed graphs
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Multi-agent Physical A* with Large Pheromones
Autonomous Agents and Multi-Agent Systems
PHA*: finding the shortest path with A* in an unknown physical environment
Journal of Artificial Intelligence Research
Finding patterns in an unknown graph
AI Communications - The Symposium on Combinatorial Search
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We address the problem of finding the shortest path between two points in an unknown real physical environment, where a traveling agent must move around in the environment to explore unknown territories. We present the Physical-A* algorithm (PHA*) to solve such a problem. PHA* is a two-level algorithm in which the upper level is A*, which chooses the next node to expand and the lower level directs the agent to that node in order to explore it. The complexity of this algorithm is measured by the traveling effort of the moving agent and not by the number of generated nodes as in classical A*. We present a number of variations of both the upper level and lower level algorithms and compare them both experimentally and theoretically. We then generalize our algorithm to the multi-agent case where a number of cooperative agents are designed to solve this problem and show experimental implementation for such a system.