Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
Introduction to algorithms
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Linear-space best-first search
Artificial Intelligence
The power of a pebble: exploring and mapping directed graphs
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Utility-based on-line exploration for repeated navigation in an embedded graph
Artificial Intelligence
Ants: agents on networks, trees, and subgraphs
Future Generation Computer Systems
PHA*: performing A* in unknown physical environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Vertex-Ant-Walk – A robust method for efficient exploration of faulty graphs
Annals of Mathematics and Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Annals of Mathematics and Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Multi-Agent Physical A* Using Large Pheromones
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Utility-based multi-agent system for performing repeated navigation tasks
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
SIGACT news online algorithms column 8
ACM SIGACT News
Multi-agent Physical A* with Large Pheromones
Autonomous Agents and Multi-Agent Systems
Distributed navigation in an unknown physical environment
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Canadian traveler problem with remote sensing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Real-Time search algorithms for exploration and mapping
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Exploring an unknown environment with an intelligent virtual agent
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Finding patterns in an unknown graph
AI Communications - The Symposium on Combinatorial Search
Hi-index | 0.00 |
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 territory. We introduce the Physical-A* algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes that A* would expand and returns the shortest path between the two points. However. due to the physical nature of the problem, the complexity of the algorithm is measured by the traveling effort of the moving agent and not by the number of generated nodes, as in standard A*. PHA* is presented a a two-level algorithm, such that its high level, A*, chooses the next node to be expanded and its low level directs the agent to that node in order to explore it. We present a number of variations for both the high-level and low-level procedures and evaluate their performance theoretically and experimentally. We show that the travel cost of our best variation is fairly dose to the optimal travel cost, assuming that the mandatory nodes of A* are known in advance. We then generalize our algorithm to the multi-agent case, where a number of cooperative agents are designed to solve the problem. Specifically, we provide an experimental implementation for such a system. It should be noted that the problem addressed here is not a navigation problem, but rather a problem of finding the shortest path between two points for future usage.