Real-time search for learning autonomous agents
Real-time search for learning autonomous agents
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
A Multiagent Approach to Qualitative Landmark-Based Navigation
Autonomous Robots
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
Are many reactive agents better than a few deliberative ones?
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Comparison of different grid abstractions for pathfinding on maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
C3LRTA*, color code coordinated LRTA* algorithm
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
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This paper introduces a Vision based Learning real-time A* (VLRTA*) search algorithm where target position is unknown and unpredictable by agents in partially unknown/dynamic environment. We have mapped human vision for agents, which is omni directional vision but in a single direction at some point in time. Agents can not see through obstacles so vision can be blocked due to hurdles in search space. The proposed algorithm has been applied to solve randomly generated mazes with multiple agents. We have evaluated this algorithm on a large number of test cases with random obstacles and varying obstacle ratio. Through Experimental evaluations, we have shown that our suggested vision technique is effective in both locate target time and solution quality. Moreover, the strategy used in Vision Based LRTA* becomes more efficient if the number of agents is increased with proportion to obstacle ratio.