Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Artificial Intelligence
The trailblazer search: a new method for searching and capturing moving targets
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Real-Time Bidirectional Search: Coordinated Problem Solving in Uncertain Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving-Target Search: A Real-Time Search for Changing Goals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
Real-Time Search for Autonomous Agents and Multiagent Systems
Autonomous Agents and Multi-Agent Systems
Controlling the learning process of real-time heuristic search
Artificial Intelligence
Performance simulations of moving target search algorithms
International Journal of Computer Games Technology - Artificial Intelligence for Computer Games
Utility-based on-line exploration for repeated navigation in an embedded graph
Artificial Intelligence
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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The capability of learning is one of the salient features of realtime search algorithms such as LRTA*. The major impediment is, however, the instability of the solution quality during convergence: (1) they try to find all optimal solutions even after obtaining fairly good solutions, and (2) they tend to move towards unexplored areas thus failing to balance exploration and exploitation. We propose and analyze two new realtime search algorithms to stabilize the convergence process. Ɛ-search (weighted realtime search) allows suboptimal solutions with Ɛ error to reduce the total amount of learning performed. δ-search (realtime search with upper bounds) utilizes the upper bounds of estimated costs, which become available after the problem is solved once. Guided by the upper bounds, δ-search can better control the tradeoff between exploration and exploitation.