Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
AI Magazine
Speeding up the Convergence of Real-Time Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Controlling the learning process of real-time heuristic search
Artificial Intelligence
Comparing real-time and incremental heuristic search for real-time situated agents
Autonomous Agents and Multi-Agent Systems
Learning in real-time search: a unifying framework
Journal of Artificial Intelligence Research
On learning in agent-centered search
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
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Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. Recently, a different approach, RIBS, was suggested which, instead of learning costs to the goal, learns costs from the start state. RIBS can solve some problems faster, but in other problems has poor performance. We present a new algorithm, f-cost Learning Real-Time A* (f-LRTA*), which combines both approaches, simultaneously learning distances from the start and heuristics to the goal. An empirical evaluation demonstrates that f-LRTA* outperforms both RIBS and LRTA*-style approaches in a range of scenarios.