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Found 13 papers, 3 papers with code
Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search
While the study of unit-cost Multi-Agent Pathfinding (MAPF) problems has been popular, many real-world problems require continuous time and costs due to various movement models.
Tightest Admissible Shortest Path
This is a generalization of the shortest path problem to bounded uncertainty, where edge-weight uncertainty can be traded for computational cost.
A Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates
The shortest path problem in graphs is a cornerstone of AI theory and applications.
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other.
Multi-Agent Path Finding with Deadlines
We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL).
Multi-Agent Path Finding with Deadlines: Preliminary Results
We formalize the problem of multi-agent path finding with deadlines (MAPF-DL).
Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants
In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents.
Rational Deployment of Multiple Heuristics in IDA*
Recent advances in metareasoning for search has shown its usefulness in improving numerous search algorithms.
Theta*: Any-Angle Path Planning on Grids
Angle-Propagation Theta* achieves a better worst-case complexity per vertex expansion than Basic Theta* by propagating angle ranges when it expands vertices, but is more complex, not as fast and finds slightly longer paths.
Predicting the Performance of IDA* using Conditional Distributions
In this paper we show that, in addition to requiring the heuristic to be consistent, their formulas predictions are accurate only at levels of the brute-force search tree where the heuristic values obey the unconditional distribution that they defined and then used in their formula.
BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm
Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems.
Towards Rational Deployment of Multiple Heuristics in A*
The obvious way to use several admissible heuristics in A* is to take their maximum.
