Formalizes CT-TAPF problem and introduces CT-TCBS optimal solver using incremental expansion for team formation plus task-centric sub-optimal solvers that improve efficiency over agent-centric baselines.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.MA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAGR-BB matches exhaustive search accuracy on multi-agent Blocksworld while reducing hypothesis evaluations by orders of magnitude via RL scoring inside factorized branch-and-bound.
citing papers explorer
-
Multi-Agent Cooperative Transportation: Optimal and Efficient Task Allocation and Path Finding
Formalizes CT-TAPF problem and introduces CT-TCBS optimal solver using incremental expansion for team formation plus task-centric sub-optimal solvers that improve efficiency over agent-centric baselines.
-
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
MAGR-BB matches exhaustive search accuracy on multi-agent Blocksworld while reducing hypothesis evaluations by orders of magnitude via RL scoring inside factorized branch-and-bound.