  The Multi-Agent Path Finding (MAPF) problem entails finding collision-free
paths for a set of agents, guiding them from their start to goal locations.
However, MAPF does not account for several practical task-related constraints.
For example, agents may need to perform actions at goal locations with specific
execution times, adhering to predetermined orders and timeframes. Moreover,
goal assignments may not be predefined for agents, and the optimization
objective may lack an explicit definition. To incorporate task assignment, path
planning, and a user-defined objective into a coherent framework, this paper
examines the Task Assignment and Path Finding with Precedence and Temporal
Constraints (TAPF-PTC) problem. We augment Conflict-Based Search (CBS) to
simultaneously generate task assignments and collision-free paths that adhere
to precedence and temporal constraints, maximizing an objective quantified by
the return from a user-defined reward function in reinforcement learning (RL).
Experimentally, we demonstrate that our algorithm, CBS-TA-PTC, can solve highly
challenging bomb-defusing tasks with precedence and temporal constraints
efficiently relative to MARL and adapted Target Assignment and Path Finding
(TAPF) methods.
