Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Preallocation and Planning Under Stochastic Resource Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| plan for stochastic resource limit P(X=x) instead of expected limit E(X) (ablation) | Preallocation MILP and CMDP · Search and rescue (SAR) | realized value | Helped |
| plan for stochastic resource limit P(X=x) instead of expected limit E(X) (ablation) | Preallocation MILP and CMDP · Search and rescue (SAR) | overcapacity frequency | Helped |
| plan for stochastic resource limit P(X=x) (ablation) | MILP and CMDP · SAR | planning runtime | Hurt |
| plan for stochastic limit P(X=x) instead of E(X) (ablation) | MILP and CMDP · TCL, 3 agents, increasing horizon | error from centralized upper bound | Helped |
| plan for stochastic limit P(X=x) instead of E(X) (ablation) | MILP and CMDP · TCL | violation frequency | Helped |
| plan for stochastic limit P(X=x) (ablation) | MILP and CMDP · TCL | runtime | Hurt |
| use MILP instead of CMDP | preallocation · SAR | overcapacity probability, value, runtime | Mixed |
| add periodic re-planning with communication gaps | CMDP · TCL, horizon 216, re-plan horizon 24 | number of violations | Helped |
| plan for stochastic constraint within re-planning, gaps of 3 steps (ablation) | CMDP re-planning · TCL | violations | Helped |
| increase number of agents (SAR) or horizon (TCL) | CMDP · SAR and TCL | runtime scaling | No clear effect |
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