Research map / Learning theory and algorithms / Combinatorial and search optimization
A Formal Metareasoning Model of Concurrent Planning and Execution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Allow base-level actions to execute while planning continues | Concurrent Planning and Execution (CoPE) · Airport example, deadline 30 | success probability: 0.25 -> 0.85 | Helped |
| Commit to an action before planning is complete | CoPE · Airport example, deadline 25 | success probability: zero -> 0.5 | Helped |
| Account for base-level execution when scheduling computation, rather than ignoring execution | CoPE algorithms on 15-puzzle instances · 15-puzzle, 20 processes, base-level action duration 3 | success probability | Helped |
| Plan base-level execution in advance rather than selecting execution only on demand | BGS adaptations to CoPE on 15-puzzle instances · 15-puzzle, base-level action duration 3 | success probability | Helped |
| Consider more action placements than Max-LET by using 2-Bounded | BGS adaptations to CoPE on 15-puzzle instances · 15-puzzle, base-level action duration 3 | success probability | Helped |
| Use demand-execution instead of planning execution in advance | BGS adaptations to CoPE on 15-puzzle instances · 15-puzzle, base-level action duration 3 | success probability | Hurt |
| Use demand-execution schemes instead of the more success-focused Max-LET and 2-Bounded schemes | CoPE algorithms on 15-puzzle instances · 15-puzzle, 20 processes, base-level action duration 3 | runtime | Helped |
| Solve the MDP directly using value iteration | CoPE · CoPE instances with more than 2 processes | runtime feasibility | Hurt |
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- Agent-Oriented Planning in Multi-Agent Systems (ICLR 2025 · similarity 0.84)
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