Research map / Multi-agent systems and games / Human-AI and goal-conditioned
An LP-Based Approach for Goal Recognition as Planning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine state equation and landmark constraints (ablation) | LP goal recognition · Optimal observations, all domains | agreement ratio: 0.79 -> 0.86 | Helped |
| Use post-hoc constraints instead of the state equation and landmark pair (ablation) | LP goal recognition · Optimal observations, all domains | agreement ratio: 0.86 -> 0.54 | Hurt |
| Add uncertainty-based goal inclusion (ablation) | LP goal recognition · Optimal observations, all domains | accuracy: 0.94 -> 0.95 | Helped |
| Add uncertainty-based goal inclusion (ablation) | LP goal recognition · Sub-optimal observations, all domains | accuracy: 0.93 -> 0.97 | Helped |
| Add uncertainty-based goal inclusion (ablation) | LP goal recognition · Sub-optimal observations, all domains | agreement ratio: 0.82 -> 0.75 | Hurt |
| Allow the model to ignore observations based on sensor unreliability (ablation) | LP goal recognition · Noisy observations, all domains | agreement ratio: 0.71 -> 0.73 | Helped |
| Use higher-information operator-counting constraints rather than post-hoc constraints (ablation) | LP goal recognition · Goal recognition tasks | agreement ratio | Helped |
Closest papers by meaning
- Landmark-Based Heuristics for Goal Recognition (AAAI 2017 · similarity 0.90)
- Active Goal Recognition (AAAI 2020 · similarity 0.89)
- Goal Recognition Design with Non-Observable Actions (AAAI 2016 · similarity 0.88)
- Stochastic Goal Recognition Design Problems with Suboptimal Agents (AAAI 2022 · similarity 0.87)
- Block-Level Goal Recognition Design (AAAI 2024 · similarity 0.84)
- Counting and Reasoning with Plans (AAAI 2025 · similarity 0.83)
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