Open-Ended SIPS blends proposal sampling and Bayesian inverse planning for real-time goal inference
xuanalogue · x · 2024-07-25
Open-Ended SIPS is presented as a sequential Monte Carlo algorithm for open-ended goal inference, combining particle-filter-style approximate inference with boundedly rational Bayesian inverse planning.
- The method samples plausible goals bottom-up with sequence models, then refines them with top-down reasoning.
- The paper argues this better matches how humans infer goals in familiar settings.
- It is also practical: the authors report real-time execution on a single laptop CPU, with inference taking about 0.08–0.95 s per observation.
- The accompanying figure frames the problem as Bayesian inverse planning over boundedly rational agents, where exact simulation over many goals becomes expensive.
Related event: New research models open-ended goal inference as Bayesian filtering(4 posts)→
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