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New miss-MDP framework bridges missing data theory and POMDPs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new framework called missingness-MDPs (miss-MDPs) that integrates the theory of missing data into partially observable Markov decision processes (POMDPs). This novel subclass of POMDPs specifically addresses scenarios where observation functions are missing, detailing the probability of individual state features being unobserved. The work focuses on computing near-optimal policies for miss-MDPs with unknown missingness functions by learning from trajectory data, offering PAC algorithms that yield epsilon-optimal policies with high probability. AI

IMPACT Introduces a new theoretical framework for handling missing data in sequential decision-making problems, potentially improving AI agents' robustness in real-world scenarios.

RANK_REASON Academic paper introducing a novel theoretical framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New miss-MDP framework bridges missing data theory and POMDPs

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Academic paper introducing a novel theoretical framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nils Jansen ·

    Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs

    We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of missing data. A miss-MDP is a POMDP whose observation function is a missingness function, specifying the probability that individ…

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