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ENTITY Partially observable Markov decision processes and performance sensitivity analysis

Partially observable Markov decision processes and performance sensitivity analysis

PulseAugur coverage of Partially observable Markov decision processes and performance sensitivity analysis — every cluster mentioning Partially observable Markov decision processes and performance sensitivity analysis across labs, papers, and developer communities, ranked by signal.

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  1. TOOL · CL_154073 ·

    New framework unifies uncertainty reduction and reward in POMDPs

    Researchers have developed a new framework for Partially Observable Markov Decision Processes (POMDPs) that directly rewards uncertainty reduction. This approach, termed minimizing Expected Free Energy (EFE), is shown t…

  2. TOOL · CL_93300 ·

    New PO-PDDL formulation enables LLM-friendly robot planning under uncertainty

    Researchers have developed PO-PDDL, a new symbolic formulation for Partially Observable Markov Decision Processes (POMDPs) that maintains the relational structure of PDDL while explicitly modeling partial observability …

  3. COMMENTARY · CL_88317 ·

    ReAct Pattern Enhances LLM Reasoning and Action Capabilities

    The ReAct Pattern is a design pattern for Large Language Models (LLMs) that enhances their reasoning and action capabilities in complex environments. It enables LLMs to perceive, reason, and act, allowing them to learn …

  4. RESEARCH · CL_68129 ·

    POMDP value functions characterized as semi-algebraic sets

    Researchers have characterized the feasible set of value functions in partially observable Markov decision processes (POMDPs) as a semi-algebraic set. This extends previous work on fully observable processes, revealing …

  5. TOOL · CL_34515 ·

    New framework synthesizes POMDP policies using sampling and model-checking

    Researchers have developed a new framework to synthesize policies for Partially Observable Markov Decision Processes (POMDPs), which are used for decision-making under uncertainty. This approach combines sampling-based …

  6. TOOL · CL_25564 ·

    New MCTS analysis offers theoretical guarantees for POMDP planning

    Researchers have developed a new finite-time analysis for Monte Carlo Tree Search (MCTS) when applied to Partially Observable Markov Decision Processes (POMDPs). This work provides probabilistic concentration bounds for…