Papers Decision Making Under Uncertainty
“Decision Making Under Uncertainty” 태그가 달린 논문 263편 · 필터 해제
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
Multi-armed bandit (MAB) is a widely adopted framework for sequential decision-making under uncertainty. Traditional bandit algorithms rely solely on online data, which tends to be scarce as it must be gathered during th…
Decision Making Under UncertaintyMulti-Armed BanditsSequential Decision MakingToward Explainable Offline RL: Analyzing Representations in Intrinsically Motivated Decision Transformers
Elastic Decision Transformers (EDTs) have proved to be particularly successful in offline reinforcement learning, offering a flexible framework that unifies sequence modeling with decision-making under uncertainty. Recen…
Decision MakingDecision Making Under UncertaintyOffline RLThe Gittins Index: A Design Principle for Decision-Making Under Uncertainty
The Gittins index is a tool that optimally solves a variety of decision-making problems involving uncertainty, including multi-armed bandit problems, minimizing mean latency in queues, and search problems like the Pandor…
Bayesian OptimizationDecision MakingDecision Making Under UncertaintyQ-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments
This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for "Quantum Adaptive Reward-Driven Neural Sim…
Autonomous NavigationCollision AvoidanceComputational EfficiencyDecision Making Under UncertaintyCognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of intere…
Decision MakingDecision Making Under UncertaintyDisaster ResponseSynthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks
Time series forecasting plays a critical role in domains such as energy, finance, and healthcare, where accurate predictions inform decision-making under uncertainty. Although Transformer-based models have demonstrated s…
BenchmarkingDecision Making Under UncertaintyInductive BiasTime Series+1Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review
Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action Models have opened new avenues for embodied AI in mobile serv…
Decision Making Under UncertaintySensor FusionVision-Language-ActionComparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Tasks
Large language models (LLMs) are increasingly used to simulate or automate human behavior in complex sequential decision-making tasks. A natural question is then whether LLMs exhibit similar decision-making behavior to h…
Decision MakingDecision Making Under UncertaintySequential Decision Making\textsc{rfPG}: Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs
Partially observable Markov decision processes (POMDPs) model specific environments in sequential decision-making under uncertainty. Critically, optimal policies for POMDPs may not be robust against perturbations in the …
Decision Making Under UncertaintySequential Decision MakingA Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue
Mobile crowdsensing is evolving beyond traditional human-centric models by integrating heterogeneous entities like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Optimizing task allocation among the…
Decision Making Under UncertaintyMulti-agent Reinforcement LearningSequential Decision MakingRecovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints
Rational decision-making under uncertainty requires coherent degrees of belief in events. However, event probabilities generated by Large Language Models (LLMs) have been shown to exhibit incoherence, violating the axiom…
Decision Making Under UncertaintyLanguage ModelingLanguage ModellingLarge Language ModelICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
Two-stage stochastic programming (2SP) offers a basic framework for modelling decision-making under uncertainty, yet scalability remains a challenge due to the computational complexity of recourse function evaluation. Ex…
Decision Making Under UncertaintyPrimal-dual algorithm for contextual stochastic combinatorial optimization
This paper introduces a novel approach to contextual stochastic optimization, integrating operations research and machine learning to address decision-making under uncertainty. Traditional methods often fail to leverage …
Combinatorial OptimizationDecision Making Under UncertaintyImitation LearningStochastic OptimizationLearning Symbolic Persistent Macro-Actions for POMDP Solving Over Time
This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertainty with macro-actions. Our method lever…
Computational EfficiencyDecision MakingDecision Making Under UncertaintyInductive logic programming+1LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
Partially Observable Markov Decision Processes (POMDPs) model decision making under uncertainty. While there are many approaches to approximately solving POMDPs, we aim to address the problem of learning such models. In …
Decision MakingDecision Making Under UncertaintyProgram inductionWasserstein Distributionally Robust Regret Optimization
Distributionally Robust Optimization (DRO) is a popular framework for decision-making under uncertainty, but its adversarial nature can lead to overly conservative solutions. To address this, we study ex-ante Distributio…
Decision Making Under UncertaintyMeasures of Variability for Risk-averse Policy Gradient
Risk-averse reinforcement learning (RARL) is critical for decision-making under uncertainty, which is especially valuable in high-stake applications. However, most existing works focus on risk measures, e.g., conditional…
Decision MakingDecision Making Under UncertaintyTowards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making
The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncertainty. My work focuses on reinforcement learning (RL), multi-armed bandi…
Decision MakingDecision Making Under UncertaintyGeneral Reinforcement LearningMulti-Armed Bandits+5Efficient Portfolio Selection through Preference Aggregation with Quicksort and the Bradley--Terry Model
How to allocate limited resources to projects that will yield the greatest long-term benefits is a problem that often arises in decision-making under uncertainty. For example, organizations may need to evaluate and selec…
Decision Making Under UncertaintyCounterfactual Inference under Thompson Sampling
Recommender systems exemplify sequential decision-making under uncertainty, strategically deciding what content to serve to users, to optimise a range of potential objectives. To balance the explore-exploit trade-off suc…
Causal InferencecounterfactualCounterfactual InferenceDecision Making+4