Decision Making Under Uncertainty
43 papers with code • 0 benchmarks • 2 datasets
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Libraries
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xView3-SAR
Risk-Aware Planning Dataset
Latest papers with no code
EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure
In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid.
Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem.
Solving Long-run Average Reward Robust MDPs via Stochastic Games
First, we derive new computational complexity bounds for solving long-run average reward polytopic RMDPs, showing for the first time that the threshold decision problem for them is in NP coNP and that they admit a randomized algorithm with sub-exponential expected runtime.
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Exploration with Uncertain Semantic Data
To this end, we propose a robust Bayesian inference process for OA-CMABs that is based on the concept of probabilistic data validation.
Modeling Boundedly Rational Agents with Latent Inference Budgets
We study the problem of modeling a population of agents pursuing unknown goals subject to unknown computational constraints.
Learning Successor Features with Distributed Hebbian Temporal Memory
This paper presents a novel approach to address the challenge of online temporal memory learning for decision-making under uncertainty in non-stationary, partially observable environments.
Partially Observable Stochastic Games with Neural Perception Mechanisms
Stochastic games are a well established model for multi-agent sequential decision making under uncertainty.
Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality
Structure learning is essential for Bayesian networks (BNs) as it uncovers causal relationships, and enables knowledge discovery, predictions, inferences, and decision-making under uncertainty.
Measurement Simplification in ρ-POMDP with Performance Guarantees
In both cases we show a significant speed-up in planning with performance guarantees.
Safe POMDP Online Planning via Shielding
POMDP online planning algorithms such as Partially Observable Monte-Carlo Planning (POMCP) can solve very large POMDPs with the goal of maximizing the expected return.
