Decision Making
2070 papers with code • 1 benchmarks • 38 datasets
Decision Making is a complex task that involves analyzing data (of different level of abstraction) from disparate sources and with different levels of certainty, merging the information by weighing in on some data source more than other, and arriving at a conclusion by exploring all possible alternatives.
Source: Complex Events Recognition under Uncertainty in a Sensor Network
Benchmarks
These leaderboards are used to track progress in Decision Making
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Libraries
Use these libraries to find Decision Making models and implementations
D4RL
Charades-STA
FairFace
VizDoom
TORCS
LogiQA
ShARC
GazeFollow
Latest papers with no code
Reevaluating coexistence and stability in ecosystem networks to address ecological transients: methods and implications
Here, we develop and demonstrate a new framework for representing ecosystems without considering equilibrium dynamics.
Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making
EISE and MPT are collaboratively trained, enabling EISE to autonomously learn and extract patterns from environmental data, thereby forming semantic representations that MPT could more effectively interpret and utilize for motion planning.
Bias Mitigation via Compensation: A Reinforcement Learning Perspective
Group dynamics might require that one agent (e. g., the AI system) compensate for biases and errors in another agent (e. g., the human), but this compensation should be carefully developed.
Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
The application of deep learning models in medical diagnosis has showcased considerable efficacy in recent years.
Large Language Model Agent for Fake News Detection
This adaptability enables FactAgent's application to news verification across various domains.
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models
In this paper, we study Imitation Learning from Observation with pretrained models and find existing approaches such as BCO and AIME face knowledge barriers, specifically the Embodiment Knowledge Barrier (EKB) and the Demonstration Knowledge Barrier (DKB), greatly limiting their performance.
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making.
IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence
Traffic congestion due to road incidents poses a significant challenge in urban environments, leading to increased pollution, economic losses, and traffic congestion.
Reduced-Rank Multi-objective Policy Learning and Optimization
However, if causal effects are heterogeneous and predictable from covariates, personalized treatment decisions can improve individual outcomes and contribute to both efficiency and equity.
Work Smarter...Not Harder: Efficient Minimization of Dependency Length in SOV Languages
Dependency length minimization is a universally observed quantitative property of natural languages.
