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Found 15 papers, 4 papers with code
Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-Off
Regulation is increasingly cited as the most important and pressing concern in machine learning.
Explainable deep learning improves human mental models of self-driving cars
We anticipate our method could be applied to other safety-critical systems with a human in the loop, such as autonomous drones and robotic surgeons.
Adaptive Language-Guided Abstraction from Contrastive Explanations
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations.
Enhancing Preference-based Linear Bandits via Human Response Time
Theoretical and empirical analyses show that for queries with strong preferences, response times complement choices by providing extra information about preference strength, leading to significantly improved utility estimation.
Learning with Language-Guided State Abstractions
We describe a framework for using natural language to design state abstractions for imitation learning.
Preference-Conditioned Language-Guided Abstraction
We observe that how humans behave reveals how they see the world.
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models.
Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors.
Learning Household Task Knowledge from WikiHow Descriptions
Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment.
On Memory Mechanism in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
In this paper we instead propose to learn to predict path quality from crowdsourced human assessments.
Bayesian Inference of Temporal Task Specifications from Demonstrations
When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task.
Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators
The dual quaternion algebra is used to model the agents' poses and also in the distributed control laws, making the proposed technique easily applicable to time-varying formation control of general robotic systems.
Robotics Optimization and Control
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection.
Fairness in Multi-Agent Sequential Decision-Making
We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy.
