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19 papers · 1 filter
Reinforcement Learning Agent Training with Goals for Real World Tasks
Xuan Zhao, Marcos Campos
Reinforcement Learning (RL) is a promising approach for solving various control, optimization, and sequential decision making tasks. However, designing reward functions for complex…
Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task
Ishan Tarunesh, Somak Aditya, Monojit Choudhury
The recent state-of-the-art natural language understanding (NLU) systems often behave unpredictably, failing on simpler reasoning examples. Despite this, there has been limited foc…
Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-making
Mikhail Jacob, Brian Magerko
Action selection from many options with few constraints is crucial for improvisation and co-creativity. Our previous work proposed creative arc negotiation to solve this problem, i…
Grounding Spatio-Temporal Language with Transformers
Tristan Karch, Laetitia Teodorescu, Katja Hofmann +2
Language is an interface to the outside world. In order for embodied agents to use it, language must be grounded in other, sensorimotor modalities. While there is an extended liter…
A Bayesian Approach to Identifying Representational Errors
Ramya Ramakrishnan, Vaibhav Unhelkar, Ece Kamar +1
Trained AI systems and expert decision makers can make errors that are often difficult to identify and understand. Determining the root cause for these errors can improve future de…
Scalable Anytime Planning for Multi-Agent MDPs
Shushman Choudhury, Jayesh K. Gupta, Peter Morales +1
We present a scalable tree search planning algorithm for large multi-agent sequential decision problems that require dynamic collaboration. Teams of agents need to coordinate decis…