11 citations · 36 across the 11 of their papers we have counts for
6 papers · 1 filter
Human-Guided Complexity-Controlled Abstractions
Andi Peng, Mycal Tucker, Eoin Kenny +3
Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concep…
Towards True Lossless Sparse Communication in Multi-Agent Systems
Seth Karten, Mycal Tucker, Siva Kailas +1
Communication enables agents to cooperate to achieve their goals. Learning when to communicate, i.e., sparse (in time) communication, and whom to message is particularly important…
Prototype Based Classification from Hierarchy to Fairness
Mycal Tucker, Julie Shah
Artificial neural nets can represent and classify many types of data but are often tailored to particular applications -- e.g., for "fair" or "hierarchical" classification. Once an…
Probe-Based Interventions for Modifying Agent Behavior
Mycal Tucker, William Kuhl, Khizer Shahid +3
Neural nets are powerful function approximators, but the behavior of a given neural net, once trained, cannot be easily modified. We wish, however, for people to be able to influen…
Interpretable Learned Emergent Communication for Human-Agent Teams
Seth Karten, Mycal Tucker, Huao Li +3
Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents…
Emergent Discrete Communication in Semantic Spaces
Mycal Tucker, Huao Li, Siddharth Agrawal +4
Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do al…