activity
20172026
most citedSurprise-Based Intrinsic Motivation for Deep Reinforcement Learning

99 citations · 100 across the 18 of their papers we have counts for

collaborators

22 papers

cs.GT2026

Dynamic Auction Mechanisms in a Closed-Credit Economy: Sequential Slot Reallocation in Air Traffic Management

Jihun Kim, Pan-Yang Su, Maria G. Mendoza +3

Indivisible resources allocated in advance may need to be reallocated when capacity reductions make existing assignments infeasible, yet monetary compensation is often impractical…

cs.MA2026

Persistent LLM Orchestration of Heterogeneous Robot Teams via Partial Delegation to Multi-Agent Protocols

Maria G. Mendoza, Victoria M. Tuck, Zachary Ravichandran +2

Natural language specifications let non-expert users task robot teams, and large language models (LLMs) have recently proven effective at grounding such semantic specifications int…

cs.MA2026

Orchestrating Level- Policies Against Unknown Opponents in Partially-Observable Dynamic Games

Addison Kalanther, Sanika Bharvirkar, Daniel Bostwick +2

Level- reasoning generates a hierarchy of policies specialized to opponents with different reasoning levels. When an opponent's level is unknown, a common deployment rule estima…

cs.GT2026

Congestion Games with Heterogeneous Valuations: An Optimal Transport Approach

Pan-Yang Su, Negar Mehr, Shankar Sastry

In emerging urban mobility and logistics applications, such as advanced air mobility, electric vehicle charging, and shared service systems, agents with heterogeneous valuations ch…

cs.MA2026

Hierarchical Generative Agents for Simulating Sequential Human Behavior

Maria G. Mendoza, Lucas Waldburger, Jin Lee +1

Complex cognitive, emotional, and social processes shape human evacuations during natural disasters. Accurate modeling and understanding of human behavior in disasters or emergenci…

eess.SY2026

Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach

Maria G. Mendoza, Pan-Yang Su, Bryce L. Ferguson +1

We study dynamic multi-robot task allocation under uncertain task completion, time-window constraints, and incomplete information. Tasks arrive online over a finite horizon and mus…