2 citations · 4 across the 6 of their papers we have counts for
6 papers
Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL
Wichayaporn Wongkamjan, Yanze Wang, Feng Gu +4
An increasingly common socio-technical problem is people being taken in by offers that sound ``too good to be true'', where persuasion and trust shape decision-making. This paper i…
Personalized Help for Optimizing Low-Skilled Users' Strategy
Feng Gu, Wichayaporn Wongkamjan, Jonathan K. Kummerfeld +3
AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhu…
What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models
Navapat Nananukul, Wichayaporn Wongkamjan
Role-playing games (RPGs) provide players with a rich, interactive world to explore. Dialogue serves as the primary means of communication between developers and players, manifesti…
More Victories, Less Cooperation: Assessing Cicero's Diplomacy Play
Wichayaporn Wongkamjan, Feng Gu, Yanze Wang +6
The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence. The most prominent communicative Diplomacy AI, Cicero, has excellent str…
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Xiyao Wang, Ruijie Zheng, Yanchao Sun +4
Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for d…
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Xiyao Wang, Wichayaporn Wongkamjan, Furong Huang
Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning.…