Personalized Help for Optimizing Low-Skilled Users' Strategy
Feng Gu, Wichayaporn Wongkamjan, Jonathan K. Kummerfeld, Denis Peskoff, Jonathan May, Jordan Boyd-Graber
TL;DR
This work investigates how real-time AI guidance can aid humans in a complex, collaborative game. It introduces pholus, a personalized advisor that outputs both moves and messages tailored to a player's history in Diplomacy, and evaluates its impact through 12 online games with 41 players. Quantitative results show that move guidance positively affects performance, with the strongest gains when both move and message guidance are provided, especially for novices who can reach or approach veteran performance. The study demonstrates the potential of human–AI collaboration to accelerate learning in unfamiliar environments and outlines directions for modeling user intent and reducing overreliance while improving accessibility.
Abstract
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 superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it.
