ALLAN: Stabilizing Hostilities through Arbitration and Diplomatic Engagement

Project funded by DARPA
PI: Jordan Boyd-Graber

Using a gaming environment based on the classic board game Diplomacy, SHADE will train and evaluate automated and artificial intelligence (AI)-assisted techniques that can identify and address the complications of multi-party negotiation with deception, collusion, profiling, and other real-world features.

Diplomatic negotiation is complex and requires a detailed, in-depth understanding of the positions and interactions of many potentially untrustworthy allies and adversaries. Successful diplomats must identify deceptions, explore valid courses of action, and assess the consequences of any diplomatic actions.

The UMD team is working to build datasets that are annotated for deception in online conversations and build agents that communicate using natural language.

	Jordan Boyd-Graber Associate Professor, Computer Science (UMD)
	Jonathan Kummerfeld PI, Sydney
	Jonathan May PI, USC ISI
	Karthik Narasimhan PI, Princeton
	Denis Peskoff Postdoc, Princeton
	Brandon Stewart PI, Princeton

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• Denis Peskoff and Brandon Stewart. Credible without Credit: Domain Experts Assess Generative Language Models, ACL 2023. ~~Pubs:SHADE~~

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the researchers and do not necessarily reflect the views of the sponsor.