Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
In artificial intelligence and pervasive computing research, inferring users' high-level goals from activity sequences is an important task. A major challenge in goal recogni...
We propose Alternating-time Dynamic Logic (ADL) as a multi-agent variant of Dynamic Logic in which atomic programs are replaced by coalitions. In ADL, the Dynamic Logic operators ...
Weighted voting games are a natural and practically important class of simple coalitional games, in which each agent is assigned a numeric weight, and a coalition is deemed to be ...
Piotr Faliszewski, Edith Elkind, Michael Wooldridg...
Whenever rational agents form coalitions to execute tasks, doing so via a decentralized negotiation process—while more robust and democratic—may lead to a loss of efficiency ...
Georgios Chalkiadakis, Edith Elkind, Maria Polukar...