We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real ...
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Coevolution has often been based on averaged outcomes, resulting in unstable evaluation. Several theoretical approaches have used archives to provide stable evaluation. However, t...
We study a problem of scheduling client requests to servers. Each client has a particular latency requirement at each server and may choose either to be assigned to some server in ...
Ioannis Caragiannis, Christos Kaklamanis, Panagiot...