We investigate a mixed economy of an individual rational expert and several na¨ıve near-sighted agents in the context of security decision making. Agents select between three ca...
Jens Grossklags, Benjamin Johnson, Nicolas Christi...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
At present there exists a large gap in size, performance, adaptability and robustness between natural and artificial information processors for performing coherent perception-act...
A problem of planning for cooperative teams under uncertainty is a crucial one in multiagent systems. Decentralized partially observable Markov decision processes (DECPOMDPs) prov...