State estimation in multiagent settings involves updating an agent’s belief over the physical states and the space of other agents’ models. Performance of the previous approac...
We present a new method for carrying out state estimation in multiagent settings that are characterized by continuous or large discrete state spaces. State estimation in multiagen...
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
We address the problem of model checking hybrid systems which exhibit nontrivial discrete behavior and thus cannot be treated by considering the discrete states one by one, as most...
Werner Damm, Stefan Disch, Hardi Hungar, Jun Pang,...
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful ...