We propose a framework for verifiable and compositional reinforcement
le...
In inverse reinforcement learning (IRL), a learning agent infers a rewar...
We consider parametric Markov decision processes (pMDPs) that are augmen...
Probabilistic model checking aims to prove whether a Markov decision pro...
We develop a probabilistic control algorithm, , for swarms
of agents wit...
We propose a novel framework for verifiable and compositional reinforcem...
We study the problem of inverse reinforcement learning (IRL), where the
...
We study the problem of minimizing the resource capacity of autonomous a...
Uncertain partially observable Markov decision processes (uPOMDPs) allow...
We study the distributed synthesis of policies for multi-agent systems t...
We consider a discrete-time linear-quadratic Gaussian control problem in...
We consider a discrete-time linear-quadratic Gaussian control problem in...
We study the synthesis of policies for multi-agent systems to implement
...
We consider Markov decision processes (MDPs) in which the transition
pro...
We synthesize shared control protocols subject to probabilistic temporal...
Deception plays a key role in adversarial or strategic interactions for ...
Progressively intricate cyber infiltration mechanisms have made conventi...
We study the problem of synthesizing a policy that maximizes the entropy...
We develop a method for computing policies in Markov decision processes ...
We formalize synthesis of shared control protocols with correctness
guar...