This work studies efficient centralized solution methods for cluster-based control policies of
transition-independent Markov decision processes (TI-MDPs). We focus on control of multi …

This work studies efficient solution methods for cluster-based control policies of transition-
independent Markov decision processes (TI-MDPs). We focus on control of multi-agent …

We present the FlipDyn, a dynamic game in which two opponents (a defender and an
adversary) choose strategies to optimally takeover a resource that involves a dynamical …

In this work we investigate an importance sampling approach for evaluating policies for a
structurally time-varying factored Markov decision process (MDP), ie the policy's value is …

Identifying important agents is a key goal in study of multi-agent systems (MAS), but
standard graph-based centrality metrics often fail to account for the long-term influence of an …

We consider cluster-based control of agents mod-eled as a transition-independent Markov
decision process (MDP), and the objective of assigning agents to clusters to maximize the …

In an era where intricate cyber and physical systems are integrated into daily life, the
imperative for controlling and optimizing them emerges as a critical avenue. This avenue …

We present a multi-agent system where agents can cooperate to solve a system of
dependent tasks, with agents having the capability to explore a solution space and make …

We present a multi-agent system where agents can cooperate to solve a system of
dependent tasks, with agents having the capability to explore a solution space, make …

Many natural and engineered systems are the result of strategic, interacting agents making
decisions within physical or digital environments. To understand how the group behavior …