An agent with limited consumable execution resources needs policies that attempt to achieve good performance while respecting these limitations. Otherwise, an agent (such as a pla...
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
The policy optimization problem for dynamic power management has received considerable attention in the recent past. We formulate policy optimization as a constrained optimization...
We present a novel affective goal selection mechanism for decision-making in agents with limited computational resources (e.g., such as robots operating under real-time constraint...
Multi-agent planning in stochastic environments can be framed formally as a decentralized Markov decision problem. Many real-life distributed problems that arise in manufacturing,...