We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTNMAKER takes as inpu...
Hierarchical state decompositions address the curse-ofdimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, w...
Erik G. Schultink, Ruggiero Cavallo, David C. Park...
Several researchers have shown that the efficiency of value iteration, a dynamic programming algorithm for Markov decision processes, can be improved by prioritizing the order of...
Many studies show that, when Internet links go up or down, the dynamics of BGP may cause several minutes of packet loss. The loss occurs even when multiple paths between the sende...
Nate Kushman, Srikanth Kandula, Dina Katabi, Bruce...
Two discrete-event simulations are developed to assess the feasibility of improving the delivery process of reinforced concrete structures. The simulations represent the resource ...
John-Michael Wong, Kristen Parrish, Iris D. Tommel...