Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, r...
Service-oriented computing paradigm encourages the use of dynamic binding of application requirements to the resources needed to fulfill application tasks. Especially in pervasive...
Nanjangud C. Narendra, Umesh Bellur, S. K. Nandy, ...
We apply a constrained Hidden Markov Model architecture to the problem of simultaneous localization and surveying from sensor logs of mobile agents navigating in unknown environmen...
Microprocessor design is undergoing a major paradigm shift towards multi-core designs, in anticipation that future performance gains will come from exploiting threadlevel parallel...
Richard A. Hankins, Gautham N. Chinya, Jamison D. ...