The Workshop on Accelerating Artificial Intelligence for Embedded Autonomy aims at gathering researchers and practitioners in the fields of autonomy, automated reasoning, planning algorithms, and embedded systems to discuss the development of novel hardware architectures that can accelerate the wide variety of AI algorithms demanded by advanced autonomous and intelligent systems. Topics of interest include hardware architectures and design methodologies to accelerate: Applications based on deep learning, skill-level and instinctive autonomy based on deep reinforcement learning, storage and retrieval of facts in knowledge bases, logical reasoning methods such as deduction, search for classical planning algorithms and Hierarchical Task Networks (HTN), inference in probabilistic models such as Bayesian networks and probabilistic logic, planning algorithms for Markov Decision Processes (MDP), and planning algorithms for Partial Observable Markov Decision Processes (POMDP).
For more information, please visit AAIEA 2019 homepage.
Alessandro Pinto - United Technologies Research Center
Gerald Wang - United Technologies Research Center
Luca Carloni - Columbia University