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Artificial intelligence: The 3 big trends to watch in 2017 - TechRepublic

#artificialintelligence

In 2016, the White House recognized the importance of AI at its Frontiers Conference. The concept of driverless cars became a reality, with Uber's self-driving fleet in Pittsburgh and Tesla's new models equipped with the hardware for full autonomy. Google's DeepMind platform, AlphaGo, beat the world champion of the game--10 years ahead of predictions. "Increasing use of machine learning and knowledge-based modeling methods" are major trends to watch in 2017, said Marie desJardins, associate dean and professor of computer science at the University of Maryland, Baltimore County. How will this play out?


Artificial intelligence: The 3 big trends to watch in 2017 - TechRepublic

#artificialintelligence

In 2016, the White House recognized the importance of AI at its Frontiers Conference. The concept of driverless cars became a reality, with Uber's self-driving fleet in Pittsburgh and Tesla's new models equipped with the hardware for full autonomy. Google's DeepMind platform, AlphaGo, beat the world champion of the game--10 years ahead of predictions. "Increasing use of machine learning and knowledge-based modeling methods" are major trends to watch in 2017, said Marie desJardins, associate dean and professor of computer science at the University of Maryland, Baltimore County. How will this play out?


Artificial intelligence: The 3 big trends to watch in 2017 - TechRepublic

#artificialintelligence

In 2016, the White House recognized the importance of AI at its Frontiers Conference. The concept of driverless cars became a reality, with Uber's self-driving fleet in Pittsburgh and Tesla's new models equipped with the hardware for full autonomy. Google's DeepMind platform, AlphaGo, beat the world champion of the game--10 years ahead of predictions. "Increasing use of machine learning and knowledge-based modeling methods" are major trends to watch in 2017, said Marie desJardins, associate dean and professor of computer science at the University of Maryland, Baltimore County. How will this play out?


Planning with Abstract Markov Decision Processes

AAAI Conferences

Robots acting in human-scale environments must plan under uncertainty in large state-action spaces and face constantly changing reward functions as requirements and goals change. Planning under uncertainty in large state-action spaces requires hierarchical abstraction for efficient computation. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level "flat" MDP . AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct. We present empirical results showing significantly improved planning speed, while maintaining solution quality, in the Taxi domain and in a mobile-manipulation robotics problem. Furthermore, our approach allows specification of a decision-making model for a mobile-manipulation problem on a Turtlebot, spanning from low-level control actions operating on continuous variables all the way up through high-level object manipulation tasks.