The 2014 international planning competition: Progress and trends
Abstract We review the 2014 International Planning Competition (IPC-2014), the eighth in a
series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess …
series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess …
Goal probability analysis in probabilistic planning: Exploring and enhancing the state of the art
Unavoidable dead-ends are common in many probabilistic planning problems, eg when
actions may fail or when operating under resource constraints. An important objective in …
actions may fail or when operating under resource constraints. An important objective in …
Non-stationary Markov decision processes, a worst-case approach using model-based reinforcement learning
This work tackles the problem of robust zero-shot planning in non-stationary stochastic
environments. We study Markov Decision Processes (MDPs) evolving over time and …
environments. We study Markov Decision Processes (MDPs) evolving over time and …
On monte carlo tree search and reinforcement learning
Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved wide-
spread adoption within the games community. Its links to traditional reinforcement learning …
spread adoption within the games community. Its links to traditional reinforcement learning …
Safe path planning for UAV urban operation under GNSS signal occlusion risk
This paper introduces a concept of safe path planning for UAV's autonomous operation in an
urban environment where GNSS-positioning may become unreliable or even unavailable. If …
urban environment where GNSS-positioning may become unreliable or even unavailable. If …
[PDF][PDF] A framework for reinforcement learning and planning
Two successful approaches to Markov Decision Process optimization are planning and
reinforcement learning. Both research communities operate large separate. This framework …
reinforcement learning. Both research communities operate large separate. This framework …
Nonlinear Hybrid Planning with deep net ltearned transition models and mixed-integer linear programming
In many real-world hybrid (mixed discrete continuous) planning problems such as Reservoir
Control, Heating, Ventilation and Air Conditioning (HVAC), and Navigation, it is difficult to …
Control, Heating, Ventilation and Air Conditioning (HVAC), and Navigation, it is difficult to …
Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
Abstract Building an Artificial Intelligence (AI) agent that can design on its own has been a
goal since the 1980s. Recently, deep learning has shown the ability to learn from large …
goal since the 1980s. Recently, deep learning has shown the ability to learn from large …
[BOOK][B] Autonomous horizons: the way forward
GL Zacharias - 2019 - apps.dtic.mil
Dr. Greg Zacharias, Chief Scientist of the United States Air Force 2015-18, explores next
steps in autonomous systems AS development, fielding, and training. Rapid advances in AS …
steps in autonomous systems AS development, fielding, and training. Rapid advances in AS …
[BOOK][B] Handbuch der künstlichen Intelligenz
G Görz, CR Rollinger, J Schneeberger - 2003 - degruyter.com
Liste der Autoren Page 1 Liste der Autoren Clemens Beckstein Gerhard Brewka Christian
Borgelt Wolfram Burgard Hans-Dieter Burkhard Stephan Busemann Thomas Christaller Leonie …
Borgelt Wolfram Burgard Hans-Dieter Burkhard Stephan Busemann Thomas Christaller Leonie …