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 …
[图书][B] Algorithms for decision making
A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …
underlying mathematical problem formulations and the algorithms for solving them …
People construct simplified mental representations to plan
One of the most striking features of human cognition is the ability to plan. Two aspects of
human planning stand out—its efficiency and flexibility. Efficiency is especially impressive …
human planning stand out—its efficiency and flexibility. Efficiency is especially impressive …
On the convergence of projective-simulation–based reinforcement learning in Markov decision processes
WL Boyajian, J Clausen, LM Trenkwalder… - Quantum machine …, 2020 - Springer
In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …
tasks has significantly increased. Many algorithms speeding up supervised and …
A survey of point-based POMDP solvers
The past decade has seen a significant breakthrough in research on solving partially
observable Markov decision processes (POMDPs). Where past solvers could not scale …
observable Markov decision processes (POMDPs). Where past solvers could not scale …
[图书][B] Markov decision processes in artificial intelligence
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential
decision problems under uncertainty as well as reinforcement learning problems. Written by …
decision problems under uncertainty as well as reinforcement learning problems. Written by …
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 …
[图书][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 …
[图书][B] Planning with Markov decision processes: An AI perspective
A Kolobov - 2012 - books.google.com
Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling
sequential decision-making scenarios with probabilistic dynamics. They are the framework …
sequential decision-making scenarios with probabilistic dynamics. They are the framework …
PROST: Probabilistic planning based on UCT
T Keller, P Eyerich - Proceedings of the International Conference on …, 2012 - ojs.aaai.org
We present PROST, a probabilistic planning system that is based on the UCT algorithm by
Kocsis and Szepesvari (2006), which has been applied successfully to many areas of …
Kocsis and Szepesvari (2006), which has been applied successfully to many areas of …