Helmert, Malte.
Understanding Planning Tasks Domain Complexity and Heuristic Decomposition / [electronic resource] : by Malte Helmert. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. - XIV, 270 p. online resource. - Lecture Notes in Computer Science, 4929 0302-9743 ; . - Lecture Notes in Computer Science, 4929 .
Planning Benchmarks -- The Role of Benchmarks -- Defining Planning Domains -- The Benchmark Suite -- Transportation and Route Planning -- IPC Domains: Transportation and Route Planning -- IPC Domains: Others -- Conclusions -- Fast Downward -- Solving Planning Tasks Hierarchically -- Translation -- Knowledge Compilation -- Search -- Experiments -- Discussion.
Action planning has always played a central role in Artificial Intelligence. Given a description of the current situation, a description of possible actions and a description of the goals to be achieved, the task is to identify a sequence of actions, i.e., a plan that transforms the current situation into one that satisfies the goal description. This monograph is a revised version of Malte Helmert's doctoral thesis, Solving Planning Tasks in Theory and Practice, written under the supervision of Professor Bernhard Nebel as thesis advisor at Albert-Ludwigs-Universität Freiburg, Germany, in 2006. The book contains an exhaustive analysis of the computational complexity of the benchmark problems that have been used in the past decade, namely the standard benchmark domains of the International Planning Competitions (IPC). At the same time, it contributes to the practice of solving planning tasks by presenting a powerful new approach to heuristic planning. The author also provides an in-depth analysis of so-called routing and transportation problems. All in all, this book will contribute significantly to advancing the state of the art in automatic planning.
9783540777236
10.1007/978-3-540-77723-6 doi
Computer science.
Computer software.
Artificial intelligence.
Computer Science.
Artificial Intelligence (incl. Robotics).
Algorithm Analysis and Problem Complexity.
Computation by Abstract Devices.
Probability and Statistics in Computer Science.
Q334-342 TJ210.2-211.495
006.3
Understanding Planning Tasks Domain Complexity and Heuristic Decomposition / [electronic resource] : by Malte Helmert. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. - XIV, 270 p. online resource. - Lecture Notes in Computer Science, 4929 0302-9743 ; . - Lecture Notes in Computer Science, 4929 .
Planning Benchmarks -- The Role of Benchmarks -- Defining Planning Domains -- The Benchmark Suite -- Transportation and Route Planning -- IPC Domains: Transportation and Route Planning -- IPC Domains: Others -- Conclusions -- Fast Downward -- Solving Planning Tasks Hierarchically -- Translation -- Knowledge Compilation -- Search -- Experiments -- Discussion.
Action planning has always played a central role in Artificial Intelligence. Given a description of the current situation, a description of possible actions and a description of the goals to be achieved, the task is to identify a sequence of actions, i.e., a plan that transforms the current situation into one that satisfies the goal description. This monograph is a revised version of Malte Helmert's doctoral thesis, Solving Planning Tasks in Theory and Practice, written under the supervision of Professor Bernhard Nebel as thesis advisor at Albert-Ludwigs-Universität Freiburg, Germany, in 2006. The book contains an exhaustive analysis of the computational complexity of the benchmark problems that have been used in the past decade, namely the standard benchmark domains of the International Planning Competitions (IPC). At the same time, it contributes to the practice of solving planning tasks by presenting a powerful new approach to heuristic planning. The author also provides an in-depth analysis of so-called routing and transportation problems. All in all, this book will contribute significantly to advancing the state of the art in automatic planning.
9783540777236
10.1007/978-3-540-77723-6 doi
Computer science.
Computer software.
Artificial intelligence.
Computer Science.
Artificial Intelligence (incl. Robotics).
Algorithm Analysis and Problem Complexity.
Computation by Abstract Devices.
Probability and Statistics in Computer Science.
Q334-342 TJ210.2-211.495
006.3