Heuristic function

Results: 331



#Item
11Toward a Lifelong Motion Planning System that Learns from Experience Dmitry Berenson Pieter Abbeel

Toward a Lifelong Motion Planning System that Learns from Experience Dmitry Berenson Pieter Abbeel

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Source URL: www.cse.unr.edu

Language: English - Date: 2012-12-20 19:29:23
12Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic Jonathan D. Gammell1 , Siddhartha S. Srinivasa2 , and Timothy D. Barfoot1 Abstract— Rapidly-explori

Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic Jonathan D. Gammell1 , Siddhartha S. Srinivasa2 , and Timothy D. Barfoot1 Abstract— Rapidly-explori

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Source URL: www.ri.cmu.edu

Language: English - Date: 2014-07-22 11:25:05
13Generating and Evaluating Unsolvable STRIPS Planning Instances for Classical Planning Bachelor’s Thesis  Natural Science Faculty of the University of Basel

Generating and Evaluating Unsolvable STRIPS Planning Instances for Classical Planning Bachelor’s Thesis Natural Science Faculty of the University of Basel

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Source URL: ai.cs.unibas.ch

Language: English - Date: 2014-08-11 10:56:40
14A Normal Form for Classical Planning Tasks Florian Pommerening and Malte Helmert University of Basel Basel, Switzerland {florian.pommerening,malte.helmert}@unibas.ch

A Normal Form for Classical Planning Tasks Florian Pommerening and Malte Helmert University of Basel Basel, Switzerland {florian.pommerening,malte.helmert}@unibas.ch

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Source URL: ai.cs.unibas.ch

Language: English - Date: 2015-04-01 08:50:48
15Anytime Incremental Planning with E-Graphs Mike Phillips1 and Andrew Dornbush1 and Sachin Chitta2 and Maxim Likhachev1 Abstract— Robots operating in real world environments need to find motion plans quickly. Robot moti

Anytime Incremental Planning with E-Graphs Mike Phillips1 and Andrew Dornbush1 and Sachin Chitta2 and Maxim Likhachev1 Abstract— Robots operating in real world environments need to find motion plans quickly. Robot moti

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Source URL: www.cs.cmu.edu

Language: English - Date: 2013-02-26 12:06:48
16Online Generation and Use of Macro-Actions in Forward-Chaining Planning Amanda Smith  A Thesis submitted for the degree of Doctor of Philosophy

Online Generation and Use of Macro-Actions in Forward-Chaining Planning Amanda Smith A Thesis submitted for the degree of Doctor of Philosophy

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Source URL: www.inf.kcl.ac.uk

Language: English - Date: 2013-10-04 08:31:29
17A Pattern Database Approach for Solving the TopSpin Puzzle Problem Pier Paolo Bortoluzzi University of Basel Examinar: Malte Helmert Supervisor: Martin Wehrle

A Pattern Database Approach for Solving the TopSpin Puzzle Problem Pier Paolo Bortoluzzi University of Basel Examinar: Malte Helmert Supervisor: Martin Wehrle

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Source URL: ai.cs.unibas.ch

Language: English - Date: 2013-01-07 17:29:29
18A Beginner’s Introduction to Heuristic Search Planning 7. Going Further Malte Helmert

A Beginner’s Introduction to Heuristic Search Planning 7. Going Further Malte Helmert

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Source URL: ai.cs.unibas.ch

Language: English - Date: 2015-01-20 05:17:56
19Counterexample-guided Cartesian Abstraction Refinement Jendrik Seipp and Malte Helmert Universit¨at Basel Basel, Switzerland {jendrik.seipp,malte.helmert}@unibas.ch

Counterexample-guided Cartesian Abstraction Refinement Jendrik Seipp and Malte Helmert Universit¨at Basel Basel, Switzerland {jendrik.seipp,malte.helmert}@unibas.ch

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Source URL: ai.cs.unibas.ch

Language: English - Date: 2013-04-08 08:41:11
20Additive Counterexample-guided Cartesian Abstraction Refinement Jendrik Seipp and Malte Helmert Universit¨at Basel Basel, Switzerland {jendrik.seipp,malte.helmert}@unibas.ch

Additive Counterexample-guided Cartesian Abstraction Refinement Jendrik Seipp and Malte Helmert Universit¨at Basel Basel, Switzerland {jendrik.seipp,malte.helmert}@unibas.ch

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Source URL: ai.cs.unibas.ch

Language: English - Date: 2013-06-05 11:45:20