Paper Info
Title
Improving Domain-independent Planning via Critical Section Macro-Operators
Abstract
Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks. This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is "locked" (e.g., the robotic hand is holding an object) and thus "bridge" states in which the resource is locked and cannot be used. We also introduce an "aggressive" variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.
Year
Venue
Field
2019
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Programming language,Robotic hand,Computer science,Critical section,Operator (computer programming),Artificial intelligence,Macro,State space,Domain model,Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Lukáš Chrpa1206.19
Mauro Vallati221646.63