Introduction to algorithms
Explanation-based learning: a problem solving perspective
Machine learning: paradigms and methods
Automating process discovery through event-data analysis
Proceedings of the 17th international conference on Software engineering
Machine Learning Methods for Planning
Machine Learning Methods for Planning
Using genetic programming to learn and improve control knowledge
Artificial Intelligence
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Programming by demonstration: a machine learning approach
Programming by demonstration: a machine learning approach
Learning Generalized Policies from Planning Examples Using Concept Languages
Applied Intelligence
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Enabling ScientificWorkflow Reuse through Structured Composition of Dataflow and Control-Flow
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Learning hierarchical task networks by observation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Bringing Semantics to Web Services with OWL-S
World Wide Web
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Proceedings of the 14th international conference on Intelligent user interfaces
Discovering frequent work procedures from resource connections
Proceedings of the 14th international conference on Intelligent user interfaces
Using Workflow Medleys to Streamline Exploratory Tasks
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Learning measures of progress for planning domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
HTN-MAKER: learning HTNs with minimal additional knowledge engineering required
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
POIROT: integrated learning of web service procedures
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
Learning domain-specific planners from example plans
Learning domain-specific planners from example plans
Scientific workflow design with data assembly lines
Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science
Learning probabilistic hierarchical task networks to capture user preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A context driven approach for workflow mining
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Process mining and petri net synthesis
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
The prom framework: a new era in process mining tool support
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
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A workflow is a model of a process that systematically describes patterns of activity. Workflows capture a sequence of operations, their enablement conditions, and data flow dependencies among them. It is hard to design a complete and correct workflow from scratch, while it is much easier for humans to demonstrate the solution than to state the solution declaratively. This article presents RECYCLE, our approach to learning workflow models from example demonstration traces. RECYCLE captures control flow, data flow, and enablement conditions of an underlying workflow process. Unlike prior work from workflow mining and AI planning literature, (1) RECYCLE can learn from a single demonstration trace with loops, (2) RECYCLE learns both loop and conditional branch structure, and (3) RECYCLE handles data flow among actions. In this article, we describe the phases of RECYCLE's learning algorithm: substructure analysis and node abstraction. To ground the discussion, we present a simplified flight reservation system with some of the important characteristics of the real domains we worked with. We present some results from a patient transport domain.