POIROT: acquiring workflows by combining models learned from interpreted traces

Authors:
Mark H. Burstein;Fusan Yaman;Robert M. Laddaga;Robert J. Bobrow
Affiliations:
BBN Technologies, Cambridge, MA, USA;BBN Technologies, Cambridge, MA, USA;BBN Technologies, Cambridge, MA, USA;BBN Technologies, Cambridge, MA, USA
Venue:
Proceedings of the fifth international conference on Knowledge capture
Year:
2009

Citing 20
Cited 1

Automating process discovery through event-data analysis

Proceedings of the 17th international conference on Software engineering
Event-based detection of concurrency

SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
Introspective multistrategy learning: on the construction of learning strategies

Artificial Intelligence
Introduction

Machine Learning - Special issue on multistrategy learning
A Heuristic Approach to the Discovery of Macro-Operators

Machine Learning
Mining Process Models from Workflow Logs

EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Learning k-Reversible Context-Free Grammars from Positive Structural Examples

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Interacting Learning-Goals: Treating Learning as a Planning Task

EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Integrating Machine Learning and Workflow Management to Support Acquisition and Adaptation of Workflow Models

DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Workflow Mining: Discovering Process Models from Event Logs

IEEE Transactions on Knowledge and Data Engineering
Dynamic Invocation of Semantic Web Services That Use Unfamiliar Ontologies

IEEE Intelligent Systems
Bringing Semantics to Web Services with OWL-S

World Wide Web
Democratic approximation of lexicographic preference models

Proceedings of the 25th international conference on Machine learning
POIROT: integrated learning of web service procedures

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
The effect of rule use on the utility of explanation-based learning

IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
SHOP: simple hierarchical ordered planner

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Recognizing plan/goal abandonment

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A context driven approach for workflow mining

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Relational macros for transfer in reinforcement learning

ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Mining hierarchies of models: from abstract views to concrete specifications

BPM'05 Proceedings of the 3rd international conference on Business Process Management

CARSA: A context-aware reasoning-based service agent model for AI planning of web service composition

Journal of Network and Computer Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The POIROT project is a four-year effort to develop an architecture that integrates the products of a number of targeted reasoning and learning components to produce executable representations of demonstrated web service workflow processes. To do this it combines contributions from multiple trace analysis (interpretation) and learning methods guided by a meta-control regime that reviews explicit learning hypotheses and posts new learning goals and internal learning subtasks. POIROT's meta-controller guides the activity of its components through largely distinct phases of processing from trace interpretation, to inductive learning, hypotheses combination and experimental evaluation. In this paper we discuss the impact that various kinds of inference during the trace interpretation phase can have on the quality of the learned models.