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1 POIROT – Integrated Learning of Web Service Procedures
BibTeX
@MISC{Burstein_1poirot,
author = {Mark Burstein and Robert Laddaga and David Mcdonald and Michael Cox and Brett Benyo and Paul Robertson and Talib Hussain and Marshall Brinn and Drew Mcdermott},
title = {1 POIROT – Integrated Learning of Web Service Procedures},
year = {}
}
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Abstract
POIROT is an integration framework for combining ma-chine learning mechanisms to learn hierarchical models of web services procedures from a single or very small set of demonstration examples. The system is organized around a shared representation language for communications with a central hypothesis blackboard. Component learning systems share semantic representations of their hypotheses (gener-alizations) and inferences about demonstration traces. To further the process, components may generate learning goals for other learning components. POIROT’s learners or hy-pothesis formers develop workflows that include order de-pendencies, subgoals, and decision criteria for selecting or prioritizing subtasks and service parameters. Hypothesis evaluators, guided by POIROT’s meta-control component, plan experiments to confirm or disconfirm hypotheses ex-tracted from these learning products. Collectively, they cre-ate methods that POIROT can use to reproduce the demon-stration and solve similar problems. After its first phase of development, POIROT has demonstrated it can learn some moderately complex hierarchical task models from semantic traces of user-generated service transaction sequences at a level that is approaching human performance on the same learning task.
Keyphrases
web service procedure poirot integrated learning system share semantic representation complex hierarchical task model integration framework central hypothesis blackboard demonstration example plan experiment small set learning task human performance poirot learner meta-control component hy-pothesis former learning goal similar problem user-generated service transaction sequence demonstration trace include order de-pendencies semantic trace hypothesis evaluator representation language learning component hierarchical model first phase decision criterion service parameter
