Abstracting Web Agent Proofs into Human-Level Justifications
Authors
Furtado, Vasco
Pinheiro, Paulo
McGuinness, Deborah L.
Deshwal, Priyendra
Narayanan, Dhyanesh
Carvalho, Juliana
Pinheiro, Vladia
Chang, Cynthia
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Issue Date
2007-05-07
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Abstract
Information supporting answer explanations are derived from proofs. One of the difficulties for humans to understand web agent proofs is that the proofs are typically described at the machine-level. In this paper, we introduce a novel and generic approach for abstracting machine-level portable proofs into human-level justifications. This abstraction facilitates generating explanations from proofs on the web. Our approach consists of creating a repository of proof templates, called abstraction patterns, describing how machine-level inference rules and axioms in proofs can be replaced by rules that are more meaningful for humans. Intermediate results supporting machine-level proofs may also be dropped during the abstraction process. The Inference Web Abstractor algorithm has been developed with the goal of matching the abstraction patterns in the repository against the original proof and applying a set of strategies to abstract the proof thereby simplifying its presentation. The tools used for creating and applying abstraction patterns are shown along with an intelligence analysis example.