Trustable Task Processing Systems

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Abstract:

As personal assistant software matures and assumes more autonomous control of user activities, it becomes more critical that this software can tell the user why it is doing what it is doing, and instill trust in the user that its task knowledge reflects standard practice and is being appropriately applied. Our research focuses broadly on providing infrastructure that may be used to increase trust in intelligent agents. In this paper, we will report on a study we designed to identify factors that influence trust in intelligent adaptive agents. We will then introduce our work on explaining adaptive task processing agents as motivated by the results of the trust study. We will introduce our task execution explanation component and provide examples in the context of a particular adaptive agent named CALO. Key features include (1) an architecture designed for re-use among different task execution systems; (2) a set of introspective predicates and a software wrapper that extracts explanation-relevant information from a task execution systems; (3) a version of the Inference Web explainer for generating formal justifications of task processing and converting them to user-friendly explanations; and (4) a unified framework for explaining results from task execution, learning, and deductive reasoning.

History

DateCreated ByLink
October 27, 2011
16:22:02
Cynthia ChangDownload

Related Projects:

Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML.
DCO-DS LogoTheory and Practice of Accountable Systems (TPAS)
Principal Investigator: Jim Hendler
Description: The TPAS Project investigates computational and social properties of information networks necessary to provide reliable assessments of compliance with rules and policies governing the use of information.

Related Research Areas:

Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts:
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,
Semantic Foundations
Lead Professor: Deborah L. McGuinness
Description: Semantic Foundations
Concepts: