Trustable Task Processing Systems

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.

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Associated Projects

The NSF funded Theory and Practice of Accountable Systems (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. In past research, we have demonstrated that achieving basic social policy goals in open information networks will require increased reliance on information accountability through after-the-fact detection of rule violations.

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.

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