Biblio
We seek to address the challenge of engineering socially intelligent personal agents that are privacy-aware. We propose Arnor, a method, including a metamodel based on social constructs. Arnor incorporates social norms and goes beyond existing agent-oriented software engineering (AOSE) methods by systematically capturing how a personal agent’s actions influence the social experience it delivers. We conduct two empirical studies to evaluate Arnor. First, via a multiphase developer study, we show that Arnor simplifies application development. Second, via simulation experiments, we show that Arnor provides improved privacy-preserving social experience to end users than personal agents engineered using a traditional AOSE method.
Services today are configured through policies that capture expected behaviors. However, because of subtle and changing stakeholder requirements, producing and maintaining policies is nontrivial. Policy errors are surprisingly common and cause avoidable security vulnerabilities.
We propose Aragorn, an approach that applies formal argumentation to produce policies that balance stakeholder concerns. We demonstrate empirically that, compared to the traditional approach for specifying policies, Aragorn performs (1) better on coverage, correctness, and quality; (2) equally well on learnability and effort÷coverage and difficulty; and (3) slightly worse on time and effort needed. Thus, Aragorn demonstrates the potential for capturing policy rationales as arguments.
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To interact effectively, agents must enter into commitments. What should an agent do when these commitments conflict? We describe Coco, an approach for reasoning about which specific commitments apply to specific parties in light of general types of commitments, specific circumstances, and dominance relations among specific commitments. Coco adapts answer-set programming to identify a maximalsetofnondominatedcommitments. It provides a modeling language and tool geared to support practical applications.
Privacy remains a major challenge today partly because it brings together social and technical considerations. Yet, current software engineering focuses only on the technical aspects. In contrast, our approach, Revani, understands privacy from the standpoint of sociotechnical systems (STSs), with particular attention on the social elements of STSs. We specify STSs via a combination of technical mechanisms and social norms founded on accountability.
Revani provides a way to formally represent mechanisms and norms, and applies model checking to verify whether specified mechanisms and norms would satisfy the requirements of the stakeholders. Additionally, Revani provides a set of design patterns and a revision tool to update an STS specification as necessary. We demonstrate the working of Revani on a healthcare emergency use case pertaining to disasters.
We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions.
Secure collaboration requires the collaborating parties to apply the
right policies for their interaction. We adopt a notion of
conditional, directed norms as a way to capture the standards of
correctness for a collaboration. How can we handle conflicting norms?
We describe an approach based on knowledge of what norm dominates what
norm in what situation. Our approach adapts answer-set programming to
compute stable sets of norms with respect to their computed conflicts
and dominance. It assesses agent compliance with respect to those
stable sets. We demonstrate our approach on a healthcare scenario.