Visible to the public Biblio

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Munindar P. Singh.  2022.  Consent as a Foundation for Responsible Autonomy. Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI). 36
This paper focuses on a dynamic aspect of responsible autonomy, namely, to make intelligent agents be responsible at run time. That is, it considers settings where decision making by agents impinges upon the outcomes perceived by other agents. For an agent to act responsibly, it must accommodate the desires and other attitudes of its users and, through other agents, of their users. The contribution of this paper is twofold. First, it provides a conceptual analysis of consent, its benefits and misuses, and how understanding consent can help achieve responsible autonomy. Second, it outlines challenges for AI (in particular, for agents and multiagent systems) that merit investigation to form as a basis for modeling consent in multiagent systems and applying consent to achieve responsible autonomy.
Blue Sky Track
Karthik Sheshadari, Nirav Ajmeri, Jessica Staddon.  2017.  No (Privacy) News is Good News: An Analysis of New York Times and Guardian Privacy News from 2010 to 2016. Proceedings of 15th Annual Conference on Privacy, Security and Trust (PST). :1-12.
Munindar P. Singh, Amit K. Chopra.  2017.  The Internet of Things and Multiagent Systems: Decentralized Intelligence in Distributed Computing. Proceedings of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS). :1738–1747.

Traditionally, distributed computing concentrates on computation understood at the level of information exchange and sets aside human and organizational concerns as largely to be handled in an ad hoc manner.  Increasingly, however, distributed applications involve multiple loci of autonomy.  Research in multiagent systems (MAS) addresses autonomy by drawing on concepts and techniques from artificial intelligence.  However, MAS research generally lacks an adequate understanding of modern distributed computing.

In this Blue Sky paper, we envision decentralized multiagent systems as a way to place decentralized intelligence in distributed computing, specifically, by supporting computation at the level of social meanings.  We motivate our proposals for research in the context of the Internet of Things (IoT), which has become a major thrust in distributed computing.  From the IoT's representative applications, we abstract out the major challenges of relevance to decentralized intelligence.  These include the heterogeneity of IoT components; asynchronous and delay-tolerant communication and decoupled enactment; and multiple stakeholders with subtle requirements for governance, incorporating resource usage, cooperation, and privacy.  The IoT yields high-impact problems that require solutions that go beyond traditional ways of thinking.

We conclude with highlights of some possible research directions in decentralized MAS, including programming models; interaction-oriented software engineering; and what we term enlightened governance.

Blue Sky Thinking Track

Thomas Christopher King, Akın Günay, Amit K. Chopra, Munindar P. Singh.  2017.  Tosca: Operationalizing Commitments Over Information Protocols. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI). :1–9.

The notion of commitment is widely studied as a high-level abstraction for modeling multiagent interaction.  An important challenge is supporting flexible decentralized enactments of commitment specifications.  In this paper, we combine recent advances on specifying commitments and information protocols.  Specifically, we contribute Tosca, a technique for automatically synthesizing information protocols from commitment specifications. Our main result is that the synthesized protocols support commitment alignment, which is the idea that agents must make compatible inferences about their commitments despite decentralization.

Nirav Ajmeri, Hui Guo, Pradeep K. Murukannaiah, Munindar P. Singh.  2017.  Arnor: Modeling Social Intelligence via Norms to Engineer Privacy-Aware Personal Agents. :1–9.

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.

Nirav Ajmeri, Chung-Wei Hang, Simon D. Parsons, Munindar P. Singh.  2017.  Aragorn: Eliciting and Maintaining Secure Service Policies. IEEE Computer. 50:1–8.

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.

To appear

Mehdi Mashayekhi, Hongying Du, George F. List, Munindar P. Singh.  2016.  Silk: A Simulation Study of Regulating Open Normative Multiagent Systems. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). :1–7.

In a multiagent system, a (social) norm describes what the agents may expect from each other.  Norms promote autonomy (an agent need not comply with a norm) and heterogeneity (a norm describes interactions at a high level independent of implementation details). Researchers have studied norm emergence through social learning where the agents interact repeatedly in a graph structure.

In contrast, we consider norm emergence in an open system, where membership can change, and where no predetermined graph structure exists.  We propose Silk, a mechanism wherein a generator monitors interactions among member agents and recommends norms to help resolve conflicts.  Each member decides on whether to accept or reject a recommended norm.  Upon exiting the system, a member passes its experience along to incoming members of the same type.  Thus, members develop norms in a hybrid manner to resolve conflicts.

We evaluate Silk via simulation in the traffic domain.  Our results show that social norms promoting conflict resolution emerge in both moderate and selfish societies via our hybrid mechanism.

Nirav Ajmeri, Jiaming Jiang, Rada Y. Chirkova, Jon Doyle, Munindar P. Singh.  2016.  Coco: Runtime Reasoning about Conflicting Commitments. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). :1–7.

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.

Luis G. Nardin, Tina Balke-Visser, Nirav Ajmeri, Anup K. Kalia, Jaime S. Sichman, Munindar P. Singh.  2016.  Classifying Sanctions and Designing a Conceptual Sanctioning Process for Socio-Technical Systems. The Knowledge Engineering Review. 31:1–25.

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.

Amit K. Chopra, Munindar P. Singh.  2015.  Cupid: Commitments in Relational Algebra. Proceedings of the 23rd Conference on Artificial Intelligence (AAAI). :1–8.

We propose Cupid, a language for specifying commitments that supports their information-centric aspects, and offers crucial benefits.  One, Cupid is first-order, enabling a systematic treatment of commitment instances.  Two, Cupid supports features needed for real-world scenarios such as deadlines, nested commitments, and complex event expressions for capturing the lifecycle of commitment instances.  Three, Cupid maps to relational database queries and thus provides a set-based semantics for retrieving commitment instances in states such as being violated,discharged, and so on.  We prove that Cupid queries are safe.  Four,to aid commitment modelers, we propose the notion of well-identified commitments, and finitely violable and finitely expirable commitments.  We give syntactic restrictions for obtaining such commitments.