8 found
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Munindar P. Singh [5]Munindar Singh [3]
  1.  34
    Argument schemes for reasoning about trust.Simon Parsons, Katie Atkinson, Zimi Li, Peter McBurney, Elizabeth Sklar, Munindar Singh, Karen Haigh, Karl Levitt & Jeff Rowe - 2014 - Argument and Computation 5 (2-3):160-190.
    Trust is a natural mechanism by which an autonomous party, an agent, can deal with the inherent uncertainty regarding the behaviours of other parties and the uncertainty in the information it shares with those parties. Trust is thus crucial in any decentralised system. This paper builds on recent efforts to use argumentation to reason about trust. Specifically, a set of schemes is provided, and abstract patterns of reasoning that apply in multiple situations geared towards trust. Schemes are described in which (...)
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  2.  11
    Understanding dynamics of polarization via multiagent social simulation.Amanul Haque, Nirav Ajmeri & Munindar P. Singh - 2023 - AI and Society 38 (4):1373-1389.
    It is widely recognized that the Web contributes to user polarization, and such polarization affects not just politics but also peoples’ stances about public health, such as vaccination. Understanding polarization in social networks is challenging because it depends not only on user attitudes but also their interactions and exposure to information. We adopt Social Judgment Theory to operationalize attitude shift and model user behavior based on empirical evidence from past studies. We design a social simulation to analyze how content sharing (...)
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  3.  61
    A logic of intentions and beliefs.Munindar P. Singh & Nicholas M. Asher - 1993 - Journal of Philosophical Logic 22 (5):513 - 544.
    Intentions are an important concept in Artificial Intelligence and Cognitive Science. We present a formal theory of intentions and beliefs based on Discourse Representation Theory that captures many of their important logical properties. Unlike possible worlds approaches, this theory does not assume that agents are perfect reasoners, and gives a realistic view of their internal architecture; unlike most representational approaches, it has an objective semantics, and does not rely on an ad hoc labeling of the internal states of agents. We (...)
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  4.  37
    Embedding AI in society: ethics, policy, governance, and impacts.Michael Pflanzer, Veljko Dubljević, William A. Bauer, Darby Orcutt, George List & Munindar P. Singh - 2023 - AI and Society 38 (4):1267-1271.
  5.  8
    Maintenance commitments: Conception, semantics, and coherence.Pankaj Telang, Munindar P. Singh & Neil Yorke-Smith - 2023 - Artificial Intelligence 324 (C):103993.
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  6.  13
    Normative Multiagent Systems: Guest Editors’ Introduction.Guido Boella, Gabriella Pigozzi & Munindar Singh - 2010 - Logic Journal of the IGPL 18 (1):1-3.
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  7.  12
    Logic-Based Agent Verification.Michael Fisher, Munindar Singh, Diana Spears & Mike Wooldridge - 2007 - Journal of Applied Logic 5 (2):193-195.
  8.  59
    An ontology for commitments in multiagent systems. [REVIEW]Munindar P. Singh - 1999 - Artificial Intelligence and Law 7 (1):97-113.
    Social commitments have long been recognized as an important concept for multiagent systems. We propose a rich formulation of social commitments that motivates an architecture for multiagent systems, which we dub spheres of commitment. We identify the key operations on commitments and multiagent systems. We distinguish between explicit and implicit commitments. Multiagent systems, viewed as spheres of commitment (SoComs), provide the context for the different operations on commitments. Armed with the above ideas, we can capture normative concepts such as obligations, (...)
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