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Enabling smart environments through scalable policy reasoning and Internet of Things
(Wiley, 2019-04)
In this paper, we discuss how to combine ontology-based policy reasoning mechanisms with in-use Internet of Things applications to customize and automate device behaviors. We discuss how the policy framework can be extended ...
SOBE: Source behavior estimation for subjective opinions In multiagent systems
(International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2016)
In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources
of these opinions may not always be competent and honest but more likely erroneous or even ...
Learning and reasoning in complex coalition information environments: a critical analysis
(IEEE, 2018-09-05)
In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight - i.e., accurate ...
Source behavior discovery for fusion of subjective opinions
(IEEE, 2016)
Information is at the center of decision making in many systems and use-cases. In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources of these opinions ...
Policy conflict resolution in IoT via planning
(Advances in Artificial Intelligence, 2017)
With the explosion of connected devices to automate tasks, manually governing interactions among such devices—and associated services—has become an impossible task. This is because devices have their own obligations and ...
Tractable policy management framework for IoT
(SPIE, 2017)
Due to the advancement in the technology, hype of connected devices (hence forth referred to as IoT) in support of automating the functionality of many domains, be it intelligent manufacturing or smart homes, have become ...
Discovering frequent patterns to bootstrap trust
(Springer International Publishing, 2013)
When a new agent enters to an open multiagent system, bootstrapping its trust becomes a challenge because of the lack of any direct or reputational evidence. To get around this problem, existing approaches assume the same ...
Campaign participation prediction with deep learning
(Elsevier, 2021-08)
Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to ...
Misclassification risk and uncertainty quantification in deep classifiers
(IEEE, 2021)
In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ...
Handling epistemic and aleatory uncertainties in probabilistic circuits
(Springer, 2022-04)
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian ...
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