Browsing by Author "Mel, G. de"

Now showing 1 - 5 of 5

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Combining semantic web and IoT to reason with health and safety policies
(IEEE, 2018-01-12) Göynügür, Emre; Şensoy, Murat; Mel, G. de; Computer Science; ŞENSOY, Murat; Göynügür, Emre
Monitoring and following health and safety regulations are especially important - but made difficult - in hazardous work environments such as underground mines to prevent work place accidents and illnesses. Even though there are IoT solutions for health and safety, every work place has different characteristics and monitoring is typically done by humans in control rooms. During emergencies, conflicts may arise among prohibitions and obligations, and humans may not be better placed to make decision without any assistance as they do not have a bird's-eye-view of the environment. Motivated by this observations, in this paper, we discuss how health and safety regulations can be implemented using a semantic policy framework. We then show how this framework can be integrated into an in-use smart underground mine solution. We also evaluate the performance of our framework to show that it can cope with the complexity and the amount of data generated by the system.
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Policy conflict resolution in IoT via planning
(Advances in Artificial Intelligence, 2017) Göynügür, Emre; Bernardini, S.; Mel, G. de; Talamadupula, K.; Şensoy, Murat; Computer Science; ŞENSOY, Murat; Göynügür, Emre
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 prohibitions in context, and humans are not equipped to maintain a bird’s-eye-view of the environment. Motivated by this observation, in this paper, we present an ontology-based policy framework which can efficiently detect policy conflicts and automatically resolve such using an AI planner.
Open Access
Reasoning with uncertain information and trust
(SPIE, 2013) Şensoy, Murat; Mel, G. de; Fokoue, A.; Norman, T. J.; Pan, J. Z.; Tang, Y.; Oren, N.; Sycara, K.; Kaplan, L.; Pham, T.; Computer Science; ŞENSOY, Murat
A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, they do not provide an explicit means for reasoning about second order uncertainty. Dempster-Shafer theory of evidence (DST) overcomes this weakness and provides means to fuse and reason about uncertain information. In this paper, we combine DL-Lite with DST to allow scalable reasoning over uncertain semantic knowledge bases. Furthermore, our formalism allows for the detection of conflicts between the fused information and domain constraints. Finally, we propose methods to resolve such conflicts through trust revision by exploiting evidence regarding the information sources. The effectiveness of the proposed approaches is shown through simulations under various settings.
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SOBE: Source behavior estimation for subjective opinions In multiagent systems
(International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2016) Şensoy, Murat; Kaplan, L.; Mel, G. de; Gunes, Taha Doğan; Computer Science; ŞENSOY, Murat; Gunes, Taha Doğan
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 malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. In this work, we propose to learn statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. We show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.
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Source behavior discovery for fusion of subjective opinions
(IEEE, 2016) Şensoy, Murat; Kaplan, L.; Mel, G. de; Gunes, Taha; Computer Science; ŞENSOY, Murat; Gunes, Taha
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 are not always competent and honest but often erroneous or even malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. Using statistical methods, one can learn how likely a source distorts the ground truth and the associated distortion models so that reports from these sources can still be fused to enhance estimation of the ground truth. In this work, we propose to learn a number of statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. Using real data from the Web and through extensive comparison with recent trust-based approaches over simulations, we show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.