Browsing by Author "Kaplan, L."
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Evidential deep learning to quantify classification uncertainty
Şensoy, Murat; Kaplan, L.; Kandemir, M. (Neural Information Processing Systems Foundation, 2018)Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant ... -
FUSE-BEE: Fusion of subjective opinions through behavior estimation
Şensoy, Murat; Kaplan, L.; Ayci, Gönül; de Mel, G. (IEEE, 2015)Information is critical in almost all decision making processes. Therefore, it is important to get the right information at the right time from the right sources. However, information sources may behave differently while ... -
Learning and reasoning in complex coalition information environments: a critical analysis
Cerutti, F.; Alzantot, M.; Xing, T.; Harborne, D.; Bakdash, J. Z.; Braines, D.; Chakraborty, S.; Kaplan, L.; Kimmig, A.; Preece, A.; Raghavendra, R.; Şensoy, Murat; Srivastava, M. (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 ... -
Partial observable update for subjective logic and its application for trust estimation
Kaplan, L.; Şensoy, Murat; Chakraborty, S.; de Mel, G. (Elsevier, 2015)Subjective Logic (SL) is a type of probabilistic logic, which is suitable for reasoning about situations with uncertainty and incomplete knowledge. In recent years, SL has drawn a significant amount of attention from the ... -
Probabilistic logic programming with beta-distributed random variables
Cerutti, F.; Kaplan, L.; Kimmig, A.; Şensoy, Murat (Association for the Advancement of Artificial Intelligence, 2019-07-17)We enable aProbLog-a probabilistic logical programming approach-to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms ... -
Reasoning with uncertain information and trust
Şensoy, Murat; Mel, G. de; Fokoue, A.; Norman, T. J.; Pan, J. Z.; Tang, Y.; Oren, N.; Sycara, K.; Kaplan, L.; Pham, T. (SPIE, 2013)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, ... -
Semantic reasoning with uncertain information from unreliable sources
Şensoy, Murat; Kaplan, L.; de Mel, G. (Springer International Publishing, 2016)Intelligent software agents may significantly benefit from semantic reasoning. However, existing semantic reasoners are based on Description Logics, which cannot handle vague, incomplete, and unreliable knowledge. In this ... -
SOBE: Source behavior estimation for subjective opinions In multiagent systems
Şensoy, Murat; Kaplan, L.; Mel, G. de; Gunes, Taha Doğan (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 ... -
Source behavior discovery for fusion of subjective opinions
Şensoy, Murat; Kaplan, L.; Mel, G. de; Gunes, Taha (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 ... -
Subjective bayesian networks and human-in-the-loop situational understanding
Braines, D.; Thomas, A.; Kaplan, L.; Şensoy, Murat; Bakdash, J. Z.; Ivanovska, M.; Preece, A.; Cerutti, F. (Springer, 2018-03-21)In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well ... -
TRIBE: Trust revision for information based on evidence
Şensoy, Murat; de Mel, G.; Kaplan, L.; Pham, T.; Norman, T. J. (IEEE, 2013)In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is ... -
Trust estimation and fusion of uncertain information by exploiting consistency
Kaplan, L.; Şensoy, Murat; de Mel, G. (IEEE, 2014)Agents may cooperate by communicating their opinions about various phenomena. These opinions are then fused by agents and used for informed decision-making. However, fusing opinions from diverse sources is not trivial - ... -
Trust estimation of sources over correlated propositions
Kaplan, L.; Şensoy, Murat (IEEE, 2018-09-05)This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are ... -
Uncertainty-aware deep classifiers using generative models
Şensoy, Murat; Kaplan, L.; Cerutti, F.; Saleki, Maryam (Association for the Advancement of Artificial Intelligence, 2020)Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ... -
Uncertainty-aware situational understanding
Tomsett, R.; Kaplan, L.; Cerutti, F.; Sullivan, P.; Vente, D.; Vilamala, M. R.; Kimmig, A.; Preece, A.; Şensoy, Murat (SPIE, 2019)Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore ...
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