Person: ŞENSOY, Murat
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Murat
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ŞENSOY
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Conference ObjectPublication Open Access Evidential deep learning to quantify classification uncertainty(Neural Information Processing Systems Foundation, 2018) Şensoy, Murat; Kaplan, L.; Kandemir, M.; Computer Science; ŞENSOY, MuratDeterministic 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 model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of out-of-distribution queries and endurance against adversarial perturbations.Conference ObjectPublication 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, MuratA 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.
ArticlePublication Metadata only Handling epistemic and aleatory uncertainties in probabilistic circuits(Springer, 2022-04) Cerutti, F.; Kaplan, L. M.; Kimmig, A.; Şensoy, Murat; Computer Science; ŞENSOY, MuratWhen 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 approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to Bayesian inference of posterior distributions that overcomes the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian inference of posterior distributions from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities.EditorialPublication Metadata only Preface(Springer International Publishing, 2016) Dignum, V.; Noriega, P.; Şensoy, Murat; Sichman, J. S.; Computer Science; ŞENSOY, MuratThe pervasiveness of open systems raises a range of challenges and opportunities for research and technological development in the area of autonomous agents and multi-agent systems. Open systems comprise loosely coupled entities interacting within a social space. These entities join the social space in order to achieve some goals that are unattainable by agents in isolation. However, when those entities are autonomous, they might misbehave and, furthermore, in open systems one may not know what entities will be active beforehand, when they may become active, or when they may leave the system.ArticlePublication Metadata only Agent-based semantic collaborative search(Executive Committee, Taiwan Academic Network, Ministry of Education, 2013) Şensoy, Murat; Computer Science; ŞENSOY, MuratNext generation of the Web builds upon technologies such as Semantic Web and Intelligent Software Agents. These technologies aim at knowledge representation that allows both humans and software agents to understand and reason about the content on the Web. In this paper, we propose an agent-based approach for collaborative distributed semantic search of the Web resources. Our approach enables a human user to semantically describe his search interest to an agent. Depending on the interests of their users, the agents evolve their ontologies and create search concepts. Based on these search concepts, the agents coordinate and compose virtual communities. Within these communities, agents with similar interests interact to locate and share URLs relevant to search interests of their users. Through these interactions, shared vocabularies are cooperatively emerged by agents to communicate properly within the communities. Our empirical evaluations and analysis of the proposed approach show that our approach combines Semantic Web technologies and multi-agent systems in a novel way to enable users to find and share the URLs relevant to their search interests.ArticlePublication Metadata only A generalized stereotype learning approach and its instantiation in trust modeling(Elsevier, 2018-08) Fang, H.; Zhang, J.; Şensoy, Murat; Computer Science; ŞENSOY, MuratOwing to the lack of historical data regarding an entity in online communities, a user may rely on stereotyping to estimate its behavior based on historical data about others. However, these stereotypes cannot accurately reflect the user's evaluation if they are based on limited historical data about other entities. In view of this issue, we propose a novel generalized stereotype learning approach: the fuzzy semantic framework. Specifically, we propose a fuzzy semantic process, incorporated with traditional machine-learning techniques to construct stereotypes. It consists of two sub-processes: a fuzzy process that generalizes over non-nominal attributes (e.g., price) by splitting their values in a fuzzy manner, and a semantic process that generalizes over nominal attributes (e.g., location) by replacing their specific values with more general terms according to a predefined ontology. We also implement the proposed framework on the traditional decision tree method to learn users' stereotypes and validate the effectiveness of our framework for computing trust in e-marketplaces. Experiments on real data confirm that our proposed model can accurately measure the trustworthiness of sellers with which buyers have limited experience.Conference ObjectPublication Metadata only Reasoning under uncertainty: variations of subjective logic deduction(IEEE, 2013) Kaplan, L. M.; Şensoy, Murat; Tang, Y.; Chakraborty, S.; Bisdikian, C.; de Mel, G.; Computer Science; ŞENSOY, MuratThis work develops alternatives to the classical subjective logic deduction operator. Given antecedent and consequent propositions, the new operators form opinions of the consequent that match the variance of the consequent posterior distribution given opinions on the antecedent and the conditional rules connecting the antecedent with the consequent. As a result, the uncertainty of the consequent actually map to the spread for the probability projection of the opinion. Monte Carlo simulations demonstrate this connection for the new operators. Finally, the work uses Monte Carlo simulations to evaluate the quality of fusing opinions from multiple agents before and after deduction.Book PartPublication Metadata only Discovering frequent patterns to bootstrap trust(Springer International Publishing, 2013) Şensoy, Murat; Yilmaz, B.; Norman, T. J.; Computer Science; ŞENSOY, MuratWhen 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 a priori trust for all newcomers. However, assuming the same a priori trust for all agents may lead to other problems like whitewashing. In this paper, we leverage graph mining and knowledge representation to estimate a priori trust for agents. For this purpose, our approach first discovers significant patterns that may be used to characterise trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate trustworthiness. Lastly, a priori trust for newcomers are estimated using the discovered features based on the trained model. Through extensive simulations, we have showed that the proposed approach significantly outperforms existing approaches.ArticlePublication Metadata only Interest-based negotiation for policy-regulated asset sharing(Springer Nature, 2016) Parizas, C.; de Mel, G.; Preece, A. D.; Şensoy, Murat; Calo, S. B.; Pham, T.; Computer Science; ŞENSOY, MuratResources sharing is an important but complex problem to be solved. The problem is exacerbated in a coalition context due to policy constraints, that reflect concerns regarding security, privacy and performance to name a few, placed on the resources. Thus, to effectively share resources, members of a coalition need to negotiate on policies and at times refine them to meet the needs of the operating environment. Towards achieving this goal, in this work we propose and evaluate a novel policy negotiation mechanism based on the interest-based negotiation paradigm. Interest-based negotiation, promotes collaboration when compared with the traditional, position-based negotiation approaches.Conference ObjectPublication Metadata only Trust estimation and fusion of uncertain information by exploiting consistency(IEEE, 2014) Kaplan, L.; Şensoy, Murat; de Mel, G.; Computer Science; ŞENSOY, MuratAgents 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 - especially in open multiagent systems - where it is not possible to ensure that the sources are honest and their opinions are not misleading. In this paper, we propose a novel approach that exploits consistencies and conflicts between personal observations and shared information to derive trust evidence for information sources. Based on the derived evidence, we describe how opinions from diverse sources can be fused. We have evaluated our approach for trust estimation and opinion fusion using a service selection scenario. Through extensive simulations, we have shown that our approach significantly outperforms the existing trust-based information fusion approaches.