Murat Sensoy

OWL-POLAR: A framework for semantic policy representation and reasoning

In a distributed system, the actions of one component may lead to severe failures in the system as a whole. To govern such systems, constraints are placed on the behaviour of components to avoid such undesirable actions. Policies or norms are declarations of soft constraints regulating what is prohibited, permitted or obliged within a distributed system. These constraints provide systems-level means to mitigate against failures. A few machine-processable representations for policies have been proposed, but they tend to be either limited in the types of policies that can be expressed or are limited by the complexity of associated reasoning mechanisms. In this paper, we present a language that sufficiently expresses the types of policies essential in practical systems, and which enables both policy-governed decision-making and policy analysis within the bounds of decidability. We then propose an OWL-based representation of policies that meets these criteria and reasoning mechanisms that use a novel combination of ontology consistency checking and query answering. The proposed policy representation and reasoning mechanisms allow development of distributed agent-based systems that operate flexibly and effectively in policy-constrained environments.

Trust and obfuscation principles for quality of information in emerging pervasive environments

The emergence of large scale, distributed, sensor-enabled, machine-to-machine pervasive applications necessitates engaging with providers of information on demand to collect the information, of varying quality levels, to be used to infer about the state of the world and decide actions in response. In these highly fluid operational environments, involving information providers and consumers of various degrees of trust and intentions, obfuscation of information is used to protect providers from misuses of the information they share, while still providing benefits to their information consumers. In this paper, we develop the initial principles for relating to trust and obfuscation within the context of this emerging breed of applications. We start by extending the definitions of trust and obfuscation into this emerging application space. We, then, highlight their role as we move from tightly-coupled to loosely-coupled sensory-inference systems. Finally, we present the interplay between trust and obfuscation as well as the implications for reasoning under obfuscation.

A Generalized Stereotypical Trust Model

Stereotypical trust modeling can be adopted by a buyer to effectively evaluate trustworthiness of a seller who has little or no past experience in e-marketplaces. The buyer forms trust stereotypes based on her past experience with other sellers. However, when the buyer has limited past experience with sellers, the formed stereotypes cannot accurately reflect her trust evaluation towards sellers. To address this issue, we propose a novel generalized stereotypical trust model. Specifically, we first build a semantic ontology to represent hierarchical relationships among seller attribute values. We then propose a fuzzy semantic decision tree (FSDT) learning method to construct trust stereotypes that generalizes over seller non-nominal attributes by splitting their values in a fuzzy manner, and generalizes over nominal attributes by replacing their specific values with more general terms according to the ontology. Experimental results confirm that our proposed model can more accurately measure the trustworthiness of sellers in simulated e-marketplaces where buyers have limited experience with sellers.

Using Subjective Logic to Handle Uncertainty and Conflicts

In coalition operations, information from different sources belong to different organisations have to be gathered and aggregated. The information from these resources may not be consistent. Inconsistencies in the gathered information creates severe uncertainties that hinders the usefulness of the information. In this paper, we have propose a Subjective Logic based approach for modelling the trustworthiness of information sources within a specific context. This model is used to handle inconsistencies through filtering information from less trustworthy sources.

Inference management, trust and obfuscation principles for quality of information in emerging pervasive environments ☆

Abstract The emergence of large scale, distributed, sensor-enabled, machine-to-machine pervasive applications necessitates engaging with providers of information on demand to collect the information, of varying quality levels, to be used to infer about the state of the world and decide actions in response. In these highly fluid operational environments, involving information providers and consumers of various degrees of trust and intentions, information transformation, such as obfuscation, is used to manage the inferences that could be made to protect providers from misuses of the information they share, while still providing benefits to their information consumers. In this paper, we develop the initial principles for relating to inference management and the role that trust and obfuscation plays in it within the context of this emerging breed of applications. We start by extending the definitions of trust and obfuscation into this emerging application space. We, then, highlight their role as we move from the tightly-coupled to loosely-coupled sensory-inference systems and describe how quality, value and risk of information relate in collaborative and adversarial systems. Next, we discuss quality distortion illustrated through a human activity recognition sensory system. We then present a system architecture to support an inference firewall capability in a publish/subscribe system for sensory information and conclude with a discussion and closing remarks.

Goal-directed policy conflict detection and prioritisation

A policy (or norm) is a guideline stating what is allowed, forbidden or obligated for an entity, in a certain situation, so that acceptable outcomes are achieved. Policies occur in many types of scenarios, whether they are loose social networks of individuals or highly structured institutions. It is important, however, for policies to be consistent and to support their goals. This requires a thorough understanding of the implications of introducing specific policies and how they interact. It is difficult, even for experts, to write consistent, unambiguous and accurate policies, and conflicts are practically unavoidable. In this paper we address this challenge of providing automated support for identifying and resolving logical and functional conflicts.

Discovering frequent patterns to bootstrap trust

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 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.

Trust and obfuscation

In modern coalition operations, decision makers must be capable of obtaining and fusing data from diverse sources. The reliability of these sources can vary, and, in order to protect their interests, the data they provide can be obfuscated. The trustworthiness of fused data depends on both the reliability of these sources and their obfuscation strategy. Information consumers must determine how to evaluate trust in the presence of obfuscation, while information providers must determine the appropriate level of obfuscation in order to ensure both that they remain trusted, and do not reveal any private information. In this paper, through a coalition scenario, we discuss and formalise trust and obfuscation in these contexts and the complex relationships between them.

On choosing an efficient service selection mechanism in dynamic environments

Consumers use service selection mechanisms to decide on a service provider to interact with. Although there are various service selection mechanisms, each mechanism has different strengths and weaknesses for different settings. In this paper, we propose a novel approach for consumers to learn how to choose the most useful service selection mechanism among different alternatives in dynamic environments. In this approach, consumers continuously observe outcomes of different service selection mechanisms. Using their observations and a reinforcement learning algorithm, consumers learn to choose the most useful service selection mechanism with respect to their trade-offs. Through the simulations, we show that not only the consumers choose the most useful service selection mechanism using the proposed approach, but also the performance of the proposed approach does not go below the lower-bound defined by the tradeoffs of the consumers.

A knowledge driven policy framework for internet of things

With the proliferation of technology, connected and interconnected devices (henceforth referred to as IoT) are fast becoming a viable option to automate the day-to-day interactions of users with their environment—be it manufacturing or home-care automation. However, with the explosion of IoT deployments we have observed in recent years, manually governing the interactions between humans-to-devices—and especially devices-to- devices—is an impractical task, if not an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equip to maintain a bird’s-eye-view of the interaction space. Motivated by this observation, in this paper, we propose an end-to-end framework that (a) automatically dis- covers devices, and their associated services and capabilities w.r.t. an ontology; (b) supports representation of high-level—and expressive—user policies to govern the devices and services in the environment; (c) pro- vides efficient procedures to refine and reason about policies to automate the management of interactions; and (d) delegates similar capable devices to fulfill the interactions, when conflicts occur. We then present our initial work in instrumenting the framework and discuss its details.