Murat Şensoy

OWL-POLAR: semantic policies for agent reasoning

Policies are declarations of constraints on the behaviour of components within distributed systems, and are often used to capture norms within agent-based systems. 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 limited by the complexity of associated reasoning mechanisms. In this paper, we argue for 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 using and a reasoning mechanism that uses a novel combination of ontology consistency checking and query answering. In this way, agent-based systems can be developed that operate flexibly and effectively in policy-constrainted environments.

Experience-based service provider selection in agent-mediated E-Commerce

For a given service demand, it is necessary to select a suitable service provider among many possibilities. An accurate selection is difficult when consumers do not have a significant history with many of the service providers and thus need to interact with others to make informed selections. In traditional approaches, consumers rate the service providers and exchange these ratings among each other. Contrary to traditional, rating-based service provider selection, this paper advocates an objective, experience-based approach in which consumers record their experiences with service providers rather than the overall, subjective ratings. A consumers experience with a provider captures the requested service and the delivered service in terms of service-specific attribute values. When an experience is transferred from one service consumer to another, the receiving consumer evaluates the experience using her own evaluation criteria. By sharing experiences, service consumers can model service providers accurately and thus make better selections for their needs. Rating-based strategies use highly subjective information for decision making since ratings depend on satisfaction criteria of the rater. However, the proposed method uses experiences, which do not include any interpretation. Comparisons of experience-based and rating-based strategies show that the experience-based approach results in higher customer satisfaction rates in many real-life settings.

Stage: Stereotypical Trust Assessment Through Graph Extraction

Bootstrapping trust assessment where there is little or no evidence regarding a subject is a significant challenge for existing trust and reputation systems. When direct or indirect evidence is absent, existing approaches usually assume that all agents are equally trustworthy. This naive assumption makes existing approaches vulnerable to attacks such as Sybil and whitewashing. Inspired by real‐life scenarios, we argue that malicious agents may share some common patterns or complex features in their descriptions. If such patterns or features can be detected, they can be exploited to bootstrap trust assessments. Based on this idea, we propose the use of frequent subgraph mining and state‐of‐the‐art knowledge representation formalisms to estimate a priori trust for agents. Our approach first discovers significant patterns that may be used to characterize trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate the trustworthiness of agents. Last, a priori trust for unknown agents (e.g., newcomers) is estimated using the discovered features based on the trained model. Through empirical evaluation, we show that the proposed approach significantly outperforms well‐known trust approaches if trustworthiness of agents is correlated with patterns in their descriptions or social networks. Furthermore, we show that the proposed approach performs at least as good as the existing approaches if such correlations do not exist.

Resource Determination and Allocation in Sensor Networks

Many organizations depend on critical sensory information to achieve their tasks. As the number of those tasks increases, efficient determination and allocation of required resources in sensor networks become crucial. In this paper, we propose means to describe tasks semantically with their requirements and constraints so that software agents can reason about those tasks and determine what type of sensor resources they may need. Based on the semantic description and reasoning mechanisms, we propose a distributed agent-based approach to efficiently allocate sensor resources to tasks. Our evaluation of the proposed approach shows that not only it enables fully automated determination and allocation of resources for tasks, but also the resulting allocation is efficient and close to optimum.

Ontological logic programming

In this paper, we propose Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. The proposed approach enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programs. The interpretation of these terms are delegated to an ontology reasoner during the interpretation of the program. Unlike similar approaches, OLP makes use of the full capacity of both the ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. Furthermore, using a case-study, we demonstrate the usefulness of OLP in real-life settings.

Evolving service semantics cooperatively: a consumer-driven approach

Commerce relies on dynamic creation and modification of services. New service offerings or service demands come into play frequently. Whereas traditional commerce supports creation of new service demands from consumers, e-commerce has so far expected service providers to come up with desirable new service offerings and assigned service consumers a passive role in the process. That is, current e-commerce architecture lacks a consumer-driven approach for the generation of new service descriptions. This paper bridges this gap by proposing a multiagent system of consumers that represent their service needs semantically using ontologies. Using our proposed approach, agents can create new service descriptions, share them with interested others, and use service descriptions that are created by other agents. Hence, more accurate concepts describing consumers’ service needs are cooperatively and iteratively created. This leads to a society of consumers with different but overlapping ontologies where mutually accepted service concepts emerge based on consumers’ exchange of service descriptions. Our simulations of consumer societies show that allowing cooperative evolution of service ontologies facilitates better representation of consumers’ service needs. Further, through cooperation, not only more useful service concepts emerge over time, but also ontologies of consumers having similar service needs become aligned gradually.

Reputation mechanism for e-commerce in virtual reality environments

The interest in 3D technology and Virtual Reality (VR) is growing both from academia and industry, promoting the quick development of virtual marketplaces (VMs) (i.e. ecommerce systems in VR environments). VMs have inherited trust problems, e.g. sellers may advertise a perfect deal but doesn’t deliver the promised service or product at the end. In view of this, we propose a five-sense feedback oriented reputation mechanism (supported by 3D technology and VR) particularly for VMs. The user study confirms that users prefer VMs with our reputation mechanism over those with traditional ones. In our reputation mechanism, five-sense feedback is objective and buyers can use it directly in their reputation evaluation of target sellers. However, for the scenarios where buyers only provide subjective ratings, we apply the approach of subjectivity alignment for reputation computation (SARC), where ratings provided by one buyer can then be aligned (converted) for another buyer according to the two buyers’ subjectivity. Evaluation results indicate that SARC can more accurately model sellers’ reputation than the state-of-the-art approaches.

Combining semantic web and logic programming for agent reasoning

Web Ontology Language (OWL) provides means to semantically represent domain knowledge as ontologies. Then, ontological reasoning allows software agents to effectively share and semantically interpret the knowledge. OWL adopts open world semantics and in order to achieve decidability its expressiveness is strictly limited. Therefore, many real-life problems cannot be represented only using ontologies and cannot be solved using just ontological reasoning. On the other hand, traditional reasoning mechanisms for autonomous agents are mostly based on Logic Programming (LP) and closed world assumption. LP provides a very expressive formal language, however it requires domain knowledge to be encoded as a part of logic programs. In this paper, we propose Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. The proposed approach enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programs. The interpretation of these terms are delegated to an ontology reasoner during the interpretation of the program. Unlike similar approaches, OLP makes use of the full capacity of both the ontological reasoning and logic programming. Using case-studies, we demonstrate the usefulness of OLP in multi-agent settings.

A Comparative Study of Reasoning Techniques for Service Selection

Open multiagent systems do not provide guarantees about the quality of the service of its providers. This makes it difficult for service consumers to find correct service providers. Many existing approaches share the intuition that service consumers can share their knowledge about service providers to help locate useful service providers. However, representing existing past knowledge and reasoning about this knowledge are two important challenges. A traditional approach for dealing with these challenges is to represent past dealings with ratings and to aggregate the ratings. However, rating-based approaches lack the expressiveness to articulate objective information about service dealings. To enable richer representations, we have developed an objective experience-based approach for service provider selection, in which consumers record their experienceswith service providers rather than the overall, subjective ratings for a provider. A consumers experience with a service provider is represented using an ontology that can capture subtle details including the context in which the service was requested. When a service consumer decides to share her experiences with a second service consumer, the receiving consumer evaluates the experience using its own context and evaluation criteria. In this work, we tackle the problem of reasoning about the collected experiences. We study different reasoning techniques for consumer agents to use in selecting service providers. We formulate these techniques into agent strategies and examine their strengths and weaknesses through simulations.

Partial observable update for subjective logic and its application for trust estimation

We proposed an approach to incorporate belief updates from partially observable evidence.We described how it can be used to estimate trustworthiness of information sources.We exploited consistency between reported and observed opinions for trust estimation.We showed that the proposed approach can be used to enhance trust estimation significantly. 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 multi-agent systems community as it connects beliefs and uncertainty in propositions to a rigorous statistical characterization via Dirichlet distributions. However, one serious limitation of SL is that the belief updates are done only based on completely observable evidence. This work extends SL to incorporate belief updates from partially observable evidence. Normally, the belief updates in SL presume that the current evidence for a proposition points to only one of its mutually exclusive attribute states. Instead, this work considers that the current attribute state may not be completely observable, and instead, one is only able to obtain a measurement that is statistically related to this state. In other words, the SL belief is updated based upon the likelihood that one of the attributes was observed. The paper then illustrates properties of the partial observable updates as a function of the state likelihood and illustrates the use of these likelihoods for a trust estimation application. Finally, the utility of the partial observable updates is demonstrated via various simulations including the trust estimation case.