Chung-Wei Hang

Trustworthy Service Selection and Composition

We consider Service-Oriented Computing (SOC) environments. Such environments are populated with services that stand proxy for a variety of information resources. A fundamental challenge in SOC is to select and compose services, to support specified user needs directly or by providing additional services. Existing approaches for service selection either fail to capture the dynamic relationships between services or assume that the environment is fully observable. In practical situations, however, consumers are often not aware of how the services are implemented. We propose two distributed trust-aware service selection approaches: one based on Bayesian networks and the other on a beta-mixture model. We experimentally validate our approach through a simulation study. Our results show that both approaches accurately punish and reward services in terms of the qualities they offer, and further that the approaches are effective despite incomplete observations regarding the services under consideration.

A probabilistic approach for maintaining trust based on evidence

Leading agent-based trust models address two important needs. First, they show how an agent may estimate the trustworthiness of another agent based on prior interactions. Second, they show how agents may share their knowledge in order to cooperatively assess the trustworthiness of others. However, in real-life settings, information relevant to trust is usually obtained piecemeal, not all at once. Unfortunately, the problem of maintaining trust has drawn little attention. Existing approaches handle trust updates in a heuristic, not a principled, manner. This paper builds on a formal model that considers probability and certainty as two dimensions of trust. It proposes a mechanism using which an agent can update the amount of trust it places in other agents on an ongoing basis. This paper shows via simulation that the proposed approach (a) provides accurate estimates of the trustworthiness of agents that change behavior frequently; and (b) captures the dynamic behavior of the agents. This paper includes an evaluation based on a real dataset drawn from Amazon Marketplace, a leading e-commerce site.

From Quality to Utility: Adaptive Service Selection Framework

We consider an approach to service selection wherein service consumers choose services with desired nonfunctional properties to maximize their utility. A consumer’s utility from using a service clearly depends upon the qualities offered by the service. Many existing service selection approaches support agents estimating trustworthiness of services based on their quality of service. However, existing approaches do not emphasize the relationship between a consumer’s interests and the utility the consumer draws from a service. Further, they do not properly support consumers being able to compose services with desired quality (and utility) profiles.

Behind the Curtain: Service Selection via Trust in Composite Services

Service selection, where some of the services are accessed indirectly as constituents of composite services, is difficult for the following reasons: (1) the interpretation of service qualities is subjective; (2) evidence must be combined from multiple sources; (3) service profiles change dynamically; and (4) constituent services may be only partially observable behind composite services. We propose an approach where we map service qualities to a common probabilistic trust metric. Whereas current trust approaches estimate the trustworthiness of a composite service based on a fully observable and static setting, we propose a statistical approach built on expectation maximized over a finite mixture model. Our experiments show that our approach can dynamically punish or reward the constituents of composite services while making only partial observations.

Shin: Generalized Trust Propagation with Limited Evidence

Shin incorporates a probabilistic method for revising trust estimates in trustees, yielding higher prediction accuracy than traditional approaches that base trust exclusively on a series of referrals culminating with the trustee.

Generalized framework for personalized recommendations in agent networks

An agent network can be modeled as a directed weighted graph whose vertices represent agents and edges represent a trust relationship between the agents. This article proposes a new recommendation approach, dubbed LocPat, which can recommend trustworthy agents to a requester in an agent network. We relate the recommendation problem to the graph similarity problem, and define the similarity measurement as a mutually reinforcing relation. We understand an agent as querying an agent network to which it belongs to generate personalized recommendations. We formulate a query into an agent network as a structure graph applied in a personalized manner that reflects the pattern of relationships centered on the requesting agent. We use this pattern as a basis for recommending an agent or object (a vertex in the graph). By calculating the vertex similarity between the agent network and a structure graph, we can produce a recommendation based on similarity scores that reflect both the link structure and the trust values on the edges. Our resulting approach is generic in that it can capture existing network-based approaches merely through the introduction of appropriate structure graphs. We evaluate different structure graphs with respect to two main kinds of settings, namely, social networks and ratings networks. Our experimental results show that our approach provides personalized and flexible recommendations effectively and efficiently based on local information.

Aragorn: Eliciting and Maintaining Secure Service Policies

Services are configured via policies that capture expected behaviors, but stakeholder requirements can change, making policy errors a surprisingly common occurrence. Aragorn applies formal argumentation to produce policies that balance stakeholder concerns.

A Mechanism for Cooperative Demand-Side Management

Demand-side management (DSM) is an important theme in studies of the Smart Grid and offers the possibility of leveling power consumption with its attendant benefits of reducing capital expenses. This paper develops an algorithmic mechanism that reduces peak total power consumption and encourages prosocial behavior, such as expressing flexibility in ones power consumption and reporting preferences truthfully. The objective is to provide a tractable, budget-balanced mechanism that promotes truth-telling from households. The resulting mechanism is theoretically and empirically proven to be ex ante budget-balanced, weakly Pareto-efficient, and weakly Bayesian incentive-compatible. A simulation study verifies that the mechanism could largely reduce the computational complexity that the optimal allocation requires, while maintaining approximately the same performance. A user study with 20 subjects further shows the effectiveness of the mechanism in preventing participants from defecting and incentivizing them to reveal flexible preferences.