Author name cluster

Anwitaman Datta

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6 papers

1 author row

IS Journal 2017 Journal Article

Cooperation and Competition When Bidding for Complex Projects: Centralized and Decentralized Perspectives

Piotr Skowron
Krzysztof Rzadca
Anwitaman Datta

To successfully complete a complex project, agents (companies or individuals) must form a team with the required competencies and resources. A team can be formed either by the project issuer based on individual agents' offers (centralized formation) or by the agents themselves (decentralized formation) bidding for a project as a consortium. The authors investigate rational strategies for agents, propose concepts to characterize the stability of winning teams and study computational complexity of finding these concepts of stability.

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TAAS Journal 2015 Journal Article

Game-Theoretic Mechanisms to Increase Data Availability in Decentralized Storage Systems

Krzysztof Rzadca
Anwitaman Datta
Gunnar Kreitz
Sonja Buchegger

In a decentralized storage system, agents replicate each other’s data to increase availability. Compared to organizationally centralized solutions, such as cloud storage, a decentralized storage system requires less trust in the provider and may result in smaller monetary costs. Our system is based on reciprocal storage contracts that allow the agents to adopt to changes in their replication partners’ availability (by dropping inefficient contracts and forming new contracts with other partners). The data availability provided by the system is a function of the participating agents’ availability. However, a straightforward system in which agents’ matching is decentralized uses the given agent availability inefficiently. As agents are autonomous, the highly available agents form cliques replicating data between each other, which makes the system too hostile for the weakly available newcomers. In contrast, a centralized, equitable matching is not incentive compatible: it does not reward users for keeping their software running. We solve this dilemma by a mixed solution: an “adoption” mechanism in which highly available agents donate some replication space, which in turn is used to help the worst-off agents. We show that the adoption motivates agents to increase their availability (is incentive-compatible), but also that it is sufficient for acceptable data availability for weakly-available agents.

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AAAI Conference 2012 Conference Paper

Modeling Context Aware Dynamic Trust Using Hidden Markov Model

Xin Liu
Anwitaman Datta

Modeling trust in complex dynamic environments is an important yet challenging issue since an intelligent agent may strategically change its behavior to maximize its profits. In this paper, we propose a context aware trust model to predict dynamic trust by using a Hidden Markov Model (HMM) to model an agent’s interactions. Although HMMs have already been applied in the past to model an agent’s dynamic behavior to greatly improve the traditional static probabilistic trust approaches, most HMM based trust models only focus on outcomes of the past interactions without considering interaction context, which we believe, reflects immensely on the dynamic behavior or intent of an agent. Interaction contextual information is comprehensively studied and integrated into the model to more precisely approximate an agent’s dynamic behavior. Evaluation using real auction data and synthetic data demonstrates the efficacy of our approach in comparison with previous state-of-the-art trust mechanisms.

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IJCAI Conference 2011 Conference Paper

A Trust Prediction Approach Capturing Agents' Dynamic Behavior

Xin Liu
Anwitaman Datta

Predicting trust among the agents is of great importance to various open distributed settings (e. g. , e-market, peer-to-peer networks, etc. ) in that dishonest agents can easily join the system and achieve their goals by circumventing agreed rules, or gaining unfair advantages, etc. Most existing trust mechanisms derive trust by statistically investigating the target agent's historical information. However, even if rich historical information is available, it is challenging to model an agent's behavior since an intelligent agent may strategically change its behavior to maximize its profits. We therefore propose a trust prediction approach to capture dynamic behavior of the target agent. Specifically, we first identify features which are capable of describing/representing context of a transaction. Then we use these features to measure similarity between context of the potential transaction and that of previous transactions to estimate trustworthiness of the potential transaction based on previous similar transactions' outcomes. Evaluation using real auction data and synthetic data demonstrates efficacy of our approach in comparison with an existing representative trust mechanism.

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AAMAS Conference 2011 Conference Paper

MetaTrust: Discriminant Analysis of Local Information for Global Trust Assessment

Liu Xin
Gilles Tredan
Anwitaman Datta

A traditional approach to reasoning about the trustworthiness of a transaction is to determine the trustworthiness of the specific agent involved, based on its past behavior. As a departure from such traditional trust models, we propose a transaction centered trust model (MetaTrust) where an agent uses its previous transactions to assess the trustworthiness of a potential transaction based on associated meta-information, which is capable of distinguishing successful transactions from unsuccessful ones. This meta information is harnessed using a machine learning algorithm (namely, discriminant analysis) to extract relationships between the potential transaction and previous transactions.

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TAAS Journal 2010 Journal Article

Structured overlay for heterogeneous environments

Šarūnas Girdzijauskas
Anwitaman Datta
Karl Aberer

Recent years have seen advances in building large Internet-scale index structures, generally known as structured overlays. Early structured overlays realized distributed hash tables (DHTs) which are ill suited for anything but exact queries. The need to support range queries necessitates systems that can handle uneven load distributions. However such systems suffer from practical problems—including poor latency, disproportionate bandwidth usage at participating peers, or unrealistic assumptions on peers' homogeneity, in terms of available storage or bandwidth resources. In this article we consider a system that is not only able to support uneven load distributions but also to operate in heterogeneous environments, where each peer can autonomously decide how much of its resources to contribute to the system. We provide the theoretical foundations of realizing such a network and present a newly proposed system Oscar based on these principles. Oscar can construct efficient overlays given arbitrary load distributions by employing a novel scalable network sampling technique. The simulations of our system validate the theory and evaluate Oscar's performance under typical challenges, encountered in real-life large-scale networked systems, including participant heterogeneity, faults, and skewed and dynamic load-distributions. Thus the Oscar distributed index fills in an important gap in the family of structured overlays, bringing into life a practical Internet-scale index, which can play a crucial role in enabling data-oriented applications distributed over wide-area networks.

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