Akın Günay

Structural and semantic similarity metrics for web service matchmaking

Service matchmaking is the process of finding appropriate services for a given set of requirements. We present a novel service matchmaking approach based on the internal process of services. We model service internal processes using finite state machines and use various heuristics to find structural similarities between services. Further, we use a process ontology that captures the semantic relations between processes. This semantic information is then used to determine semantic similarities between processes and to compute match rates of services. We develop a case study to illustrate the benefits of using process-based matchmaking of services and to evaluate strengths of the different heuristics we propose.

Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments

Commitments are being used to specify interactions among autonomous agents in multiagent systems. Various formalizations of commitments have shown their strength in representing and reasoning on multiagent interactions. These formalizations mostly study commitment lifecycles, emphasizing fulfillment of a single commitment. However, when multiple commitments coexist, fulfillment of one commitment may have an effect on the lifecycle of other commitments. Since agents generally participate in more than one commitment at a time, it is important for an agent to determine whether it can honor its commitments. These commitments may be the existing commitments of the agent as well as any prospective commitments that the agent plans to participate in. To address this, we develop the concept of commitment feasibility, i.e., whether it is possible for an agent to fulfill a set of commitments all together. To achieve this we generalize the fulfillment of a single commitment to the feasibility of a set of commitments. We then develop a solid method to determine commitment feasibility. Our method is based on the transformation of feasibility into a constraint satisfaction problem and use of constraint satisfaction techniques to come up with a conclusion. We show soundness and completeness of our method and illustrate its applicability over realistic cases.

Dynamically generated commitment protocols in open systems

Agent interaction is a fundamental part of any multiagent system. Such interactions are usually regulated by protocols, which are typically defined at design-time. However, in many situations a protocol may not exist or the available protocols may not fit the needs of the agents. In order to deal with such situations agents should be able to generate protocols at runtime. In this paper we develop a three-phase framework to enable agents to create a commitment protocol dynamically. In the first phase one of the agents generates candidate commitment protocols, by considering its goals, its abilities and its knowledge about the other agents’ services. We propose two algorithms that ensure that each generated protocol allows the agent to reach its goals if the protocol is enacted. The second phase is ranking of the generated protocols in terms of their expected utility in order to select the one that best suits the agent. The third phase is the negotiation of the protocol between agents that will enact the protocol so that the agents can agree on a protocol that will be used for enactment. We demonstrate the applicability of our approach using a case study.

Commitment Protocol Generation

Multiagent systems contain agents that interact with each other to carry out their activities. The agents’ interactions are usually regulated with protocols that are assumed to be defined by designers at design time. However, in many settings, such protocols may not exist or the available protocols may not fit the needs of the agents. In such cases, agents need to generate a protocol on the fly. Accordingly, this paper proposes a method that can be used by an agent to generate commitment protocols to interact with other agents. The generation algorithm considers the agent’s own goals and capabilities as well as its beliefs about other agents’ goals and capabilities. This enables generation of commitments that are more likely to be accepted by other agents. We demonstrate the workings of the algorithm on a case study.

Detecting and predicting privacy violations in online social networks

Online social networks have become an essential part of social and work life. They enable users to share, discuss, and create content together with various others. Obviously, not all content is meant to be seen by all. It is extremely important to ensure that content is only shown to those that are approved by the content’s owner so that the owner’s privacy is preserved. Generally, online social networks are promising to preserve privacy through privacy agreements, but still everyday new privacy leakages are taking place. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained.We have developed

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

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PROTOSS: A Run Time Tool for Detecting Privacy Violations in Online Social Networks

, a run time tool for detecting and predicting

Service matchmaking revisited: An approach based on model checking

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GOSU: computing goal support with commitments in multiagent systems

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Detecting conflicts in commitments

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