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

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Semantic Matchmaking of Web Services using Model Checking *

ABSTRACT Service matchmaking is the process of finding suitable services given by the providers for the service requests of consumers. Previous approaches to service matchmaking is mostly based on matching the input-output parameters of service requests and service provisions. However, such approaches do not capture the semantics of the services and hence cannot match requests to services effectively. This paper proposes an agent-based approach for matchmaking that is based on capturing the semantics of services and requests formally through temporal logic. Requests are represented as a set of properties and compared to the service representations using model checking, yielding results on whether a service can satisfy a request or not. By help of domain ontologies, our approach also supports flexible matching, where partially matching services are identified. We provide a general framework, where our approach can work with other existing matchmaking approaches and is integrated with current efforts such as OWL-S and SWRL

PROMOCA: Probabilistic Modeling and Analysis of Agents in Commitment Protocols

Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.Published versio

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

Structural and Semantic Similarity Metrics for

Abstract. 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.

Automated Analysis of Commitment Protocols Using Probabilistic Model Checking

Commitment protocols provide an effective formalism for the regulation of agent interaction. Although existing work mainly focus on the design-time development of static commitment protocols, recent studies propose methods to create them dynamically at run-time with respect to the goals of the agents. These methods require agents to verify new commitment protocols taking their goals, and beliefs about the other agents’ behavior into account. Accordingly, in this paper, we first propose a probabilistic model to formally capture commitment protocols according to agents’ beliefs. Secondly, we identify a set of important properties for the verification of a new commitment protocol from an agent’s perspective and formalize these properties in our model. Thirdly, we develop probabilistic model checking algorithms with advanced reduction for efficient verification of these properties. Finally, we implement these algorithms as a tool and evaluate the proposed properties over different commitment protocols

Anadolu Sevgilim

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