Catálogo de publicaciones - libros

Automatic service composition may dramatically improve development efficiency of Web Service applications. This paper proposes an approach to automatically process semantic and dynamic service composition using Description Logics(DLs) and AI planning techniques. Services and service composition problems are formalized with DLs to provide well-defined semantics. Four relationships among services as well as two combined service expressions are defined, with which AI planning techniques can be used to reason about how to compose services to achieve user-defined goals. We present an algorithm for automatic service composition, which uses backward-chaining search of potential services, and automatically eliminates irrelevant services while selecting, thus guarantee the execution efficiency. We also make some performance optimization of the algorithm such as removing redundant services and reusing previously achieved goals. All the composition steps could be done dynamically and automatically. Finally, we present an example to show how the algorithm works.

Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute, especially when using a P2P-based multi-agent infrastructure for knowledge sharing. This raises the question of how much credence to give each information source. We cannot expect each user to know the trustworthiness of each source. We tackle this problem by employing a reputation mechanism, which offer a viable solution to encouraging trustworthy behavior in Semantic Web. In addition, we introduce a reputation multi-agent system, SemTrust, which enable Semantic Web to utilize reputation mechanism based on semantic similarity between agents. Our experiments show that the system with SemTrust outperforms the system without it and our approach is more robust with security under conditions where existing malicious agents. We hope that these methods will help move the Semantic Web closer to fulfilling its promise by using reputation-based multi-agent technology.

Cross-organizational interoperability and coordination are major challenges to Virtual Organization(VO) applications. Multi-agent systems combined with Semantic Web are promising approach for solving the challenging problems. In this paper, a semantic Web enabled multi-agent platform for logistic VO supporting is presented. The issue of extending OWL with multi-attribute constraints for VO modeling is addressed. A constraint rule language SWOCRL is proposed which is based on OWL and SWRL with constraint extension and class-scoped restriction. Important VO concepts such as organizations, activities, resources, contracts, interactions and their logistic specializations are described with OWL plus SWOCRL.

Multi-path routing is an important mechanism for Quality of Service of multimedia communication in current networks. The key point of multi-path routing is to develop an efficient method to distribute the traffic over these multiple paths. This paper proposes a novel approach, QMP (QoS Multi-path Routing), to dynamically evolve the traffic distribution parameters using natural computation and make them adaptive to the changes of network flow and topology. First, a kind of function that reflects the changes of the network characteristics is coded using a novel natural computation technique. Then, regarding it as an antibody, QMP uses an artificial immune idea to evolve the function. Using the evolved function, some parameters in the networks can be predicted. After that, this paper proposes a traffic distribution approach that uses the parameters obtained to forward the real-time traffic, and in the meantime, adjusts the traffic parameters to adapt to the new network changes. The simulations show that this approach performs better than the classic optimal routing algorithms on feasibility and is also comparable to the optimal algorithms in terms of performance. Furthermore, QMP is better than the previous excellent multi-path and single-path routing approaches in both aspects of the delays and network resource usage.

This paper introduces the policy uniform concept model and defines the general policy specification language-GPSL based on the formal foundation of DDL. Compared with other policy languages, GPSL describes the general concept model providing the support for the policies including action, goal, utility and so on. Moreover GPSL provides the support for complex action composition and description to define complex system behavior. To resolve the policy conflicts we use meta-policy to illustrate the policy relationships and owl to define policy ontology. Finally this paper analyzes the policy implementation in autonomic computing system.

In massive agent system, there are many diffusion processes among agent social strategies which take place concurrently. A social law is a restriction on the set of strategies available to agents [1]. All agents will trend to select an identical social strategy in the agent social law evolution, which can be called as the phenomenon of . This paper presents a model for the concurrent social strategy diffusion with unification trend. With the model, an agent’s social strategy is influenced not only by the diffusion that bear on itself, but also by concurrent diffusion processes that bear on other agents; and, an agent will incline to the average social strategy of the whole system which can make the system be more unified.

In competition environment a satisfactory multi-agent learning algorithm should, at a minimum, have rationality and convergence. Exploiter-PHC (It is written as Exploiter here) could beat many fair opponents, but it is neither rational against stationary policy nor convergent in self-play, even it could be beaten possibly by some fair opponents in lower league. Now an improved algorithm named ExploiterWT (Exploiter With Testing) based on Exploiter is proposed. The basic idea of ExploiterWT is that an additional testing period is added to estimate the Nash Equilibrium policy. ExploiterWT could satisfy these properties mentioned above. It needn’t Nash Equilibrium as apriori knowledge like Exploiter when it begins to exploiting. Even ExploiterWT could avoid being beaten by some fair opponents in lower league. In this paper, at first the thoughts of this algorithm will be introduced, and then experiment results obtained in Game Pennies-Matching against other algorithms will be given.

In this paper, we discuss guidelines for a reward design problem that defines when and what amount of reward should be given to the agents, within the context of reinforcement learning approach. We take keepaway soccer as a standard task of multiagent domain which requires skilled teamwork. The difficulties of designing reward for good teamwork are due to its features as follows: i) since it is a continuing task which has no explicit goal, it is hard to tell when reward should be given to the agents, ii) since it is a multiagent cooperative task, it is hard to make a fair share of the reward for each agent’s contribution. Through some experiments, we show that reward design have a major effect on the agent’s behavior, and introduce the reward function that makes agents perform keepaway successfully.

Though research on agents that interact with humans via voice or text has been intensively conducted, agents that can interact with more than two persons in parallel have not been studied well. To enable an agent to interact with multiple people, we propose a method to assign multiple scenarios to one agent, in which each scenario describes an interaction protocol between the agent and one person. Obviously, coordination among multiple scenarios is required to avoid conflicts in actions. For example, one agent cannot execute both walking and sitting actions simultaneously. However, what is more important is that a coordination policy, a way of specifying how to manage conflicts among multiple actions, must be introduced. In this paper, we introduce a coordination scenario that avoids conflicts in actions and coordinates scenarios according to a coordination policy. The coordination scenario, which controls the execution of interaction scenarios by communication, is generated by a coordination policy for solving conflicts. When the coordination scenario receives a request to execute actions from an interaction scenario, it checks whether the actions will trigger conflicts and sends an order not to execute them if conflicts will occur. The coordination scenario interworks concurrent interaction scenarios by executing this process repeatedly.

Although both multi-objective optimization and agent technology gained a lot of interest during the last decade, many aspects of their functionality still remain open. This paper proposes the multi-agent negotiation model applied in multi-objective optimization. There are three types of agents in the system. The plan agent plans the global best benefit; the action agent plans the best benefit of the single objective; and the resource agent manages the common resource. The agents compete and cooperate to reach the global best benefit through their negotiation. The model is applied in evolutionary multi-objective optimization to realize its parallel and distributed computation, and the experiment on MAGE shows the model is effective.