No abstract

The ability to automatically answer a request that requires the composition of a set of web services has received much interest in the last decade, as it supports B2B applications. Planning techniques are used widely in the literature to describe the web services composition problem but they don't scale up well. This weakness is due to the search space explosion caused by the large ranges of data exchanged among services. In addition, it is more interesting to use a decentralised planner because the nature of the problem is distributed.In this paper, we consider a set of web service agents where each agent has a set of services organised in a graph. To respond to a request, agents propose their best local partial plans which are partial paths in the graph. They then coordinate their partial plans to provide the global plan for the submitted request using an algorithm based on a distributed heuristic function. This function ensures the optimality and the completeness of the algorithm. Indeed, it is based not only on the agent capabilities to respond to a request, but also taking into account the plans proposed by other agents.The complexity of the algorithm is polynomial. The experiments show the ability of our approach to find the optimal solutions for automated web services composition taking into account the dependencies betwen the agents.

No abstract

A* is the algorithm of finding the shortest path between two nodes in a graph. When the searching problem is constituted of a set of linked graphs, A* searches solution like if it is face of one graph formed by linked graphs. While researchers have developed solutions to reduce the execution time of A* in multiple cases by multiples techniques, we develop a new algorithm: DEC-A* which is a decentralized version of A* composing a solution through a collection of graph. A* uses a distance-plus-cost heuristic function to determine the order in which the search visits nodes in the tree. Our algorithm DEC-A* extends the evaluation of the distance-plus-cost heuristic to be the sum of two functions : local distance, which evaluates the cost to reach the nearest neighbor node s to the goal, and global distance which evaluates the cost from s to the goal through other graphs. DEC-A* reduces the time of finding the shortest path and reduces the complexity, while ensuring the privacy of graphs.