The evolution of the electronic sources connected through wide area networks like Internet has encouraged the development of new information gathering techniques that go beyond traditional information retrieval and WEB search methods. They use advanced techniques, like planning or constraint programming, to integrate and reason about hetereogeneous information sources. In this paper we describe MAPWEB. MAPWEB is a multiagent framework that integrates planning agents and WEB information retrieval agents. The goal of this framework is to deal with problems that require planning with information to be gathered from the WEB. MAPWEB decouples planning from information gathering, by splitting a planning problem into two parts: solving an abstract problem and validating and completing the abstract solutions by means of information gathering. This decoupling allows also to address an important aspect of information gathering: the WEB is a dynamic medium and more and more companies make their information available in the WEB everyday. The MAPWEB framework can be adapted quickly to these changes by just modifying the planning domain and adding the required information gathering agents. For instance, in a travel assistant domain, if taxi companies begin to offer WEB information, it would only be necessary to add new planning operators related to traveling by taxi, for a more complete travel domain. This paper describes the MAPWEB planning process, focusing on the aforementioned flexibility aspect.

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.