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How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection.

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack.

Web service composition is a significant problem as the number of available web services increases; however, manual composition is not an efficient option. Automated web service composition can be performed using AI Planning techniques, utilizing descriptions of available atomic web services, enhanced with semantic awareness and relaxation. This paper discusses a unified, semantically aware approach, handling both semantic (OWL-S & SAWSDL) and non-semantic (WSDL) web service descriptions. In the first case, ontology analysis is adopted to semantically enhance the planning domains and problems, in order to deal with cases where exact syntactic input-to-output matching is not feasible. In the non-semantic descriptions case, semantic information is acquired utilizing alternative sources such as lexical thesauri. Concept similarity measures are applied and utilized to achieve the desired degree of semantic relaxation. The solution to a web service composition problem is a plan describing the desired composite service. To support the proposed approach, the PORSCE framework has been implemented. The framework is modular, integrating discrete web service description languages and semantic relaxation techniques. Based on the similarity measures suggested in the paper, performance issues are also explored.

Business processes that span organizational borders describe the interaction between multiple parties working towards a common objective. They also express business rules that govern the behavior of the process and account for expressing changes reflecting new business objectives and new market situations.

We developed a service request language and support framework that allow users to formulate their requests against standard business processes.¹⁹ In this paper, we extend the approach by presenting a framework capable of automatically associating business rules with relevant processes involved in a user request. This framework plans and monitors the execution of the request and assertions against services underlying these processes. Definitions and classifications of business rules (named assertions in the paper) are given together with an assertion language for expressing them. The framework is able to handle the non-determinism typical for service-oriented computing environments and it is based on the interleaving of planning and execution. Interestingly, the language is able to express both functional and non-functional aspects of the assertions.

Automated composition of Web services or the process of forming new value-added Web services is one of the most promising challenges facing the Semantic Web today. Semantics enables Web service to describe capabilities together with their processes, hence one of the key elements for the automated composition of Web services. In this paper, we focus on the functional level of Web services i.e. services are described according to some input, output parameters semantically enhanced by concepts in a domain ontology. Web service composition is then viewed as a composition of semantic links wherein the latter links refer to semantic matchmaking between Web service parameters (i.e. outputs and inputs) in order to model their connection and interaction. The key idea is that the matchmaking enables, at run time, finding semantic compatibilities among independently defined Web service descriptions. By considering such a level of composition, a formal model to perform the automated composition of Web services i.e. Semantic Link Matrix, is introduced. The latter model is required as a starting point to apply problem-solving techniques such as regression (or progression)-based search for Web service composition. The model supports a semantic context in order to find correct, complete, consistent and robust plans as solutions. In this paper, an innovative and formal model for an AI (Artificial Intelligence) planning-oriented composition is presented. Our system is implemented and interacting with Web services which are dedicated to Telecom scenarios. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach.