مدل معامله دانش مبتنی بر قانون برای محیط های تلفن همراه
|تعداد صفحات مقاله انگلیسی
|31 صفحه PDF
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 10516 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Sciences, Volume 176, Issue 18, 22 September 2006, Pages 2642–2672
In this paper, we propose and formalize a rule based knowledge transaction model for mobile environments. Our model integrates the features of both mobile environments and intelligent agents. We use logic programming as a mathematic tool and formal specification method to study knowledge transaction in mobile environments. Our knowledge transaction model has the following major advantages: (1) It can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments. (2) It is knowledge oriented and has a declarative semantics inherited from logic programming. (3) It is a formalization that can be applied to general problem domains. We show that our model can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments.
Study on knowledge base and intelligent agent in mobile environments is a very new and meaningful research topic. As a practical scenario in this research area, a company manager may use mobile host to do the rule based decision making and negotiation. We believe that the investigation on intelligent agent and knowledge base in mobile environments is critical because this will help us to find a way to significantly improve current mobile systems. Comparing to the stationary environment, the mobile environment has a few specific properties such as mobility and disconnection. The issue of data and knowledge transaction has presented new challenges for researchers in mobile environments, such as knowledge representation, reasoning and knowledge transaction processing in this kind of environments. Currently, there is a separation between intelligent agents community on one side, and the mobile systems community on the other side , ,  and . Various proposals and systems have been developed in order to deal with data transaction processing in mobile environments , ,  and , but these approaches concentrate on data not knowledge transaction under mobile environments. The current knowledge representation, reasoning and problem solving languages and models are discussed most in conventional environments , and no much formal study has been conducted to the issue of knowledge transaction in mobile environment. As the first step, this paper addresses the accounts of knowledge transaction processing language and model in mobile environments by developing a new knowledge transaction model for mobile environments. In comparison with previous work, the formalized knowledge transaction model has the following major advantages: (1) It can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments. This extends the application domains of knowledge representation and reasoning for problem solving in conventional environments, such as logic programming, extended logic programming, stable model, SMODEL, DLV and XSB , , ,  and . (2) It is knowledge-oriented and has declarative semantics inherited from logic programming so it can be used to study knowledge transaction at a high level. This is different to all the works that only deal with data transaction , , ,  and . (3) It is a formalization that can be applied to general problem domains, which is different from most previous approaches that suffer from a lack of formal specification and, thus, only can be ad hoc for specific systems and environments , ,  and  The motivation we use extended logic programming  and  as a mathematical tool to study knowledge transaction in mobile environment is that (1) this method can represent knowledge related domain information; (2) it can represent incomplete information explicitly and can conduct knowledge reasoning using inference rules; (3) some systems have been implemented using logic programming such as SMODEL, XSB and DLV, therefore our formalization has a sound basis for future implementation. This model is rule based, and can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments. We believe that our knowledge transaction language and model will provide a foundation towards the formal specification and development of real-world mobile software systems, as the way of traditional software systems development. By illustrating a case study we demonstrate how our transaction model is applied in practical domains in mobile environments. The paper is organized as follows. In Section 2, we present some background knowledge on intelligent agent and mobile environments, and then introduce our new environmental model. In Section 3, we describe the transaction processing in mobile environments and give background knowledge on logic programming. We also give mobile semantics to some logic programming concepts and formulas. In Section 4, we first start from knowledge transaction representation language, and then we impose a set of rules for knowledge transaction in mobile environments. Lastly we formalize our knowledge transaction model. In Section 5, we go through a transaction example to demonstrate how our knowledge transaction model can handle a practical scenario. Finally in Section 6, we summarize our work and discuss potential important future work.
نتیجه گیری انگلیسی
In this paper, we developed and formalized a rule based knowledge transaction model for mobile environments, where our model integrated the features of both mobile environments and intelligent agents. The formalization started with defining a knowledge transaction processing language £, which contains necessary components for specifying knowledge transactions associated with the MH, MSS and HS. Then, a set of rules to capture features of knowledge transactions in mobile environments were imposed and specified. Lastly, the model was formally defined. By illustrating a case study, it was demonstrated that the formalized knowledge transaction model is applicable in practical domains to process knowledge transaction in mobile environments. We have justified that extended logic programming is a suitable formal specification method to study knowledge transaction in mobile environments. Our knowledge transaction model can be used for knowledge transaction representation, formalization and reasoning in mobile environments. We also demonstrated how our transaction model can be used in practical domains in mobile environments. Our work presented in this paper can certainly be extended in several directions. Formalization of knowledge transaction model in mobile environments in this paper is a good starting point to investigate knowledge base and intelligent agents in mobile systems. Implementing and bringing this formalization to a real-world system will be a very interesting future work. Current best known implementations of logic programming system in conventional environments, such as XSB , SMODEL  and DLV  can form the basis for implementing our knowledge transaction model in mobile environments. Many performance study of data transaction models or methods have been done in both classic and mobile environments, such as ESR concurrency method in , and transaction management model in . Some complete mobile agent systems such as Telescript, Ara, and Aglets  have built prototype systems which can be used for performance evaluation. The performance evaluation for our proposed knowledge transaction model in this paper can be studied by either using some analytical models  or building prototype systems during implementation. The correction proof for our proposed transaction model is another important future work. To prove the correctness of our transaction model, firstly, a set of principles need to be proposed to formalize the concept of correctness in our system, then formal proof need to be provided to show that our transaction process satisfies each of these principles. The proof of correctness may be constructed on the basis of some formal results of logic programming (answer set semantics) as well as the standard automata theory as investigated by Lynch et al. and Madria et al.  and  in classical environments. In addition, knowledge reasoning and declarative problem solving in multi-agent systems under mobile environments are challenging research topics that have significant applications in practice, we have started to carry on our research in this area .