A rule based knowledge transaction model for mobile environments
Introduction
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 [23], [29], [36], [38]. Various proposals and systems have been developed in order to deal with data transaction processing in mobile environments [1], [6], [22], [30], 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 [4], 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 [4], [14], [18], [31], [34]. (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 [1], [8], [13], [22], [30]. (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 [1], [6], [22], [30]
The motivation we use extended logic programming [19], [26] 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.
Section snippets
Environment model
To develop our knowledge transaction model in mobile environment, we propose a new environment model, which combines the features of mobile environments [1], [6], [30] and intelligent agents [36], [38]. This environment model can be used to study transaction processing [7], [20], intelligent agent and knowledge base in mobile environment. In our paper, we use this model for knowledge transaction study in mobile environments.
When we study the transaction processing in mobile environments, we
Transactions in mobile environments
In this section, we will briefly describe the transaction processing in the mobile environment and give some background knowledge on logic programming, which will provide a basis for our logic programming based transaction processing language and model.
A typical transaction T in the mobile environment will look like the following [9];in which the proxy first acquires the appropriate set of locks for the
A logic programming based transaction model
In this section, we define a logic programming based knowledge transaction model which formalizes the knowledge transaction processing in mobile environments. We start with a complete transaction stage by stage including startup, sleep, wakeup, move/handoff, read, write and commit, to give readers a clear idea what activities are supposed to happen on MH, MSS and HS at every stage. Then we define a transaction processing language , which contains necessary components for specifying knowledge
A transaction example in mobile environments
In this section, we will give an example to explain how to use our logic programming based transaction processing language to describe transaction in the mobile environments. In our example, an initial fact and a finite set of rules with respect to share investment problem domain will be given and specified. The given example discusses a yes scenarios for an update transaction: The MH requests an update transaction, the transaction is committed on HS using two phase commit protocol. The HS
Conclusions and future work
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
References (39)
Evolving a model of transaction management with embedded concurrency control for mobile database systems
Information and Software Technology
(2003)- et al.
Logic programming and knowledge representation
Logic Programming
(1994) - et al.
Nonmonotonic logic and temporal projection
Artificial Intelligence
(1987) Answer set programming and plan generation
Artificial Intelligence
(2002)- et al.
Virtual partition algorithm in a nested transaction environment and its correctness
Information Sciences
(2001) - M. Ahamad, S. Smith, Detecting mutual consistency of shared objects, in: Proceedings of the IEEE Conference on Mobile...
- et al.
Verifying security protocols as planning in logic programming
ACM Transactions on Computational Logic
(2001) Knowledge Representation, Reasoning and Declarative Problem Solving
(2003)- D. Barbara, T. Imielinski, Sleepers and workaholics: caching strategies in mobile environments, in: Proceedings of...
On Line Transaction Processing Systems
(1992)
KLAVA: a Java package for distributed and mobile applications
Software Practice and Experience
Efficient top-down computation of queries under the well-founded semantics
Journal of Logic Programming
Mobile Agent
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2010, 2010 International Conference on Management and Service Science, MASS 2010