Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise

This paper offers a service-oriented architecture (SOA) for ontology-based multi-agent system (MAS) negotiations in the context of virtual enterprises (VEs). The objective of this paper is fourfold. First, it is to design a SOA which utilizes ontology and MAS to provide a distributed and interoperable environment for automated negotiations in VE. In this architecture, individual ontologies for both the VE initiator and its potential partners are constructed to describe and store resources and service knowledge. Second, a series of semantic ontology matching methods are developed to reach agents' interoperability during the negotiation process. Third, correspondence-based extended contract net protocol is presented, which provides basic guidelines for agents' reaching mutual understandings and service negotiation. Last, a fuzzy set theory based knowledge reuse approach is proposed to evaluate the current negotiation behaviors of the VE partners. A walkthrough example is presented to illustrate the methodologies and system architecture proposed in this paper. © 2010 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201

An adaptive ontology-mediated approach to organize agent-based supply chain negotiation

Conference Theme: Soft Computing Techniques for Advanced Manufacturing and Service SystemsSession - MP-Fc Supply Chain Management & Logistics 3: cie181hk-1Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. One important aspect of SCM is that companies in the supply chain may have to make decisions which are conflicting with the other partners. Negotiation is an essential approach to solve transaction and scheduling problems among supply chain members. Multi-agent systems (MAS) are being increasingly used in SCM applications. The advances in agent technology have provided the potential of automating supply chain negotiations to alleviate human interactions. This paper proposes an ontology-mediated approach to organize the agent-based supply chain negotiation and equip the agents with sophisticated negotiation knowledge. Firstly, a generic agent negotiation scheme is developed involving the agent intelligence modules, the knowledge representation method and the interaction behaviors. Then, the negotiation knowledge is structured through the usage of ontology, which performs as a hierarchical architecture as well as a descriptive language. The relationships between negotiation ontology concepts are defined through SWRL inference rules. Through this method, agents' negotiation behaviors will be more adaptive to various negotiation environments in accordance with different negotiation knowledge.published_or_final_versionThe 40th International Conference on Computers & Industrial Engineering (CIE40), Awaji City, Japan, 25-28 July 2010. In Proceedings of the International Conference on Computers and Industrial Engineering, 2010, p. 1-

Atmospheric deposition of heavy metals in the Pearl River Delta, China

Author name used in this publication: C. S. C. WongAuthor name used in this publication: X. D. LiAuthor name used in this publication: G. ZhangAuthor name used in this publication: S. H. QiAuthor name used in this publication: X. Z. Peng2002-2003 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Heavy metal and Pb isotopic compositions of aquatic organisms in the Pearl River Estuary, South China

Author name used in this publication: C. C. M. IpAuthor name used in this publication: X. D. LiAuthor name used in this publication: G. ZhangAuthor name used in this publication: C. S. C. WongAuthor name used in this publication: W. L. Zhang2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Heavy metals in agricultural soils of the Pearl River Delta, South China

Author name used in this publication: S. C. WongAuthor name used in this publication: X. D. LiAuthor name used in this publication: G. ZhangAuthor name used in this publication: S. H. QiAuthor name used in this publication: Y. S. Min2001-2002 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks

This paper addresses the joint spectrum sharing and power allocation problem for device-to-device (D2D) communications underlaying a cellular network (CN). In the context of orthogonal frequency-division multiple-access systems, with the uplink resources shared with D2D links, both centralized and decentralized methods are proposed. Assuming global channel state information (CSI), the resource allocation problem is first formulated as a nonconvex optimization problem, which is solved using convex approximation techniques. We prove that the approximation method converges to a suboptimal solution and is often very close to the global optimal solution. On the other hand, by exploiting the decentralized network structure with only local CSI at each node, the Stackelberg game model is then adopted to devise a distributed resource allocation scheme. In this game-theoretic model, the base station (BS), which is modeled as the leader, coordinates the interference from the D2D transmission to the cellular users (CUs) by pricing the interference. Subsequently, the D2D pairs, as followers, compete for the spectrum in a noncooperative fashion. Sufficient conditions for the existence of the Nash equilibrium (NE) and the uniqueness of the solution are presented, and an iterative algorithm is proposed to solve the problem. In addition, the signaling overhead is compared between the centralized and decentralized schemes. Finally, numerical results are presented to verify the proposed schemes. It is shown that the distributed scheme is effective for the resource allocation and could protect the CUs with limited signaling overhead

Enhancing Transport Efficiency by Hybrid Routing Strategy

Traffic is essential for many dynamic processes on real networks, such as internet and urban traffic systems. The transport efficiency of the traffic system can be improved by taking full advantage of the resources in the system. In this paper, we propose a dual-strategy routing model for network traffic system, to realize the plenary utility of the whole network. The packets are delivered according to different "efficient routing strategies" [Yan, et al, Phys. Rev. E 73, 046108 (2006)]. We introduce the accumulate rate of packets, {\eta} to measure the performance of traffic system in the congested phase, and propose the so-called equivalent generation rate of packet to analyze the jamming processes. From analytical and numerical results, we find that, for suitable selection of strategies, the dual- strategy system performs better than the single-strategy system in a broad region of strategy mixing ratio. The analytical solution to the jamming processes is verified by estimating the number of jammed nodes, which coincides well with the result from simulation.Comment: 6 pages, 3 figure

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Mobile edge caching (MEC) and device-to-device (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions

Looking at Vector Space and Language Models for IR using Density Matrices

In this work, we conduct a joint analysis of both Vector Space and Language Models for IR using the mathematical framework of Quantum Theory. We shed light on how both models allocate the space of density matrices. A density matrix is shown to be a general representational tool capable of leveraging capabilities of both VSM and LM representations thus paving the way for a new generation of retrieval models. We analyze the possible implications suggested by our findings.Comment: In Proceedings of Quantum Interaction 201