Feasible negotiation procedures for multiple interdependent negotiations

Within an agent society, agents utilise their knowledge differently to achieve their individual or joint goals. Agent negotiation provides an effective solution to help agents reach agreements on their future behaviour in the society to guarantee their goals can be achieved successfully. Agents may need to conduct Multiple Interdependent Negotiations (MIN) with different opponents and for different purposes, in order to achieve a goal. By considering the complexity of negotiation environments, interdependencies, opponents and issues in the agent society, conducting MIX efficiently Is a challenging research issue. To the best of the authors\u27 knowledge, most of the state-of-art work primarily focuses on single negotiation scenarios and tries to propose sophisticated negotiation protocols and strategies to help individual agents to succeed in single negotiation. However, very little work has been done while considering interdependencies and tradeoffs among multiple negotiations, so as to help both individual agents as well as the agent society, to increase their welfare. This paper promotes the research on agent negotiation from the single negotiation level to the multiple negotiations level. To effectively conduct MIN in an agent society, this paper proposes three feasible negotiation procedures, which attempt to conduct MIN in a successive way, in a concurrent way, and in a clustered way while considering them in different negotiation situations, respectively. A simulated agent society is built to test the proposed negotiation procedures with rand om experimental settings. According to the experimental results, the successive negotiation procedure produces the highest time efficiency, the concurrent negotiation procedure promises the highest profits and success rates, whilst the clustered negotiation procedure provides a well-balanced solution between negotiation efficiency and effectiveness

Hepatitis C Virus Protects Human B Lymphocytes from Fas-Mediated Apoptosis via E2-CD81 Engagement

HCV infection is often associated with B-cell regulatory control disturbance and delayed appearance of neutralizing antibodies. CD81 is a cellular receptor for HCV and can bind to HCV envelope protein 2 (E2). CD81 also participates to form a B cell costimulatory complex. To investigate whether HCV influences B cell activation and immune function through E2 -CD81 engagement, here, human Burkitt's lymphoma cell line Raji cells and primary human B lymphocytes (PHB) were treated with HCV E2 protein and cell culture produced HCV particles (HCVcc), and then the related cell phenotypes were assayed. The results showed that both E2 and HCVcc triggered phosphorylation of IκBα, enhanced the expression of anti-apoptosis Bcl-2 family proteins, and protected Raji cells and PHB cells from Fas-mediated death. In addition, both E2 protein and HCVcc increased the expression of costimulatory molecules CD80, CD86 and CD81 itself, and decreased the expression of complement receptor CD21. The effects were dependent on E2-CD81 interaction on the cell surface, since CD81-silenced Raji cells did not respond to both treatments; and an E2 mutant that lose the CD81 binding activity, could not trigger the responses of both Raji cells and PHB cells. The effects were not associated with HCV replication in cells, for HCV pseudoparticle (HCVpp) and HCVcc failed to infect Raji cells. Hence, E2-CD81 engagement may contribute to HCV-associated B cell lymphoproliferative disorders and insufficient neutralizing antibody production

Multi-image query content-based image retrieval

Content-based retrieval is based on the premise that the similarity measures in the feature space accord well with visual perceptual similarity. Furthermore, the queryby-example paradigm assumes that the query concept is well specified by the user via the example image supplied. The inadequacy of these assumptions has led to the development of several similarity measures and visual features that capture and describe colour, texture and edge information in images. The simultaneous use of multiple features, relevance feedback and more recently and the use of multiple example images in specifying the query are attempts to improve the accuracy at which the query concept can be captured. Results obtained so far are still far from the ideal because of inadequate knowledge of the human perceptual processes and this leads to the so called ”Semantic Gap”. This thesis proposes a multi-image query-by-example content-based image retrieval scheme in which the significance of the components of feature vectors (intra-level) and the significance of the selected features (inter-level) are estimated through weight computation. These weights are used in calculating the feature distances and visual similarity between the query images and the database images. The hypothesis is that by incorporating the significance of features at both levels, the weighted visual similarity measure will yield improved retrieval performance (precision and recall rates). The model of the weight estimation and assignment is developed and experiments are conducted to validate the hypothesis. On average the proposed method improved the precision and recall rates in retrieval tasks on a database of natural images

Autonomous agent negotiation strategies in complex environments

Autonomous agents are software agents that are self-contained, capable of making independent decisions, and taking actions to satisfy internal goals based upon their perceived environment. Agent negotiation is a means for autonomous agents to communicate and compromise to reach mutually beneficial agreements. By considering the complexity of negotiation environments, agent negotiation can be classified into three levels, which are the Bilateral Negotiation Level, the Multilateral Negotiation Level, and the Multiple Related Negotiation Level. In the Bilateral Negotiation Level, negotiations are performed between only two agents. The challenges on this level are how to predict an opponent\u27s negotiation behavior, and how to reach the optimal negotiation outcome when the negotiation environment becomes open and dynamic. The contribution of this thesis on this level is (1) to propose a regression-based approach to learn, analyze and predict the opponent negotiation behaviors in open and dynamic environments based on the historical records of the current negotiation; and (2) to propose a multi-issue negotiation approach to estimate the opponent\u27s negotiation preference, and to search for the bi-beneficial negotiation outcome when the opponent changes its negotiation strategies dynamically. In the Multilateral Negotiation Level, negotiations are performed among more than two agents. Agents need more efficient negotiation protocols, strategies and approaches to handle outside options as well as competitions. Especially when negotiation environments become open and dynamic, future possible upcoming outside options still need to be considered. The challenge in this level is how to guide agents to efficiently and effectively reach agreements in highly open and dynamic negotiation environments, such as e-marketplaces. The contribution of this thesis on this level is (1) to propose a negotiation partner selection approach to filter out unexpected negotiation opponents before a multilateral negotiation starts; (2) to extend a market-driven strategy for multilateral single issue negotiation in dynamic environments by considering upcoming changes of the environment; and (3) to propose a market-based strategy for multilateral multi-issue negotiation by considering both markets situations and agents specifications. In the Multiple Related Negotiation Level, several negotiations are processed together by agents in order to achieve a global goal. These negotiations are not absolutely independent, but some how related. In order to ensure the global goal can be efficiently achieved, factors such as the negotiation procedure, the success rate, and the expected utility for each of these related negotiations should be considered. The contribution of this thesis on this level is to introduce a Multi-Negotiation Network (MNN) and a Multi-Negotiation Inuence Diagram (MNID) to search for the optimal policy to concurrently conduct the multiple related negotiation by considering both the joint success rate and the joint utility

Prediction of partners\u27 behaviors in agent negotiation under open and dynamic environments

Prediction of partners\u27 behaviors in negotiation has been an active research direction in recent years in the area of multi-agent and agent system. So by employing the prediction results, agents can modify their own negotiation strategies in order to achieve an agreement much quicker or to look after much higher benefits. Even though some of prediction strategies have been proposed by researchers, most of them are based on machine learning mechanisms which require a training process in advance. However, in most circumstances, the machine learning approaches might not work well for some kinds of agents whose behaviors are excluded in the training data. In order to address this issue, we propose three regression functions to predict agents\u27 behaviors in this paper, which are linear, power and quadratic regression functions. The experimental results illustrate that the proposed functions can estimate partners\u27 potential behaviors successfully and efficiently in different circumstances

Expectation of trading agent behaviour in negotiation of electronic marketplace

Electronic Commerce has been a very significant commercial phenomenon in recent years, and autonomous agents are widely adopted by business or individuals in electronic marketplaces to fulfill time consuming tasks in trading. Agent negotiation mechanisms are usually applied between conflicted agents in order to reach a mutually beneficial agreement. Prediction of trading agents\u27 strategies and behaviours in negotiation is a very significant research topic in agent negotiation. By employing the prediction results on opponents\u27 possible strategies and behaviours during a negotiation, trading agents can plan and perform corresponding strategies in order to maximize their own profits. Significant achievements have been made on this topic. However, most existing approaches are based on machine learning mechanisms, which may fail to capture opponents\u27 behaviours in open and dynamic electronic marketplaces. In this paper, two agent behaviour expectation approaches are introduced to help trading agents to capture opponents\u27 potential behaviours during a negotiation in complex e-marketplaces. (i) The regression analysis approach focuses on illustrating the main trends of opponents\u27 trading behaviours; (ii) the vector analysis approach pays more attention to identifying opponents\u27 detailed negotiation strategies. The experimental results show the efficiency and efficacy of the two proposed approaches in open and dynamic negotiation environments

A multiagent approach for decentralized voltage regulation in power distribution networks within DGs

Voltage regulation (VR) is a procedure to keep voltages in a distribution network (DN) within normal limits. Conven- tionally, a voltage regulator can read voltage levels from pre- dened measures, and regulate the voltages. However, due to lacking of a distributed generator\u27s (DG) information, the unexpected electricity from a DG will mislead readings on voltages levels, so as to disturb the VR in a DN. Adjust- ing a DG\u27s reactive power output is an alternative way for VR. However, because of limited penetration levels, DGs need to collaborate with other devices in order to provide an eective voltage regulation. Therefore, how to eciently manage DGs to coordinate with other electrical components by considering the dynamics of a DN is a big challenge. In this paper, an innovative multi-agent approach is proposed to solve this problem. The proposed approach employs de- centralized control of agents on local VR, and also supports agents collaboration on global VR through dynamic task al- location and communication

A concurrent interdependent service level agreement negotiation protocol in dynamic service-oriented computing environments

Service Level Agreement (SLA) negotiations are capable of helping define the quality of service in order to meet the customer\u27s service requirements. To date, a large number of negotiation protocols are proposed to handle single SLA negotiations, but little work can be found in handling multiple interdependent SLA negotiations in dynamic negotiation environments. This paper proposes an adaptive protocol for concurrently handling multiple interdependent SLA negotiations in dynamic environments. First, interdependencies between SLA negotiations are represented by a graph-based model. Then, an updating mechanism is proposed to handle the dynamism of multiple SLA negotiations. By applying the proposed updating mechanism, a protocol for concurrently processing SLA negotiations in dynamic environments with unexpected changes of service requests is presented. Experimental results show that the proposed approach can effectively handle unexpected changes of service requests from customers in dynamic environments, and successfully lead multiple SLA negotiations to agreements aligning with customers