Multi-agent based supply chain management with dynamic reconfiguration capability

Supply chain management (SCM) has received increased attention in a globally challenging environment as companies face the necessity to improve customer service and maximize profit. Therefore, dynamic reconfiguration capability is vital for supply chain management to respond to changing customer requirements and operating environments. On the other hand, for its flexible and autonomous characteristics, multi-agent systems are a viable technology for SCM, and have been widely applied in SCM. To this end, dynamic reconfiguration in agent-based SCM systems is proposed from autonomy oriented computing point of view. The performance of agent-based SCM with dynamic reconfiguration is evaluated under a modified TAC SCM scenario. With a dynamic reconfigurable SCM system, new products and processes can be introduced with considerably less expense and ramp-up time.

Observer-based robust finite-time cooperative consensus control for multi-agent networks

This paper studies the finite-time consensus tracking control for multi-agent networks. The time-varying control input and the velocity of the leader is unknown to any follower. Only the position of the leader is known to its neighbors. We first propose a new finite-time multiple-surface sliding mode observer to estimate the leader's velocity. It is seen that the estimation error of the observer can converge to zero in a finite time. Then, we prove that finite-time consensus tracking of multi-agent networks can be achieved on a new terminal sliding mode surface. Simulation results are presented to validate the analysis.

Finite-time consensus algorithm of multi-agent networks

This paper is concerned with leader-follower finite-time consensus control of multi-agent networks with input disturbances. Terminal sliding mode control scheme is used to design the distributed control law. A new terminal sliding mode surface is proposed to guarantee finite-time consensus under fixed topology, with the common assumption that the position and the velocity of the active leader is known to its neighbors only. By using the finite-time Lyapunov stability theorem, it is shown that if the directed graph of the network has a directed spanning tree, then the terminal sliding mode control law can guarantee finite-time consensus even under the assumption that the time-varying control input of the active leader is unknown to any follower.

Enhancements to fraud and deception prevention in open multi-agent marketplaces

Fraud and deception in online marketplaces has been an on-going problem. This thesis proposes novel techniques and mechanisms using agent technology to protect buyers and sellers in online environments such as eBay. The proposed solution has been rigorously tested and the results show good commercial promise.

Co-ordination in multi-agent systems

A multi-agent system is a complex software system which is composed of many relative autonomous smaller softwares called agents. The research on multi-agent systems is concerned with the interaction and coordination among these agents to let them help each other to solve complicated problems, such as finance investment management. The principal contributions represented by these 50 selected papers are "cooperation under uncertainty in distributed expert systems (DESs)", "a tool and algorithms to build DESs", and "information gathering and decision making in multi-agent systems (MASs)".

Incorporating fairness into development of an integrated multi-agent online dispute resolution environment

The paper describes the development of an integrated multi-agent online dispute resolution environment called IMODRE that was designed to assist parties involved in Australian family law disputes achieve legally fairer negotiated outcomes. The system extends our previous work in developing negotiation support systems Family_Winner and AssetDivider. In this environment one agent uses a Bayesian Belief Network expertly modeled with knowledge of the Australian Family Law domain to advise disputants of their Best Alternatives to Negotiated Agreements. Another agent incorporates the percentage split of marital property into an integrative bargaining process and applies heuristics and game theory to equitably distribute marital property assets and facilitate further trade-offs. We use this system to add greater fairness to Family property law negotiations.

Adaptive data based neural network leader-follower control of multi-agent networks

In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

A multi-agent incremental negotiation scheme for meetings scheduling

We present an agent-oriented approach to the meeting scheduling problem and propose an incremental negotiation scheme that makes use of a hierarchical structure of an individual agent's working knowledge. First, we formalise the meeting scheduling problem in a multi-agent context, then elaborate on the design of a common agent architecture of all agents in the system. As a result, each agent becomes a modularised computing unit yet possesses high autonomy and robust interface with other agents. The system reserves the meeting participants' privacy since there are no agents with dominant roles, and agents can communicate at an abstract level in their hierarchical structures

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.

A novel trust measurement method based on certified belief in strength for a multi-agent classifier system

A novel trust measurement method, namely, certified belief in strength (CBS), for a multi-agent classifier system (MACS) is proposed in this paper. The CBS method aims to improve the performance of the constituent agents of the MACS, viz., the fuzzy min-max (FMM) neural network classifier. Trust measurement is accomplished using reputation and strength of the constituent agents. Trust is built from strong elements that are associated with the FMM agents, allowing the CBS method to improve the performance of the MACS. An auction procedure based on the sealed bid, namely, the first price method, is adopted for the MACS in determining the winning agent. The effectiveness of the CBS method and the bond (based on trust) is verified by using a number of benchmark data sets. The results demonstrate that the proposed MACS-CBS model is able to produce better accuracy and stability as compared with those from other existing methods. © 2012 Springer-Verlag London.