Kind Of Multi-agent Systems Research Articles

Robust cooperative tracking of multiagent systems with asymmetric saturation actuator via output feedback

AbstractThe robust tracking control problem of the leader‐follower multiagent systems affected by asymmetric input saturation and external disturbances is addressed in this article, by following three steps. First, an radial basis function neural network (RBFNN) is developed to estimate the external disturbances and supersaturation, where the supersaturation is induced by asymmetric saturation actuator. Second, combining with the developed radial basis function neural network, a reduced‐order observer‐based static protocol and a reduced‐order observer‐based adaptive one are designed for leader‐follower systems with un‐directed communication topology as well as those with directed communication topology, respectively. Third, some mild premises are given to guarantee the semiglobal robust leader‐follower consensus for the above mentioned two kinds of multiagent systems. Finally, the theoretical results are verified by several numerical simulations.

A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems

Nowadays expert systems have been used in different fields. They must be able to operate as quickly and efficiently as possible. So, they need optimization mechanism in their different parts and optimization is a critical part of almost all expert systems. Because of difficulties in real world problems, traditional optimization techniques commonly cannot solve them. Therefore, stochastic algorithms are used to do the optimization in expert systems. Particle swarm optimization (PSO) is one the most famous stochastic optimization algorithms. But this algorithm has some difficulties like losing diversity, premature convergence, trapping in local optimums and imbalance between exploration and exploitation. To overcome these drawbacks, inspired by holonic organization in multi agent systems, a new hierarchical multi group structure for PSO is presented in this paper. Considering the particles in PSO as simple agents, PSO is a kind of multi agent system. Existence of different facilities and organizations in multi agent systems and their great impact on performance encouraged us to use them. So, inspired by holonic multi agent systems, a new structure for PSO is presented. This work has been done for the first time in the literature. Meanwhile, to promote exploration and exploitation ability of proposed structure and create a suitable balance between them, different tasks are assigned to different groups of this structure. So, a holonic PSO with different task allocations (HPSO-DTA) is created. It provides the opportunity to employ all aspects for empowering PSO including parameter settings, neighborhood topologies and learning strategies to enhance the ability of it unlike other versions of PSO that use only one of these aspects to improve their solutions. This structure provides a lot of advantages for PSO. It is a new topological structure that improves the performance of PSO. It provides several leaders with efficient information to guide the particles in the search space. Also, it helps to control suitable information flow between groups and particles in order to preserve diversity and prevent from trapping in local optimums. Meanwhile, with assigning different tasks to different groups of proposed structure, an appropriate balance between exploration and exploitation is created to enhance the performance of the algorithm. In each group, based on its assigned task, particles use different parameters settings, different dynamic neighborhood topologies and different learning strategies which are proposed in this paper to enhance the performance of algorithm. A set of thirty four benchmark functions are used to evaluate the performance of proposed structure. Proposed algorithm is compared with a set of well-known PSO algorithms that their efficiency have been proved. Experimental results and comparative analysis demonstrate good performance of HPSO-DTA compared to other algorithms. Its solution accuracy, convergence speed and robustness is completely appropriate especially in more complicated benchmarks.

An agent-based negotiation model for supplier selection of multiple products with synergy effect

Supplier selection is an important problem in supply chain management (SCM), and has attracted the attention of many researchers. Tremendous effort has been spent on the development of agent-based systems to automate supplier selection negotiation process in SCM applications. In this kind of multi-agent system (MAS), software agents are established to represent various parties and functions involving in the supplier selection negotiation process. Most of current systems only deal with relatively simple negotiations involving the acquisition of one product, they are not sufficient to support complex negotiations involving multiple products with synergy effect. However, in practice, it is common for a purchasing company to procure multiple products simultaneously, and the synergy effects that exist between products could affect the final choice of cooperative suppliers. This paper presents an agent-based negotiation model to automate the supplier selection process involving a bundle of products with synergy effect. A MAS is established to realize the proposed negotiation model for multi-product supplier selection. Furthermore, the negotiation proposal, negotiation protocol, negotiation strategies, and decision making methods involving in the negotiation model are elaborated for the multi-product supplier selection environment. Through the proposed negotiation model, the purchasing company and suppliers can reach agreements on the details of products simultaneously and exploit the synergy effect between products. Finally, illustrative examples are conducted to demonstrate the function and effectiveness of the negotiation model for multi-product supplier selection.

A Safety Ensuring Mechanism for Holonic Manufacturing Systems

This paper discusses a mechanism to ensure safe behaviors of HMSs (Holonic Manufacturing Systems). The change from low-variety high-volume to high-variety low-volume production requires highly flexible and adaptive manufacturing systems. Multi agent systems, in which decisions are made through cooperation among autonomous and cooperative elements, fulfill these requirements by exploiting full abilities of individual elements while eliminating various bottlenecks that exist in conventional systems. However, highly adaptive features induce non-deterministic behaviors of systems, and make it difficult to adopt multi agent systems as primary bases of manufacturing systems. In order to apply these systems to large and complicated applications, mechanisms that make their behaviors more predictable are essential. HMSs are a kind of multi agent systems, and an HMS safety ensuring mechanism proposed here is one of the attempts to make behaviors of multi agent systems more predictable.

Evolutionary patterns of agent organizations

Problems approached by multi-agent systems are typically complex. It is usually difficult to know at system design stage how many agents need to be in the system, what each agent's role is, and how the agents should interact to get optimal performance out of the group. The aim of the testbed presented here is to investigate which kinds of multi-agent systems could be developed to solve ranges of problems, avoiding the need to reorganize the agents from scratch for each task. The agent organization process explored here is based on the agents' knowledge, and not on their tasks. This opens up a new approach for distributed artificial intelligence designers to have their domain organized before the allocation of tasks among agents. These kinds of organizations should be more robust for solving different problems related to the same knowledge. We define information oriented domains for that purpose. An evolutionary approach to the design of a multi-agent system is suggested. Our model is based on a cellular automaton whose rules of dynamics induce the formation of an organization of agents. Patterns of organization obtained empirically are presented. Our knowledge-based organization approach is analyzed both from theoretical and practical perspectives.

A formal framework of multi-agent systems with requirement/service cooperative style

Adopting three kinds of speech acts: request, promise, and inform, this paper analyses the interaction among aents in a kind of multi-agent systems with requirements/services cooperation style (MASr-s). The paper gives the objective model the theoretic satisfaction conditions of three kinds of speech acts in MASr-s. The formal definition of MASr-s has been presented. To evaluate concrete implementation architecture and mechanism of the variant MASr-s, including client/server computing architecture and mechanism, a spectrum of MASr-s has been proposed, which capturesdirect request/passive service mechanism,direct request/active service mechanism,indirect request/active service mechanism, and peerto-peer request/service mechanism. The spectrum shows a thread to improve traditional client/server computing.