Convergence Of Agents Research Articles

Stability of a Fractional Opinion Formation Model with and without Leadership Using the New Generalized Hattaf Fractional Derivative

In this paper, we propose and analyze the dynamical behaviors of two opinion formation models, one with leadership and the other without leadership. The two proposed models are formulated by fractional differential equations (FDEs) with the frame of the new generalized Hattaf fractional (GHF) derivative. The stability in the sense of Mittag–Leffler is rigorously established for both models. The convergence of agents’ opinions to the consensus opinion is fully investigated. Numerical simulations are given to illustrate the analytical results.

Multi-Local-Worlds economic and management complex adaptive system with agent behavior and local configuration

<abstract> <p>The central focus of our investigation revolved around the convergence of agents' behavior toward a particular invariant distribution and determining the characteristics of the optimal strategies' distribution within the framework of a dynamical Multi-Local-Worlds complex adaptive system. This system was characterized by the co-evolution of agent behavior and local topological configuration. The study established a representation of an agent's behavior and local graphic topology configuration to elucidate the interaction dynamics within this dynamical context. As an illustrative example, we introduced three distinct agent types—smart agent, normal agent, and stupid agent—each associated with specific behaviors. The findings underscored that an agent's decision-making process was influenced by the evolution of random complex networks driven by preferential attachment, coupled with a volatility mechanism linked to its payment—a dynamic that propels the evolution of the complex adaptive system. Through simulation, we drew a conclusive observation that even when considering irrational behaviors characterized by limited information and memory constraints, the system's state converges to a specific attractor. This underscored the robustness and convergence properties inherent in the dynamical Multi-Local-Worlds complex adaptive system under scrutiny.</p> </abstract>

A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition

This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems.

Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)

The paper gives novel approach Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) for dynamic decision making in the retail application. Furthermore, it put up different cooperation schemes for multiagent cooperative reinforcement learning i.e. EQ learning, EGroup, EDynamic, EGoal driven and Expert agents scheme. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speedup the collection of agents that achieve excellent action policies. Accordingly this approach presents three retailer stores in the retail market place. Retailers can help to each other and can obtain profit from cooperation knowledge through learning their own strategies that just stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train in the circumstances. Assuming significant hypothesis on the vendor’s stock policy, restock period, arrival process of the consumers, the approach is formed as Markov decision process model that makes it possible to design learning algorithms. The proposed algorithms noticeably learn dynamic consumer performance. Moreover, the paper illustrates results of Cooperative Reinforcement Learning Algorithms of three shop agents for the period of one year sale duration and then demonstrated the results using proposed approach for three shop agents for the period of one year sale duration. The results obtained by the proposed expert agent based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment.

Consensus seeking in multi-agent systems with multiplicative measurement noises

In this paper, the consensus problems of the continuous-time integrator systems under noisy measurements are considered. The measurement noises, which appear when agents measure their neighbors’ states, are modeled to be multiplicative. By multiplication of the noises, here, the noise intensities are proportional to the absolute value of the relative states of an agent and its neighbor. By using known distributed protocols for integrator agent systems, the closed-loop system is described in the vector form by a singular stochastic differential equation. For the fixed and switching network topology cases, constant consensus gains are properly selected, such that mean square consensus and strong consensus can be achieved. Especially, exponential mean square convergence of agents’ states to the common value is derived for the fixed topology case. In addition, asymptotic unbiased mean square average consensus and asymptotic unbiased strong average consensus are also studied. Simulations shed light on the effectiveness of the proposed theoretical results.

Convergence of Agents' and Targets' Reports on Intraorganizational Influence Attempts1

Summary The object of the current study was to determine the convergent and discriminant validity of agents' and targets' reports on intraorganizational influence attempts with a structural equation model using latent state-trait analyses. To explain agent-target convergence, we linked the theory of formal organizations to Correspondent Inference Theory. Managers (agents) were asked to describe how they try to influence their boss, a coworker, and a subordinate. These targets also described how the agent tries to influence them. Both agents and targets rated four types of influence attempts twice within 2½ months, namely, rational persuasion, ingratiation, pressure, and upward appeals. In the multitrait-multimethod design, we analyzed 209 complete sets of questionnaires (one agent questionnaire, three target questionnaires, N = 836). The use of structural equation models in analyzing multitrait-multimethod matrices has often resulted in severe difficulties. In the present research, these problems were not encountered. On the whole, the agent-target convergence was low but significant.