Analysis of Integration Methods of Simulation, Evolutionary, and Expert Modeling to Optimize of Resource Conversion Processes

The mathematical model of multi agent resource conversion processes (RCP) is developed by the means of discrete-event simulation systems and expert systems. Within the framework of mathematical model RCP are defined: production system of the RCP structure, that taking into account conflicts origin. The discrete-event simulation and expert system "BPsim2" is developed on the basis of the multi agent RCP mathematical model. The "BPsim2" system is inculcated on the firms in Ural region (Russia).

Multi-agent systems exhibit unexpected, emergent behavior as a result of the complexity of agent behaviors and their interactions. Despite significant research interest in the past decades, computational methods to identify and analyze emergence as it happens are still needed. This paper proposes a software architecture for identifying emergent behavior in a multi-agent system as it happens, using interval-based snapshots and emergent behavior metrics. We propose various distance functions to compare between the multi-agent system under analysis and systems that have been previously shown to exhibit emergent behavior.

Optimization of performance in collective systems often requires altruism. Emergence and stabilization of altruistic behaviors are difficult because the agents incur a cost when behaving altruistically. In this paper we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. Our multi-agent system is made up of autonomous agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so reaching optimal (or near-to-optimal) performances in unknown and changing environments.

Implementing collective behaviour in cooperative multi-agent systems requires several practical constraints to be addressed. In some environments, communication bandwidth is a critical constraint which may compromise the intended cooperative behaviour. This paper introduces a bio-inspired model which invokes collective behaviour in a multi-agent system using passive sensing without any explicit inter-agent communication. An agent looks for the majority of its neighbours either in its left or its right half space using two sensors. For a source localization problem, we compare performance of the proposed model using passive sensing against the well known school-of-fish collective behaviour models using ideal explicit inter-agent communication. For different cue strengths and neighbourhood radii, our results show that the proposed strategy boosts higher levels of group cohesion to make up for the information loss and in certain conditions performs better than the other collective behaviour models.

Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

The presented review analyzes the approaches and methods of using simulation models in the maintenance and repair organization of the complex technical objects and systems. The development of the concept of "Industry 4.0" and the Internet of things involves the transition from periodic and preventive maintenance to predictive maintenance based on the study of processes during the object operation. It is shown that system models based on Petri nets are convenient for describing and analyzing technological processes and equipment maintenance. The effectiveness of timed stochastic colored Petri nets for virtual tests in the design of maintenance and repair procedures in complex systems is revealed. The use of hierarchical Petri nets allows building complex models that describe interrelated processes. A general model based on a hierarchical network is proposed to study the processes of deterioration and degradation of the technical object aggregates and elements and to form a predictive maintenance strategy. The model includes a Gantt chart of production tasks, a set of active and reserve units, maintenance and repair modules. The examples of simulation models on Petri nets for various technical systems are considered. Solutions for flexible production systems, railway bridges, offshore wind turbines, an aircraft fleet, a group of robotic vehicles, and a complex of computer equipment of an enterprise are given. The models of various maintenance strategies are considered and the generality of the models based on hierarchical Petri nets is shown. The article formulates methodological principles for constructing hierarchical networks to simulate the operation and maintenance of technical systems. The article analyzes known software tools for the implementation of timed stochastic colored Petri nets. The summary concludes that the use of simulation models on Petri nets is promising for organizing maintenance and repair of complex technical objects and systems.

Multiagent systems in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control. Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. In order to solve this problem, in our previous research we proposed a method applying genetic programming techniques, in particular Automatically Defined Function Genetic Programming (ADF-GP) (K. Mackin and E. Tazaki, 1999), to allow agents to autonomously learn effective agent communication messaging. For this research we take this approach further and combine training of the agent behavior as well as the communication protocol. By training both behavior and communication we expect to further optimize the system performance. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed method.

Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. Agent Communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most researches on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large‐scale systems. In order to solve this problem, in this paper we propose a new application of existing training methods. By applying Genetic Programming techniques, namely Automatically Defined Function Genetic Programming (ADF‐GP), in combination with pheromone communication features, we allowed the agent system to autonomously learn effective agent communication messaging for coordinated group behavior. A software simulation of a multiagent transaction system aiming at e‐commerce usage will be used to observe the effectiveness of the proposed method in the targeted environment. Using the proposed method, automatic training of a compact and efficient agent communication protocol for the multiagent system was observed.

The mathematical model of resource conversion processes (RCP) is developed by the means of discrete-event simulation systems and expert systems. Within the framework of mathematical model RCP are defined: production system of the RCP structure and rules transformation structure, that taking into account conflicts origin. The discrete-event simulation system "BPsim" is developed on the basis of the RCP mathematical model

In this paper, we propose a formal approach to study the emergent behaviors in multiagent systems with limited communications and environmental constraints. Temporal logic and formal grammars are employed to specify the formal model of multiagents, and based on that an automated approach can be used to identify the emergent behaviors due to interactions among agents. Instead of exploring all possible system state spaces, which may grow exponentially, the proposed approach computes the local group polarization and the local group momentum for generating the emergent behavior set, and accordingly from which the emergent behaviors can be detected. To facilitate simulation and analysis, a MATLAB GUI is further designed to enable us to flexibly select control parameters in formal models of multiagent systems. Simulation results are provided to verify the proposed approach.

This paper describes experiments designed to test the feasibility of linking discrete-event simulation models with expert systems. Two microcomputer systems are used: one contains the simulation model, while the other contains the expert system. The paper shows that by linking a simulation model with an expert system, it is possible to use the combination for simulation parameter learning, expert system development and expert system control of a simulation model.

The paper proposes to implement the methodology for the creation of multi-level intelligent monitoring information systems by employing an agent approach with the use of cloud technologies. Information technology of the multilevel intelligent monitoring is implemented in the form of a multi-agent monitoring information system, which is built for the mobile provision of knowledge for each individual decision-making process. Specially created intellectual agents carry out observation and formalization of the emergencies in heterogeneous objects at appropriate levels of monitoring. Functional monitoring aggregation processes carried out in accordance with the global debt arranged by the customer, the genetics of the intelligence agencies of the required classes are studied multiple times for modeling agents and coordination of their interaction, implemented at the upper, server-level management of the multi-agent monitoring information system. The client level contains a multitude of agents and communication tools with them. Using intelligent agents for solving local information retrieval tasks can increase the diversity of the multi-level intelligent monitoring process. It proposed to apply the process of the aggregation of the multi-agent monitoring information system functions at each level of the monitoring and to implement typical units in the form of intellectual agents guided by multi-layered models. Interaction of the intellectual agents has been formed situationally as typical processes of horizontal formation and vertical connections of the method of the ascending synthesis elements of the multilevel structure of the multi-agent monitoring information system. Agents-classifiers, agent-identifiers, predictor agents are identified and described among the agents for data conversion. It is proposed to adopt the method of the synthesis multilayered models to the peculiarities of their use in the structure of the intelligence agents. It was given an example of using one of the methods of the multilayered modeling in the structure of the agent-identifier of the functional dependencies.

As utility calculus cannot account for an important part of agents’ behaviour in Multi-Agent Systems, researchers have progressively adopted a more normative approach. Unfortunately, social laws have turned out to be too restrictive in real-life domains where autonomous agents’ activity cannot be completely specified in advance and the complexity of the system is ever changing. The idea of Rights is a halfway concept between anarchic and off-line constrained interaction. Rights improve coordination and facilitate social action in Multi-Agent domains through adjusting the coordination mechanisms to the complexity of the system. So far rights have not been tested or proven experimentally. We are comparing experimentally the three mentioned interaction architectures in the domain of agent-based traffic simulation.

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.

Multi agent systems (MAS) play an important role in many of industrial software systems today. MAS are distributed systems and there is no central control for the agents in most of the systems. The lack of central control and the local view of each agent from the whole system may cause some unexpected behaviors which is called emergent behavior. Detecting and fixing these unexpected behaviors is more valuable and cost effective in the requirement and design phases rather than the implementation. In this paper, a method for the detection of emergent behaviors in MAS focusing on analysis phase of MaSE methodology and fixing them in the design phase is presented. The contributions of this work are investigating a specific type of emergent behaviors, the detection of them in MaSE methodology, and proposing solutions for the detected emergent behaviors. The details and the architecture of the system are presented. The method and how to apply this on MaSE is shown along with a case study followed by the discussion part.