Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Optimization Design ◽  
Design Space ◽  
A Priori ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a virtual team of specialized strategic software agents to cooperate and evolve to adaptively search an optimization design space. Our goal is to demonstrate and understand how such dynamically evolving teams may search more effectively than any single agent or a priori set strategy. We present a core framework and methodology that has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that may occur during the optimization process. An evolutionary approach is used, but evolution occurs at the strategic rather than the solution level, where the strategies of agents in the team are the decisions for when and how to choose and alter a solution, and the agents evolve over time. As an application of this approach in a static domain, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, with each agent employing a different solution strategy, must evolve to apply the solution strategies, which are most useful given the solution set at any point in the process. We discuss the extensions to our preliminary work that will make our framework useful to the design and optimization community.

Evolutionary Multi-Agent Systems: An Adaptive Approach to Optimization in Dynamic Environments

2008 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Engineering Design ◽  
Dynamic Environments ◽  
Optimization Process ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Engineering Design Problems ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a team of autonomous software agents to be effective in unknown and changing optimization environments by evolving to use the most successful algorithms at the points in the optimization process where they will be the most effective. We present the core framework and methodology which has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that occur during the optimization process — making our approach extremely flexible to the kinds of dynamic environments encountered in engineering design problems. An evolutionary approach is used, but evolution occurs at the strategic, rather than solution level — where the strategies of agents in the team (the decisions for picking, altering, and inserting a solution) evolve over time. As an application of this approach, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, each agent running a different solution strategy, must evolve to apply the solution strategies which are most useful given the set at any point in the process. As a team, the evolutionary agents produce better solutions than any individual algorithm. We discuss the extensions to our preliminary work that will make our framework highly useful to the design and optimization community.

Second-order consensus for a class of uncertain multi-agent systems subject to input saturation

2018 ◽  
Vol 41 (7) ◽  
pp. 1957-1964 ◽  
Author(s):  
Ming-Can Fan ◽  
Miaomiao Wang
Keyword(s):  
A Priori ◽  
Input Saturation ◽  
Second Order ◽  
Control Technique ◽  
Control Input ◽  
Multi Agent Systems ◽  
State Information ◽  
Agent Systems ◽  
Barbalat’S Lemma ◽  
Multi Agent

This paper investigates the leaderless and leader-following consensus problem for a class of second-order multi-agent systems subject to input saturation, that is, the control input is required to be a priori bounded. Moreover, the control coefficients are assumed to be unavailable, which cannot be lower or upper bounded by any known constants. Distributed consensus protocols are proposed based only on agents’ own velocity state information and relative position state information among neighbouring agents and the leader. By virtue of the adaptive control technique, algebraic graph theory and Barbalat’s lemma, it is proved that the states of the multi-agent systems can achieve consensus under the assumption that the interconnection topology is undirected and connected. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results.

A System Approach to Describing, Analysing and Control of the Behaviour of Agents in MAS

2010 ◽  
pp. 253-273
Author(s):  
František Capkovic
Keyword(s):  
System Analysis ◽  
Single Agent ◽  
State Equation ◽  
Control Synthesis ◽  
System Control ◽  
Multi Agent Systems ◽  
Linear Discrete System ◽  
Multi Agent ◽  
System Properties ◽  
And Control

The Petri nets (PN)-based analytical approach to describing both the single agent behaviour as well as the cooperation of several agents in MAS (multi agent systems) is presented. PN yield the possibility to express the agent behaviour and cooperation by means of the vector state equation in the form of linear discrete system. Hence, the modular approach to the creation of the MAS model can be successfully used too. Three different interconnections of modules (agents, interfaces, environment) expressed by PN subnets are introduced. The approach makes possible to use methods of linear algebra. Moreover, it can be successfully used at the system analysis (e.g. the reachability of states), at testing the system properties, and even at the system control synthesis.

scholarly journals Nonverbal leadership emergence in walking groups

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Maria Lombardi ◽  
William H. Warren ◽  
Mario di Bernardo
Keyword(s):  
A Priori ◽  
Team Sports ◽  
Personal Factors ◽  
Multi Agent Systems ◽  
Leadership Emergence ◽  
Emergency Evacuations ◽  
Multi Agent ◽  
Group Members ◽  
Walking Group ◽  
Specific Scenario

Abstract The mechanisms underlying the emergence of leadership in multi-agent systems are under investigation in many areas of research where group coordination is involved. Nonverbal leadership has been mostly investigated in the case of animal groups, and only a few works address the problem in human ensembles, e.g. pedestrian walking, group dance. In this paper we study the emergence of leadership in the specific scenario of a small walking group. Our aim is to propose a rigorous mathematical methodology capable of unveiling the mechanisms of leadership emergence in a human group when leader or follower roles are not designated a priori. Two groups of participants were asked to walk together and turn or change speed at self-selected times. Data were analysed using time-dependent cross correlation to infer leader-follower interactions between each pair of group members. The results indicate that leadership emergence is due both to contextual factors, such as an individual’s position in the group, and to personal factors, such as an individual’s characteristic locomotor behaviour. Our approach can easily be extended to larger groups and other scenarios such as team sports and emergency evacuations.

Leader-Following Consensus Control of General Linear Multi-Agent Systems With Diverse Time-Varying Input Delays

2017 ◽  
Vol 140 (6) ◽  
Author(s):  
Chengzhi Yuan
Keyword(s):  
Single Agent ◽  
General Linear ◽  
Control Synthesis ◽  
Time Varying ◽  
Multi Agent Systems ◽  
Consensus Control ◽  
Agent Systems ◽  
Leader Following ◽  
Multi Agent ◽  
Input Delays

This paper addresses the problem of leader-following consensus control of general linear multi-agent systems (MASs) with diverse time-varying input delays under the integral quadratic constraint (IQC) framework. A novel exact-memory distributed output-feedback delay controller structure is proposed, which utilizes not only relative estimation state information from neighboring agents but also local real-time information of time delays and the associated dynamic IQC-induced states from the agent itself for feedback control. As a result, the distributed consensus problem can be decomposed into H∞ stabilization subproblems for a set of independent linear fractional transformation (LFT) systems, whose dimensions are equal to that of a single agent plant plus the associated local IQC dynamics. New delay control synthesis conditions for each subproblem are fully characterized as linear matrix inequalities (LMIs). A numerical example is used to demonstrate the proposed approach.

A Multi-Agent Framework for Collaborative Engineering Design and Optimization

2004 ◽  
Author(s):  
Qi Hao ◽  
Weiming Shen ◽  
Zhan Zhang ◽  
Seong-Whan Park ◽  
Jai-Kyung Lee
Keyword(s):  
Engineering Design ◽  
Rapid Development ◽  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Engineering Project ◽  
Development Environment ◽  
Collaborative Engineering ◽  
Heterogeneous Agent ◽  
Design And Optimization ◽  
Multi Agent

Agent technology is playing an increasingly important role in developing intelligent, distributed and collaborative applications. The innate difficulties of interoperation between heterogeneous agent communities and rapid construction of multi-agent systems have motivated the emergence of FIPA specifications and the proliferation of multi-agent system platforms or toolkits that implement FIPA specifications. In this paper, a FIPA compliant multi-agent framework called AADE (Autonomous Agent Development Environment) is presented. This framework, originating from the engineering fields, can facilitate the rapid development of collaborative engineering applications (especially in engineering design and manufacturing fields) through the provision of reusable packages of agent-level components and programming tools. An agent oriented engineering project on the development of an e-engineering design and optimization environment is designed and developed based on the facilities provided by the AADE framework.

scholarly journals Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

2019 ◽  
Vol 3 (2) ◽  
pp. 21 ◽  
Author(s):  
David Manheim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Failure Modes ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Coordination Failures ◽  
Agent Systems ◽  
Important Challenge ◽  
Multi Agent ◽  
Distributional Shifts

An important challenge for safety in machine learning and artificial intelligence systems is a set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart’s or Campbell’s law. This paper presents additional failure modes for interactions within multi-agent systems that are closely related. These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case, and are also already occurring, largely unnoticed. After motivating the discussion with examples from poker-playing artificial intelligence (AI), the paper explains why these failure modes are in some senses unavoidable. Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes: accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking. The paper then discusses how extant literature on multi-agent AI fails to address these failure modes, and identifies work which may be useful for the mitigation of these failure modes.

scholarly journals Mechanistic Modeling of Biochemical Systems Without A Priori Parameter Values Using the Design Space Toolbox v.3.0

2020 ◽  
Author(s):  
Miguel Á. Valderrama-Gómez ◽  
Jason G. Lomnitz ◽  
Rick A. Fasani ◽  
Michael A. Savageau
Keyword(s):  
Design Space ◽  
A Priori ◽  
Design Principles ◽  
Mechanistic Modeling ◽  
Design And Optimization ◽  
Biological Design ◽  
Biological Phenomena ◽  
Biochemical Systems ◽  
Space Formalism ◽  
Parameter Values

SummaryMechanistic models of biochemical systems provide a rigorous kinetics-based description of various biological phenomena. They are indispensable to elucidate biological design principles and to devise and engineer systems with novel functionalities. To date, mathematical analysis and characterization of these models remain a challenging endeavor, the main difficulty being the lack of information for most system parameters. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism that enables mechanistic modeling of complex biological processes without requiring previous knowledge of the parameter values involved. This is achieved by making use of a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are predicted in a second step. DST3 represents the most generally applicable implementation of the Design Space formalism to date and offers unique advantages over earlier versions. By expanding the capabilities of the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and guiding the design and optimization of novel synthetic circuits.

scholarly journals Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns

2019 ◽  
Author(s):  
Yong Liu ◽  
Yujing Hu ◽  
Yang Gao ◽  
Yingfeng Chen ◽  
Changjie Fan
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Transfer ◽  
Value Function ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Markov Decision ◽  
Dimensional State Space ◽  
Multi Agent ◽  
Function Transfer

Many real-world problems, such as robot control and soccer game, are naturally modeled as sparse-interaction multi-agent systems. Reutilizing single-agent knowledge in multi-agent systems with sparse interactions can greatly accelerate the multi-agent learning process. Previous works rely on bisimulation metric to define Markov decision process (MDP) similarity for controlling knowledge transfer. However, bisimulation metric is costly to compute and is not suitable for high-dimensional state space problems. In this work, we propose more scalable transfer learning methods based on a novel MDP similarity concept. We start by defining the MDP similarity based on the N-step return (NSR) values of an MDP. Then, we propose two knowledge transfer methods based on deep neural networks called direct value function transfer and NSR-based value function transfer. We conduct experiments in image-based grid world, multi-agent particle environment (MPE) and Ms. Pac-Man game. The results indicate that the proposed methods can significantly accelerate multi-agent reinforcement learning and meanwhile get better asymptotic performance.

scholarly journals DEVELOPMENT OF A MULTI-AGENT SYSTEM FOR THE ASSESSMENT OF ECOLOGICAL INFLUENCES OF TRANSPORT MEGAPROJECTS: TAKING INTO ACCOUNT THE INFORMATION ENVIRONMENT FACTOR

2020 ◽  
Vol 3 (1) ◽  
pp. 61-67
Author(s):  
Tatyana N. Yesikova ◽  
Svetlana V. Vakhrusheva
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
A Priori ◽  
Dynamic Environment ◽  
Information Environment ◽  
Information Space ◽  
Bering Strait ◽  
Multi Agent Systems ◽  
Multi Agent ◽  
Business Entities

The paper considers the issues of accounting and reflection in multi-agent systems of the influence of the information environment, information flows on agent behavior and the assessment of consequences, including environmental ones, of decisions made by them at various stages of large-scale infrastructure projects. The information space is a priori a multidimensional dynamic environment that is continuously updated and transformed, sometimes under the primacy of the interests of individual agents or influence groups, and much less frequently from the standpoint of ensuring the viability of the economic system as a whole. A large-scale project for the construction of a transcontinental highway (TKS) through the Bering Strait was chosen as the object of study. The article provides a fairly detailed description of the groups of agents involved in the decision-making process, as well as the elements of the information space that are significant for an agent at certain stages of its activity. To model the influence of the information space on decision-making processes by agents of different hierarchy levels (business entities, managerial entities, etc.), algorithms and special procedures have been developed.

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