Agent-Based Simulation of Animal Behaviour

In the biological literature on animal behaviour, in addition to real experiments and field studies, also simulation experiments are a useful source of progress. Often specific mathematical modelling techniques are adopted and directly implemented in a programming language. Modelling more complex agent behaviours is less adequate using the usually adopted mathematical modelling techniques. The literature on AI and Agent Technology offers more specific methods to design and implement (also more complex) intelligent agents and agent societies on a conceptual level. One of these methods is the compositional multi-agent system design method DESIRE. In this paper it is shown how (depending on the complexity of the required behaviour) a simulation model for animal behaviour can be designed at a conceptual level in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional design method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. A number of experiments reported in the literature on animal behaviour have been simulated for different agent models.     

Mapping visual to textual knowledge representation

In this paper, graphical representations for knowledge structures in the design method DESIRE for component-based design of knowledge and multi-agent systems are presented, together with a graphical editor based on the Constraint Graph environment. Moreover, a translator is described which translates these graphical representations to textual representations in DESIRE. The strength of the combined environment is a powerful—yet easy-to-use—framework to support the development of knowledge-based and multi-agent systems. Finally, a mapping is presented from DESIRE to Conceptual Graphs. This provides a unifying perspective on the knowledge representation format of DESIRE and allows the use of conceptual graph tools to specify and maintain the knowledge representation part of a DESIRE specification.     

Temporal Development Methods for Agent-Based

In this paper we overview one specific approach to the formal development of multi-agent systems. This approach is based on the use of temporal logics to represent both the behaviour of individual agents, and the macro-level behaviour of multi-agent systems. We describe how formal specification, verification and refinement can all be developed using this temporal basis, and how implementation can be achieved by directly executing these formal representations. We also show how the basic framework can be extended in various ways to handle the representation and implementation of agents capable of more complex deliberation and reasoning.This revised version was published online in August 2005 with a corrected cover date.     

Shaping multi-agent systems with gradient reinforcement learning

An original reinforcement learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal. This work has been conducted in part in NICTA’s Canberra laboratory.     

A compositional reasoning system for executing nonmonotonic theories of reasoning

In this paper the framework DESIRE for the design of compositional reasoning systems and multi‐agent systems was applied to build a generic nonmonotonic reasoning system. The outcome is a general reasoning system that can be used to model different nonmonotonic reasoning formalisms and that can be executed by a generic execution mechanism. The main advantages of using DESIRE (for example, compared to a direct implementation in a programming language such as PROLOG) are that the design is generic and has a transparent compositional structure, and the explicit declarative specification of both the static and dynamic aspects of the nonmonotonic reasoning processes, including their control. © 2003 Wiley Periodicals, Inc.     

A Reusable Multi-Agent Architecture for Active Intelligent Websites

In this paper a reusable multi-agent architecture for intelligent Websites is presented and illustrated for an electronic department store. The architecture has been designed and implemented using the compositional design method for multi-agent systems DESIRE. The agents within this architecture are based on a generic information broker agent model. It is shown how the architecture can be exploited to design an intelligent Website for insurance, developed in co-operation with the software company Ordina Utopics and an insurance company.     

Hierarchical multi-agent reinforcement learning

In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL. In this framework, agents are cooperative and homogeneous (use the same task decomposition). Learning is decentralized, with each agent learning three interrelated skills: how to perform each individual subtask, the order in which to carry them out, and how to coordinate with other agents. We define cooperative subtasks to be those subtasks in which coordination among agents significantly improves the performance of the overall task. Those levels of the hierarchy which include cooperative subtasks are called cooperation levels. A fundamental property of the proposed approach is that it allows agents to learn coordination faster by sharing information at the level of cooperative subtasks, rather than attempting to learn coordination at the level of primitive actions. We study the empirical performance of the Cooperative HRL algorithm using two testbeds: a simulated two-robot trash collection task, and a larger four-agent automated guided vehicle (AGV) scheduling problem. We compare the performance and speed of Cooperative HRL with other learning algorithms, as well as several well-known industrial AGV heuristics. We also address the issue of rational communication behavior among autonomous agents in this paper. The goal is for agents to learn both action and communication policies that together optimize the task given a communication cost. We extend the multi-agent HRL framework to include communication decisions and propose a cooperative multi-agent HRL algorithm called COM-Cooperative HRL. In this algorithm, we add a communication level to the hierarchical decomposition of the problem below each cooperation level. Before an agent makes a decision at a cooperative subtask, it decides if it is worthwhile to perform a communication action. A communication action has a certain cost and provides the agent with the actions selected by the other agents at a cooperation level. We demonstrate the efficiency of the COM-Cooperative HRL algorithm as well as the relation between the communication cost and the learned communication policy using a multi-agent taxi problem.     

基于本体的多智能体Internet动态路由研究

在Internet路由方面,传统采用的点对点或广播方式不能满足网络信息传输的效率要求,而采用组播方式缓解这个问题的相关研究大多为静态方法,不能很好地解决Internet提出的动态问题。为了实现高效率、自适应的Internet动态路由策略,在多Agent技术和本体论知识基础上,提出基于本体建立开放式多智能体Internet动态路由结构框架的思想。建立了基于多Agent的体系架构,并使用主体开发工具Protégé描述Internet主动路由的概念主体和任务主体模型,以支持Agent之间的知识共享和通信。根据该体系架构建立基于多Agent的Internet路由控制与分析系统（Multi-Agent Internet Routing System,MAIRS）。通过与相同实验环境下NS2的仿真结果进行比较,表明这个结合多Agent技术和本体方法的体系结构能够满足Internet路由的动态性和互操作性的需求。     

A<Emphasis Type="SmallCaps">rgonauts</Emphasis>: a working system for motivated cooperative agents

This paper presents the Argonauts multi-agent framework which was developed as part of a one year student project at Technische Universität Dortmund. The Argonauts framework builds on a BDI approach to model rational agents that act cooperatively in a dynamic and indeterministically changing environment. However, our agent model extends the traditional BDI approach in several aspects, most notably by incorporating motivation into the agent’s goal selection mechanism. The framework has been applied by the Argonauts team in the 2010 version of the annual multi-agent programming contest organized by Technische Universität Clausthal. In this paper, we present a high-level specification and analysis of the actual system used for solving the given scenario. We do this by applying the GAIA methodology, a high-level and iterative approach to model communication and roles in multi-agent scenarios. We further describe the technical details and insights gained during our participation in the multi-agent programming contest.     

Multi-Agent Modeling Toolkit – MAMT

Multi-agent system consists of two or more agents which cooperate/coordinate with each other in order to solve a complex problem which would be difficult or inappropriate if solved by single agent. Multi-agents are modeled using Agent Unified Modeling Language (AUML) as Unified Modeling Language (UML) notations do not fully express agent properties/behaviors. In this paper, we have proposed Multi-Agent Modeling Toolkit (MAMT) to help a designer in building rapid multi-agent based applications. The purpose of this toolkit is to create agent development environment where the developer can have various facilities like reusability of existing/developed agents, customize built-in agents, etc. MAMT provides the designer with built-in agents which are developed using Java Agent Development (JADE) framework, with the help of these designers can rapidly build multi-agent based applications. Creation and customization of built-in agents is based on the prototype inclusion design pattern and the designer can add or modify methods/behaviors according to their requirement(s); however the changes should be based on FIPA (Foundation of Intelligent Physical Agents) standards and compatible with JADE. MAMT has been evaluated on large number of sample applications; results were very promising and encourage the use of toolkit.     

Discrete-event modeling of multi-agent systems with broadcasting-based parallel composition

Multi-agent systems have been widely used in logistics and manufacturing. In this paper we develop an automaton-based modeling framework for a special type of multi-agent systems, where agents are instantiated from a finite number of finite-state automaton templates, and interactions among agents are characterized via cooperative synchronization and broadcasting. To describe the compositional behavior of all agents, we propose a novel broadcasting-based parallel composition rule and show that it is commutative and associative. The effectiveness of this modeling framework and the parallel composition rule is illustrated in a simple multi-agent system.     

Manufacturing planning and predictive process model integration using software agents

Intelligent agents provide a means to integrate various manufacturing software applications. The agents are typically executed in a computer-based collaborative environment, referred to as a multi-agent system. The National Institute of Standards and Technology (NIST) has developed a prototype multi-agent system supporting the integration of manufacturing planning, predictive machining models, and manufacturing control. The agents within this platform have access to a knowledge base, a manufacturing resource database, a numerical control programming system, a mathematical equation solving system, and a computer-aided design system. Intelligence has been implemented within the agents in rules that are used for process planning, service selection, and job execution. The primary purposes for developing such a platform are to support the integration of predictive models, process planning, and shop floor machining activities and to demonstrate an integration framework to enable the use of machining process knowledge with higher-level manufacturing applications.     

基于多Agent的虚拟企业结构与信息交互

虚拟企业具有分布性、自主性，互操作性，开放性等特点，传统的企业信息系统已经不能满足虚拟企业的这些特殊要求，在分析了多代理技术在虚拟企业中应用的必要性和可行性基础上，将多代理技术引入到虚拟企业信息系统中，构造了一个基于市场机制（Agora）的多Agent虚拟企业体系结构，在此基础上给出了Agent间的合作与协商模型，并描述了Agent之间的信息交互过程，最后对支持虚拟企业生命周期的原型系统的实现过程及信息交互过程进行了描述。     

动态构造虚拟公共网络服务的一个多agent协商模型

该文通过对IP网络管理中多个移动agent动态构造虚拟公共网络VPN的研究,采用基于QoS服务的多属性的动态协商定价模型,在多agent之间形成了利益最大化的服务协议,以此应用实例表明移动agent是解决异构、分布、自治环境的复杂IP网管的有效软件体系结构。     

Emergence and refinement

Emergent behaviour—system behaviour not determined by the behaviours of system components when considered in isolation—is commonplace in multi-agent systems, particularly when agents adapt to environmental change. This article considers the manner in which Formal Methods may be used to authenticate the trustworthiness of such systems. Techniques are considered for capturing emergent behaviour in the system specification and then the incremental refinement method is applied to justify design decisions embodied in an implementation. To demonstrate the approach, one and two-dimensional cellular automata are studied. In particular an incremental refinement of the ‘glider’ in Conway’s Game of Life is given from its specification.     

Synchronization of multi-agent systems without connectivity assumptions

Multi-agent systems arise from diverse fields in natural and artificial systems, such as schooling of fish, flocking of birds, coordination of autonomous agents. In multi-agent systems, a typical and basic situation is the case where each agent has the tendency to behave as other agents do in its neighborhood. Through computer simulations, Vicsek et al. (1995) showed that such a simple local interaction rule can lead to a certain kind of cooperative phenomenon (synchronization) of the overall system, if the initial states are randomly distributed and the size of the system population is large. Since this model is of fundamental importance in understanding the multi-agent systems, it has attracted much research attention in recent years. In this paper, we will present a comprehensive theoretical analysis for this class of multi-agent systems under a random framework with large population, but without imposing any connectivity assumptions as did in almost all of the previous investigations. To be precise, we will show that for any given and fixed model parameters concerning with the interaction radius r and the agents’ moving speed v, the overall system will synchronize as long as the population size n is large enough. Furthermore, to keep the synchronization property as the population size n increases, both r and v can actually be allowed to decrease according to certain scaling rates.     

Coordinating Agents in Organizations Using Social Commitments

One of the main challenges faced by the multi-agent community is to ensure the coordination of autonomous agents in open heterogeneous multi-agent systems. In order to coordinate their behaviour, the agents should be able to interact with each other. Social commitments have been used in recent years as an answer to the challenges of enabling heterogeneous agents to communicate and interact successfully. However, coordinating agents only by means of interaction models is difficult in open multi-agent systems, where possibly malevolent agents can enter at any time and violate the interaction rules. Agent organizations, institutions and normative systems have been used to control the way agents interact and behave. In this paper we try to bring together the two models of coordinating agents: commitment-based interaction and organizations. To this aim we describe how one can use social commitments to represent the expected behaviour of an agent playing a role in an organization. We thus make a first step towards a unified model of coordination in multi-agent systems: a definition of the expected behaviour of an agent using social commitments in both organizational and non-organizational contexts.     

Artifacts in the A&;A meta-model for multi-agent systems

In this article we focus on the notion of artifact for agents in multi-agent systems (MAS) as a basis for a new meta-model promoting the modelling and engineering of agent societies and MAS environment as first-class entities. Its conceptual foundations lay upon theories and results coming from computational sciences as well as from organisational and cognitive sciences, psychology, computer supported cooperative work (CSCW), anthropology and ethology. In the resulting agents & artifacts (A&A) meta-model, agents are the (pro-)active entities in charge of the goals/tasks that altogether build up the whole MAS behaviour, whereas artifacts are the reactive entities providing the services and functions that make individual agents work together in a MAS, and that shape agent environment according to the MAS needs. After presenting the scientific background, we define the notions of artifact in the A&A meta-model, discuss how it affects the notion of intelligence in MAS, and show its application to a number of agent-related research fields.