基于网格的面向Agent形式化建模框架

提出了一种基于OGSA（open grid services architecture）网格体系结构的面向Agent形式化建模框架——AOMG（agent-oriented modeling based on grid）形式化框架,该框架基于Object-Z语言,吸收了Ⅰ^＊框架和UML中的部分元模型,加入了对Agent与网格环境之间交互关系的描述,以及对Agent服务属性的处理,从而解决了现有方法无法对基于网格环境的分布式系统进行分析与设计建模的问题．给出了AOMG形式化框架中的3类核心模型：组织模型、Agent类模型和Agent服务模型．提供了一组新颖的从组织模型到Agent类模型的形式化语义映射规则,实现了系统模型从Agent抽象层次到对象层次的快速转换．     

Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.     

Tool support for the design of self-optimizing mechatronic multi-agent systems

Complex technical systems, such as mechatronic systems, can exploit networking as well as the computational power available today to achieve an automatic improvement of the technical system performance at run-time through self-optimization. To realize this vision, appropriate means for the design of such self-optimizing mechatronic systems are required. Well-established techniques and tools for the modeling of cognitive behavior, reflective behavior, and control behavior exist. However, to really enable self-optimization and its full potential, these different aspects have to be safely integrated in a manner that remains comprehensible to the designer. In this article, we present how this required integration has been realized at the semantic level by extending the unified modeling language (UML), and at the tool level by integrating the CAE tool CAMeL and the CASE tool Fujaba real-time tool suite. The presented Mechatronic UML approach supports the design of verifiable, complex, reconfigurable mechatronic systems using the multi-agent system metaphor. This work was developed in the course of the Special Research Initiative 614—self-optimizing concepts and structures in mechanical engineering—University of Paderborn, and was published on its behalf and funded by the Deutsche Forschungsgemeinschaft. Sven Burmester, Oliver Oberschelp, Florian Klein and Peter Scheideler are members of the respective research group which left after the paper was submitted.     

Design, development and validation of an agent-based model of electronic auctions

This paper presents the design, development and validation methodology of an agent-based computational model of the B2C electronic auction marketplace. It aims at a comprehensive understanding of the varied issues governing a B2C electronic auction, incorporating the behavior of all relevant agents such as the auctioneer, the consumer and the retailer; and the environment in which these agents operate and interact. In contrast with conventional methods, agent based modeling employs a bottom-up modeling approach where behaviors of individual agents and rules for their interaction, specified at the micro level, give rise to emergent macro level phenomenon. The development methodology should ensure that agent models are aligned with theory, current knowledge of the field and observed phenomena, and output validity of the model also needs to be ascertained. Beginning with a general introduction to agent based computational modeling, this paper formalizes this alignment and validation methodology and elaborates each step, noting the rationale and means for achieving these. The manner in which this process was used in modeling B2C auctions is then described.     

Survey on mechatronic engineering: A focus on design methods and product models

According to the principles of concurrent engineering and integrated design, engineers intend to develop a mechatronic system with a high level integration (functional and physical integrations) based on a well-organised design method. As a result, two main categories of issues have been pointed out: the process-based problems and the design data-related problems. Several approaches to overcome these issues have been put forward. To solve process-based problems, a dynamic perspective is generally used to present how collaboration can be improved during the mechatronic design. For design data-related problems, solutions generally come from product models and how to structure and store the data thanks to the functionality of data and documents management of Product Lifecycle Management systems. To be able to assess design methods and product models, some criteria are proposed in the paper and used to evaluate their added value on integrated design of mechatronic system. After this assessment, main outcomes which focus on the combination of design method and product model for improving the design of mechatronic system are finally discussed.     

Agents that Learn How to Generate Arguments from Other Agents

Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rules for argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to different negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.     

Autonomous agents with norms

In this paper we present some concepts and their relations that are necessary for modeling autonomous agents in an environment that is governed by some (social) norms. We divide the norms over three levels: the private level the contract level and the convention level. We show how deontic logic can be used to model the concepts and how the theory of speech acts can be used to model the generation of (some of) the norms. Finally we give some idea about an agent architecture incorporating the social norms based on a BDI framework.     

一种基于扩展I*框架和UML的面向Agent需求建模方法

以分布式电子商务系统为研究背景，提出了一种基于扩展I^*建模框架和UML的面向Agent需求建模方法．该方法将需求工程划分为早期需求获取和后期需求规格说明两个阶段：在早期需求获取阶段，在I^*策略模型中引入服务依赖概念，用于描述组织背景中Agent的意图和Agent之间的依赖关系，同时描述系统内部Agent和分布式环境中其他Agent之间的依赖关系；在后期需求规格说明阶段，采用扩展的UML在Agent抽象层次上建立统一的需求模型．该方法还为早期需求获取平滑过渡到后期需求规格说明提供了统一的模型映射规则．从而保持模型的一致性．     

An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms. The ED essentially predict the equilibriums outcomes of the MAS where the agents use individual RL algorithms. More specifically, we show how the ED predict the learning trajectories of Q-Learners for iterated games. Moreover, we apply our results to (an extension of) the COllective INtelligence framework (COIN). COIN is a proved engineering approach for learning of cooperative tasks in MASs. The utilities of the agents are re-engineered to contribute to the global utility. We show how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED. Author funded by a doctoral grant of the institute for advancement of scientific technological research in Flanders (IWT).     

A framework for model-based design of agent-oriented software

Agents are becoming one of the most important topics in distributed and autonomous decentralized systems, and there are increasing attempts to use agent technologies to develop large-scale commercial and industrial software systems. The complexity of such systems suggests a pressing need for system modeling techniques to support reliable, maintainable, and extensible design. G-nets are a type of Petri net defined to support system modeling in terms of a set of independent and loosely-coupled modules. In this paper, we customize the basic G-net model to define a so-called "agent-based G-net" that can serve as a generic model for agent design. Then, to progress from an agent-based design model to an agent-oriented model, new mechanisms to support inheritance modeling are introduced. To illustrate our formal modeling technique for multiagent systems, an example of an agent family in electronic commerce is provided. Finally, we demonstrate how we can use model checking to verify some key behavioral properties of our agent model. This is facilitated by the use of an existing Petri net tool.     

Flexible decision control in an autonomous trading agent

Modern electronic commerce creates significant challenges for decision-makers. The trading agent competition for supply-chain management (TAC SCM) is an annual competition among fully-autonomous trading agents designed by teams around the world. Agents attempt to maximize profits in a supply-chain scenario that requires them to coordinate Procurement, Production, and Sales activities in competitive markets. An agent for TAC SCM is a complex piece of software that must operate in a competitive economic environment. We report on results of an informal survey of agent design approaches among the competitors in TAC SCM, and then we describe and evaluate the design of our MinneTAC trading agent. We focus on the use of evaluators – configurable, composable modules for data analysis, modeling, and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports Sales and Procurement decisions, and how those decision process can be modified in useful ways by changing evaluator configurations.     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

Intelligent data analysis applied to debug complex software systems

The emergent behavior of complex systems, which arises from the interaction of multiple entities, can be difficult to validate, especially when the number of entities or their relationships grows. This validation requires understanding of what happens inside the system. In the case of multi-agent systems, which are complex systems as well, this understanding requires analyzing and interpreting execution traces containing agent specific information, deducing how the entities relate to each other, guessing which acquaintances are being built, and how the total amount of data can be interpreted. The paper introduces some techniques which have been applied in developments made with an agent oriented methodology, INGENIAS, which provides a framework for modeling complex agent oriented systems. These techniques can be regarded as intelligent data analysis techniques, all of which are oriented towards providing simplified representations of the system. These techniques range from raw data visualization to clustering and extraction of association rules.     

MULTI-AGENT INFRASTRUCTURES FOR OBJECTIVE AND SUBJECTIVE COORDINATION

Coordination in multi-agented systems (MAS) can be conceived as either an agent activity (the subjective viewpoint) or an activity over agents (the objective viewpoint). The two viewpoints have generated two diverging and often contrasting lines of research, as well as different and noncompatible technologies, however, their integration is mandatory for modeling and engineering complex MAS. In this paper, we explore the issue of integration at both the model and the technology levels.

First, by taking FIPA agents and coordination artifacts as reference notions for subjective and objective approaches, respectively, we sketch a framework where agent interactions with coordination artifacts are modeled as physical acts, deliberated and executed by agents analogously to communicative actions. Then, we show how the JADE infrastructure for FIPA-compliant agents, and the TuCSoN infrastructure providing agents with coordination artifacts can be integrated at the technology level, allowing JADE agents to access TuCSoN tuple centers through JADE services.     

DIAGAL: An Agent Communication Language Based on Dialogue Games and Sustained by Social Commitments

In recent years, social commitment based approaches have been proposed to solve problems issuing from previous mentalistic based semantics for agent communication languages. This paper follows the same line of thought since it presents the latest version of our dialogue game based agent communication language – DIAlogue-Game based Agent Language (DIAGAL) – which allows agents to manipulate the public layer of social commitments through dialogue, by creating, canceling and updating their social commitments. To make apparent such commitments, we consider here Agent Communication Language (ACL) from the dialectic point of view, where agents “play a game” based on commitments. Such games based on commitments are incorporated in the DIAGAL language, which has been developed having in mind the following questions: (a) What kind of structure does the game have? How are rules specified within the game? (b) What kind of games compositions are allowed? (c) How do participants in conversations reach agreement on the current game? How are games opened or closed? Using such games we show how we can study the commitments dynamic to model agent dialogue and we present metrics that can be used to evaluate the quality of a dialogue between agents. Next, we use an example (summer festival organization) to show how DIAGAL can be used in analyzing and modeling automated conversations in offices. Finally, we present the results and analysis of the summer festival simulations that we realized through our dialogue game simulator (DGS).     

Modeling Emotions and Reason in Agent-Based Systems

We analyze how to develop an agent-based system in which agents evolve co-evolutionary endogenous rules of behavior by using best response and emotions. We show that best response is not sufficient to define complete and consistent rules of behavior and we prove that the use of emotions, which complement reason, is necessary to learn rules of behavior. We model four different emotions (apathy, patience, anger and confidence) which enable the agent to deal with the rewards and with others. We propose an algorithm to model automata-based systems incorporating rationality and emotions.     

一种基于3APL的多agent合作系统*

提出了一种新的基于3APL的多agent合作系统.引入合作规划表达式给出基于扩展巴克斯范式(EBNF)个体agent的3APL语法.利用推理规则、结构结合变换规则等概念提出新的多agent合作操作语义.并深入研究多agent合作慎思循环过程,通过例子说明了多agent间如何合作产生和修正规划的过程.     

Agent规范与编程语言SPLAW的语法和语义

本文描述了Ａｇｅｎｔ规范与编程语言ＳＰＬＡＷ的语法，为ＳＰＬＡＷ中的各种语言成分提供了操作语义，并基于标记转换系统给出Ａｇｅｎｔ性质的证明规则。由于ＳＰＬＡＷ的通信部分采用不依赖于具体应用的Ａｇｅｎｔ交互语言ＫＱＭＬ，使遵循同一标准的Ａｇｅｎｔ之间能进行协作问题求解。ＳＰＬＡＷ首次为Ａｇｅｎｔ提供继承支持，使其可以实现具有面向对象特征的多线程系统。ＳＰＬＡＷ屏了为语言引入内部模态词的缺陷，试图从外     

基于对象的软件代理的设计和实现

在对代理和对象技术进行了分析对比的基础上,设计了一种基于对象的软件代理,不仅具有代理的优势而且充分利用目前的对象技术。学给出了基于对象的软件代理系统的实现技术及其对敏捷供应链的解决问题的应用实例。     

进化计算在复杂机电系统设计自动化中的应用综述

复杂机电系统设计自动化是知识自动化的一个重要分支, 在机器人系统设计、高档数控机床设计、智能装备系统设计等方面具有重要的研究意义和应用价值. 本文对进化计算在复杂机电系统设计自动化中的应用进行了综述. 首先, 介绍了几种常用进化计算方法及其优点; 其次, 对进化计算在电子系统、微机电系统和复杂机电系统三个领域的设计自动化进行了较为系统且全面的总结. 然后, 以一类典型的复杂机电系统—机器人系统的设计自动化为代表, 对进化计算在机器人系统设计自动化的研究发展进行了讨论. 最后, 针对进化计算在复杂机电系统设计自动化中存在的共性关键问题进行了讨论与展望.