Machine morality: bottom-up and top-down approaches for modelling human moral faculties

The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

Analogical mechanisms

In recent years there has been with the development of research on machine intelligence an increasing interest in the characterization of analogical thinking in artificial intelligence, This research has implications for a variety of different areas: e.g., expert systems, intelligent tutoring, natural language understanding. Thus far, research has had a strong cognitive science orientation, as cognitive psychologists and artificial intelligence researchers have combined to uncover human analogical abilities and computationally model these types of abilities. This paper reviews the trends which have occurred in the short history of this field and assesses some of the future problems which it is likely to face.     

基于生态演化的通用智能系统结构模型研究

从系统论、认知神经科学和生态演化的角度看, 智能是指生物体根据环境、条件、目标, 自适应地调整自身或调度各种资源实现目标的能力, 智能起源于生命, 智能是生物的基本特征.借助于脑神经系统演化的历史, 展示了自然智能的演化过程, 并由此构建了一个基于生态演化的通用智能系统结构模型, 系统地分析了一般智能系统的普遍性、开放性、动态演化性、相对稳定性、功能性、结构性、依附性、相对独立性、可延续性等基本特征.论文根据智能演化进程将智能系统分为7级, 利用智能系统结构模型分类探索专用人工智能和通用人工智能的发展方向以及有关智能系统的学习方法.这些工作对人工智能和智能科学基础理论研究与应用具有一定的启发意义.     

Distributed artificial intelligence for runtime feature-interactionresolution

The feature-interaction problem has many different instances. It is argued that some instances lend themselves to a distributed artificial intelligence (DAI) approach. The use of DAI techniques in current telecommunications systems appears quite natural in light of two trends in the way these systems are designed: the distribution of functionality and the incorporation of intelligence. The author illustrates the relevance of DAI techniques to the feature-interaction problem by discussing existing work (lodes, team-CPS, multistage negotiation, and negotiating agents) that address one or more instances of the problem. He further identifies the kind of cooperation and coordination that the feature-interaction problem requires and the interesting research problems it poses to distributed artificial intelligence     

多Agent系统分布式问题求解的代数模型方法(Ⅰ): 社会行为、社会局势和社会动力学

该组论文提出一种新的代数模型方法，用于多Agent系统超分布超并行社会智能问题求解，该方法通过社会动力学和社会智能，统一地处理各种复杂的并行的社会行为，用于求解用常规方法难以处理的许多社会交互问题，本文是组合论文中第一篇，提出多Agent系统分布式问题求解的代数模型结构，讨论多Agent系统中典型社会行为模式及其性质，建立形式化描述，同时也论述了代数模型中的社会局势和社会动力学。     

A View on Human Goal-Directed Activity and the Construction of Artificial Intelligence

Although activity aimed at the construction of artificial intelligence started about 60 years ago however, contemporary intelligent systems are effective in very narrow domains only. One of the reasons for this situation appears to be serious problems in the theory of intelligence. Intelligence is a characteristic of goal-directed systems and two classes of goal-directed systems can be derived from observations on animals and humans, one class is systems with innately and jointly determined goals and means. The other class contains systems that are able to construct arbitrary goals and means. It is suggested that the classes (that implicitly underlie most models of artificial intelligence) are insufficient to explain human goal-directed activity. A broader approach to goal-directed systems is considered. This approach suggests that humans are goal-directed systems that jointly synthesize arbitrary goals and means. Neural and psychological data favoring this hypothesis and its experimental validation are considered. A simple computer model based on the idea of joint synthesis to simulate goal-directed activity is presented. The usage of the idea of joint synthesis for the construction of artificial intelligence is discussed.     

Machine intelligence-the animat path to intelligent adaptivebehaviour

The animat path to artificial intelligence (AI), a bottom-up approach to creating intelligent systems, is described. Using this approach, artificial creatures or agents animats are constructed in an environment. They begin simply and are gradually made more and more complex, exhibiting more and more complex behaviors at each step. The goals and accomplishments of five research projects incorporating the animat path to AI methodology are reviewed     

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.     

交叉融合的“人工智能+”学科建设探索与实践

目前,人类正迈入智能时代,人工智能与经济、社会的发展深度融合,成为了孕育新一轮科技革命和产业变革的核心推动力量,培养智能社会建设所需要的跨学科人工智能创新创业人才变得极为必要。为了适应智能时代对人才培养的这一迫切需求,高校需要加强智能类学科建设,探索交叉融合的“人工智能+”学科建设新模式,提升人工智能领域学科地位并创新拓展其他学科的发展建设方向。文中提出了“人工智能+”学科建设的交叉融合模式:一方面,从人工智能学科内涵出发,依托计算机科学与技术学科开展人工智能基础学科方向建设,夯实人工智能领域学科的基础;另一方面,结合社会经济产业发展的重点领域方向布局,依托相关领域优势学科发展建设行业领域智能化新学科方向,实现人工智能学科建设与其他领域学科建设的共融共生、相互助力、协同发展。在重庆邮电大学和重庆市的学科建设中,这一模式发挥了显著作用,为人工智能领域的学科建设提供了有益参考。     

Why deep neural nets cannot ever match biological intelligence and what to do about it?

The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artificial intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks.     

基于HLA的智能仿真支撑环境研究

引入了分布式人工智能（DAI）中多Agent的概念，将HLA仿真技术框架和多Agent技术相结合，提出了一种具有智能特性的仿真支撑环境，实现了一个通用的Agent仿真对象模型，介绍了如何利用Agent通信语言KQML进行互操作的方法，描述了仿真系统的工作过程。     

Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system

Personalized production has emerged as a result of the increasing customer demand for more personalized products. Personalized production systems carry a greater amount of uncertainty and variability when compared with traditional manufacturing systems. In this paper, we present a smart manufacturing system using a multi-agent system and reinforcement learning, which is characterized by machines with intelligent agents to enable a system to have autonomy of decision making, sociability to interact with other systems, and intelligence to learn dynamically changing environments. In the proposed system, machines with intelligent agents evaluate the priorities of jobs and distribute them through negotiation. In addition, we propose methods for machines with intelligent agents to learn to make better decisions. The performance of the proposed system and the dispatching rule is demonstrated by comparing the results of the scheduling problem with early completion, productivity, and delay. The obtained results show that the manufacturing system with distributed artificial intelligence is competitive in a dynamic environment.     

Applying artificial intelligence to virtual reality: Intelligent virtual environments

Research into virtual environments on the one hand and artificial intelligence and artificial life on the other has largely been carried out by two different groups of people with different preoccupation and interests, but some convergence is now apparent between the two fields. Applications in which activity independent of the user takes place- involving crowds or other agents- are beginning to be tackled, while synthetic agents, virtual humans, and computer pets are all areas in which techniques from the two fields require strong integration. The two communities have much to learn from each other if wheels are not to be reinvented on both sides. This paper reviews the issues arising from combining artificial intelligence and artificial life techniques with those of virtual environments to produce just such intelligent virtual environments. The discussion is illustrated with examples that include environments providing knowledge to direct or assist the user rather than relying entirely on the user's knowledge and skills, those in which the user is represented by a partially autonomous avatar, those containing intelligent agents separate from the user, and many others from both sides of the area.     

Multi‐agent distributed intelligent system based on fuzzy decision making

Generally decision making for solving ill‐structured problems in DSS takes place in uncertain situations. The main drawbacks of existing traditional DSS are inefficiencies associated with dealing with complex models and large databases. Usually a fuzzy DSS has many input variables and, hence, its knowledge base, containing the totality of fuzzy rules, is very large. Large rule base leads to disadvantages in speed, reliability, and complexity of DSS. This paper introduces an alternative concept for designing fuzzy DSS based on multi‐agent distributed artificial intelligent technology and fuzzy decision making. The main idea of the proposed DSS is based on granulation of the overall system intelligence between cooperative autonomous intelligent agents capable of competing and cooperating with each other in order to propose a total solution to the problem and organization (combining individual solutions) of the proposed solution into the final solution. It is supposed that every agent in DSS is characterized by a set of fuzzy criteria of unequal importance and definition of a “winner” agent is based on multi‐criteria fuzzy decision making involving unequal objectives. © 2000 John Wiley & Sons, Inc.     

伦理智能体及其设计:现状和展望

智能体一直是人工智能的主要研究领域之一,任何独立的能够同环境交互并自主决策的实体都可以抽象为智能体.随着人工智能从计算智能到感知智能,再到认知智能的发展,智能体已逐步渗透到无人驾驶、服务机器人、智能家居、智慧医疗、战争武器等人类生活密切相关的领域.这些应用中,智能体与环境、尤其是与人类和社会的交互愈来愈突出,其中的伦理...     

Prolegomena to any future artificial moral agent

As artificial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an artificial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing artificial moral agents result both from controversies among ethicists about moral theory itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a ‘moral Turing Test’ is considered and the computational difficulties accompanying the different types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people and other sentient beings are great. The development of machines with enough intelligence to assess the effects of their actions on sentient beings and act accordingly may ultimately be the most important task faced by the designers of artificially intelligent automata.     

AI Approaches to Cognitive Systems �C The Example of Spatial Cognition

Cognitive abilities can be studied by observing and interpreting natural systems or by developing artificial systems that interact with their environments in intelligent ways. Cognitive systems research connects both approaches. Typically, human requirements are in the focus of interest and systems are developed to interact with humans in as natural a way as possible. To achieve this goal, a deep understanding of human cognition is required. The present paper focuses on spatial cognition, i.?e. the ability to perceive and conceive spatial environments and solve spatial tasks intelligently. It discusses artificial intelligence approaches to spatial cognition for supporting human activities.