Trends in distributed artificial intelligence

Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.     

A Decision Support System for a small scale industry in a developing country

This paper describes the design and development of a Decision Support System (DSS) for a small scale manufacturing company in a developing country. The DSS provides a solution for an integrated problem of planning, shopfloor scheduling, and control. The DSS system accepts the demand data with priorities, then uses the information from the imbedded data base system regarding product structure, production process, available resources with the status, and inventory. This in turn is used by the imbedded models to solve planning and scheduling problems, and provide solutions containing job order release, shopfloor scheduling and material requirement report. What if analysis feature provided by DSS allows the managers to incorporate their knowledge and experience to improve the quality of the solution by evaluating different scenarios presented by the manager. The laboratory equipment manufactured by the company is made to order. Product information regarding twenty-two high demand items is incorporated into the data base system, and the scheduling problem is solved for these items. The jobshop is a mixed shop problem containing a flow shop, an open shop, and a flow shop. A heuristic method used for solving this problem is presented.     

Assembly simulations in virtual environments with optimized haptic path and sequence

Virtual assembly and disassembly simulations can be accomplished in intuitive and effective ways using haptic information in virtual environments (VEs). Potential problems in a given assembly scheme can be predicted by a user who may be able to suggest an alternative scheme in the VE. This paper describes an intelligent virtual assembly system in which an optimal assembly algorithm is used to allow haptic interactions during virtual assembly operations. This algorithm provides optimal paths for haptic guidance as well as an assembly sequence of the parts to be assembled. The performance of the given assembly schemes was simulated using a virtual assembly system. Experimental results showed that the haptic-path sequence-guidance (HSG) mode gave the best performance improvement in terms of accumulated assembly time (28.33%) and travel distance (15.05%) compared to the unguided mode, while the sequence-guidance (SG) mode alone increased performance by 15.33% for assembly time and 11.36% for travel distance. The experimental results were analyzed by the sub-tasks of gripper selection, inter-part movement, and part assembly. For the HSG mode, the greatest contributor to the time and distance reductions was the optimized haptic path, while for the SG mode, the reduced numbers of gripper exchanges and orientation change made the greatest contributions to reducing the assembly time and the travel distance. As a result, the optimized haptic path, as well as sequence guidance, enhanced the working performance of virtual assembly tasks.     

Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain

This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.     

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.     

Integrated process planning and scheduling by an agent-based ant colony optimization

This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm.     

Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data

Radio frequency identification (RFID) has been widely used in manufacturing field and created a ubiquitous production environment, where advanced production planning and scheduling (APS) might be enabled. Within such environment, APS usually requires standard operation times (SOTs) and dispatching rules which have been obtained from time studies or based on past experiences. Wide variations exist and frequently cause serious discrepancies in executing plans and schedules. This paper proposes a data mining approach to estimate realistic SOTs and unknown dispatching rules from RFID-enabled shopfloor production data. The approach is evaluated by real-world data from a collaborative company which has been used RFID technology for supporting its shopfloor production over seven years. The key impact factors on SOTs are quantitatively examined. A reference table with the mined precise and practical SOTs is established for typical operations and suitable dispatching rules are labled as managerial implicities, aiming at improving the quality and stability of production plans and schedules.     

Communication infrastructure in distributed scheduling

The emergence of distributed artificial intelligent (DAI) introduced a new approach to solve scheduling problems by a set of scheduling systems that interact with each other in the problem-solving process. In this paper, we describe a communication infrastructure to handle connection and communication between distributed Internet scheduling systems for distributed applications. First, we present an agent model of distributed scheduling systems where agents can communicate and coordinate activities with each other via an agent communication language. Then, we define the syntax and semantics for the agent communication languages, and negotiation mechanism. Following that, we discuss the design and development of the prototype for the multi-agent scheduling systems. We conclude with a discussion of communication issues for heterogeneous agent-based scheduling systems to solve distributed scheduling problems.     

基于智慧车间的生产执行系统的研究及应用

为了解决中小型塑料制造企业管理系统在信息采集方式、计划实时性、灵活性等方面的不足,将射频识别Radio Frequency Identification(RFID)技术引入塑料生产车间,构建塑料制造装配车间的物料智能配送体系,形成面向离散制造业的智慧装配车间。对生产执行系统Manufacturing Execution System(MES)的架构、RFID智慧生产车间应用流程、应用可靠性等关键技术进行了研究,并对车间生产组织、制造事件和动态多变市场的响应能力进行了优化。所述内容对提升塑料制造企业核心竞争力具有重要的理论与实际意义。     

Multi-agent based real-time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment

The lack of timely feedback shopfloor information during manufacturing execution stage leads to significant difficulties in achieving real-time production scheduling. To address this problem, an overall architecture of multi-agent based real-time production scheduling is presented to close the loop of production planning and control. Several contributions are significant. Firstly, wireless devices such as radio frequency identification (RFID) are deployed into value-adding points in a ubiquitous shopfloor environment to form Machine Agent for the collection and processing of real-time shopfloor data. Secondly, Capability Evaluation Agent is designed to optimally assign the tasks to the involved machines at the process planning stage based on the real-time utilization ration of each machine. The third contribution is a Real-time Scheduling Agent for manufacturing tasks scheduling/re-scheduling strategy and methods according to the real-time feedback. Fourthly, a Process Monitor Agent model is designed for tracking and tracing the manufacturing execution based on a critical event structure. Finally, a case is used to demonstrate the proposed multi-agent based real-time production scheduling models and methods.     

An object-oriented knowledge based Petri net approach to intelligent integration of design and assembly planning

Artificial intelligence can play an important role in the reduction of manufacturing costs and the enhancement of production efficiency and product quality. In order to assist designers in the early stages of a product development this paper develops an intelligent methodology for integration of design and assembly planning processes, including product design, assembly evaluation and redesign, assembly process planning, design of assembly system and assembly simulation, subjected to both econo-technical and ergonomic evaluations. A new unified class of object-oriented knowledge based Petri nets called OOKPNs, incorporating knowledge based expert systems and fuzzy logic into ordinary place–transition Petri nets, is defined and used for the representation and modeling of the distributed design processes. A prototype intelligent integrated design and assembly planning system (IIDAP) is implemented through distributed blackboard structure with concurrent integration of multiple cooperative knowledge sources and software. It consists essentially of the networked agents and the meta-system, each of which is a knowledge Petri net system with the capabilities of problem solving, learning and conflict resolution, and can be obtained through the inheritance, polymorphism and dynamic binding of instances of OOKPNs. In IIDAP system, both C/C++ language and COOL (CLIPS object-oriented language) are used to incorporate a Petri net tool, a geometric modeling and design tool, a planner and simulator and an evaluation tool. By use of this system, product design and assembly planning can be carried out simultaneously and intelligently in an entirely computer-aided concurrent design and assembly planning system. The design of manufacturable, cost-effective, usable products can therefore be achieved rapidly and flexibly. The developed methodology and system have been successfully applied to assembly design and planning of a micro switch.     

Knowledge intensive Petri net framework for concurrent intelligent design of automatic assembly systems

The integration of design and planning of flexible assembly system has been recognized as a tool for achieving efficient assembly in a production environment that demands assembly with a high degree of flexibility. This paper proposes a concurrent intelligent approach and framework for the design of robotic flexible assembly systems. The principle of the proposed approach is based on the knowledge Petri net formalisms, incorporating Petri nets with more general problem-solving strategies in AI using knowledge-based system techniques. The complex assembly systems are modeled and analyzed by adopting a formal representation of the system dynamic behaviors through knowledge Petri net modeling from the specifications and the analysis of those models. A template is first defined for a knowledge Petri net model, and then the models for assembly system individuals are established in the form of instances of the template. The design of assembly systems is implemented through a knowledge Petri net-based function–behavior–structure model. The research results show that the proposed knowledge Petri net approach is applicable for design, simulation, analysis and evaluation, and even layout optimization of the flexible assembly system in an integrated intelligent environment. The integration of assembly design and planning process can help reduce the development time of assembly systems.     

Limits of economic and strategic rationality for agents and MA systems

AI adoption of the game-theoretic paradigm although motivated and productive, suffers from basic limits for modelling autonomous agents and MA systems. First, we briefly restate game-theory's role for DAI and MAS: the introduction of formal prototypical social situations (“games”); the use of formal and sound notions, a self-interested view of autonomous agents, etc. Then, a number of criticisms, that have an impact on modelling intelligent social/individual action, are examined: the economicist interpretation of rationality; its instrumentalist conception, which leaves implicit the ends of agents' choices; the consequent multiple equilibria allowed by the theory; the context-unboundedness of rationality. Some contributions for a more heterarchic, context-bounded, architecture of rational agent are given, and a goal-based strategy, as distinct from a strictly utilitarian principle of decision-making, is proposed. Troubles of game-theory with multi-agent systems and in particular with modelling “cooperation” are outlined. Finally, some limits inherent in the notion of “incentive engineering” are pointed out.     

A Hybrid Intelligent Multiagent System for E-Business

The paper describes a new multiagent system with enhanced capabilities obtained through a hybrid of intelligent techniques. The processing in the model is handled by two types of agents: distributed agents and a central administrator agent. Localized processing at the individual agents is carried out using mathematical techniques and genetic algorithms. The central administrator agent dynamically obtains information about the problem domain from the Internet and maintains a knowledge pool using a clustering technique called the growing self-organizing map (GSOM). Distributed agents communicate with the central administrator agent if they need further knowledge about the problem domain to provide solutions to user-defined tasks. The approach integrates traditional mathematical, data mining, and evolutionary techniques with a multiagent system. The proposed system is implemented as a travel optimizer application for the e-tourism domain. Finally, the possibilities of integrating the proposed technique with currently available e-tourism applications to provide the customer with enhanced solutions are identified.     

Real-time information driven intelligent navigation method of assembly station in unpaced lines

This paper considers the assembly station as a breakthrough to improve the real-time information driven control and optimization of assembly process in unpaced asynchronous line. By adopting automatic identification technologies, the overall architecture of the real-time intelligent navigation of assembly station (INoAS) is put forward. Under this architecture, three core services, namely the real-time assembly operating guidance service (OGS), collaborative production service (CPS) among assembly stations and real-time queuing service (RQS) of the jobs at each station, are designed to provide optimal and dynamical navigation for assembly activities for each station. Then, the disturbances and exceptions could be timely captured by installing the INoAS at each station, and the operating guidance, collaborative production information sharing and real-time queuing could be easily achieved. The presented architecture and services of INoAS will facilitate the real-time information driven process monitor and control between the line and stations.     

基于改进遗传退火算法的生产智能调度系统

为了满足多品种小批量生产模式的需求,设计了一套基于智能调度算法的生产调度系统。根据数据的特点和系统的需求,设计了合理的数据结构和不同的调度规则;根据实际应用的要求,改进了遗传退火算法,使算法可以进行柔性调度和优先级调度,并且算法可以满足零件装配关系约束。在.NET3.5框架下,开发了生产智能调度系统,实现了对生产任务的智能调度。     

Integration of control and error management for a flexible assembly cell,using a cost function

A flexible assembly cell is considered to be an essential building brick for flexible automation of small to medium sized order-based industries. In order to be able to respond adequately to market changes a production facility that can be reprogrammed or even reconfigured in a very short time is vital. Such a system will have to exhibit some form of intelligent behavior. By intelligent behavior we mean that this system should be able to learn from experience. The experience gained in making subsequent batches of products will be used to increase the efficiency of future batches. Unreliable strategies will be replaced by more reliable ones, actual time data will make it possible to choose a better plan. In this paper a reference model for planning, control and error management for a flexible assembly cell is presented and scheduling and a cost function for the selection of strategies for primitive operations are discussed.     

Intelligent design and planning of manual assembly workstations: A neuro-fuzzy approach

The design of manual assembly workstations, as with most forms of designs, is highly iterative and interactive. The designer has to consider countless constraints and solutions for contradictory goals. In order to assist the designer in design process, it is required to develop a new intelligent methodology and system. This paper develops a neuro-fuzzy hybrid approach to intelligent design and planning of manual assembly workstations. Problems, related to workstation layout design, planning, and evaluation, are discussed in detail. A fuzzy neural network is used to predict the ranges of anatomical joint motions and to design or adjust workstations and tasks. The neuro-fuzzy computing scheme is integrated with operator's posture analysis and evaluation. For training and test purposes, experiment is carried out to simulate assembly tasks on a multi-adjustable assembly workstation equipped with a flexible PEAK motion measurement and analysis system. The trained neural network is capable of memorizing and predicting the joint angles associated with a range of workstation configurations. Thus, it can also be used for the design/layout and on-line adjustment of manual assembly workstations. Thus, the developed system provides a unified, computational intelligent framework for the design, planning and simulation of manual assembly workstations.     

Knowledge-based approach and system for assembly-oriented design,Part II: the system implementation

Assembly is one of the most important stages for product development. Assembly-oriented design (AOD) is a new approach to designing assemblies, which uses a number of design and analysis tools to help the designer plan out and analyze candidate assembly schemes prior to having detailed knowledge of the geometry of the parts. Using this approach, many assembly schemes can be inexpensively evaluated for their ability to deliver the important characteristics of the final product. This research proposes a knowledge-based approach and develops an expert design system to support top-down design for assembled products. The presentation of research report is divided into two parts: the knowledge-based approach (Part I) and the knowledge-based expert design system (Part II). This paper is the second part of the report (Part II). It will focus on the development of knowledge-based expert design system for assembly oriented design. The knowledge-based assembly oriented design system, i.e., the assembly oriented design expert system (AODES) is constructed to integrate assembly modeling and design, assembly planning, assemblability analysis and evaluation within a concurrent engineering environment. This intelligent system is implemented by integrating object-oriented representation, constraint-based modeling, rule-based reasoning, truth maintenance, and interfacing to database management system and CAD module, in which fuzzy logic based knowledge representation and inference technique are also applied to deal with uncertain data and knowledge in the design process. The developed system differs from the existing systems adopting part-first bottom-up modeling technique, in which a comprehensive intelligent framework is used for assembly modeling and design in a top-down manner from the conceptual level to the detailed level. It is able to help obtain better design ideas, provide users with suggestions so as to create and improve a design, and therefore give users the possibility to assess and reduce the total production cost at an early stage during the design process. Through the use of the system, the concurrent engineering knowledge can be effectively incorporated into the assembly design process in an integrated manner. A case assembly design shows that the intelligent modeling and design system is feasible.