A framework for meta-level control in multi-agent systems

Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions. This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies.     

Framework of knowledge-based systems : Multiple meta-level architecture for representing problems and problem-solving processes

Currently available expert systems have a performance limit because of the lack of capability to describe problems and problem-solving methods. It is closely related with knowledge representation language, but this is not the only concern with this issue. Real world problems and problem-solving methods are not so simple as to be represented always in the same way by the same language. Their representations must be different depending on various factors involved in the problems themselves and the situations these problems are surrounded with. In this paper, the author discusses first the intrinsic nature of problem representation and problem-solving process representation. The requirements for and the conceptual framework of a knowledge-based system that is suited for dealing with various problems then become apparent quite naturally. The author asserts that a multiple meta-level architecture is necessary as well as a knowledge-representation language that can describe complex data structures as the basic framework of knowledge-based systems.     

A human-in-the-loop manufacturing control architecture for the next generation of production systems

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.     

A concurrent architecture for serializable production systems

This paper presents a new production system architecture that takes advantage of modern associative memory devices to allow parallel production firing, concurrent matching, and overlap among matching, selection, and firing of productions. We prove that the results produced by the architecture are correct according to the serializability criterion. A comprehensive event driven simulator is used to evaluate the scaling properties of the new architecture and to compare it with a parallel architecture that does global synchronization before every production firing. We also present measures for the improvement in speed due to the use of associative memories and an estimate for the amount of associative memory needed. Architectural evaluation is facilitated by a new benchmark program that allows for changes in the number of productions, the size of the database, the variance between the sizes of local data clusters, and the ratio between local and global data. Our results indicate that substantial improvements in speed can be achieved with a very modest increase in hardware cost     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

A multi-agent architecture for control of AGV systems

Agent is an autonomous, computational entity that can be viewed as perceiving its environment and acting upon it. Agents are event-driven objects that can be integrated in automated manufacturing environments to control certain tasks. In this paper a set of agents (a multi-agent system) is introduced to control an automated manufacturing environment. The architecture includes functions at the manufacturing cell level, materials handling and transport level, and factory scheduling level. Communication between these agents is accomplished by using a relational database (blackboard system). The relational database also integrates the requirements of a manufacturing execution system within the multi-agent task structure, which is unique to this architecture. Manufacturing cell and scheduling agents have been previously described in the literature. Here we focus our attention on the functions of the agents of the transport system, which is composed of a set of AGVs.     

A software architecture for distributed computer control systems

Distributed computer control systems have a number of potential advantages over centralized systems, especially where the application is itself physically distributed. A computer station can be placed close to the plant being controlled, and a communications network used to enable the stations to communicate to coordinate their actions. However, the software must be carefully designed to exploit the potential advantages of distribution. This paper describes the software architecture of CONIC, a system to support distributed computer control applications. This architecture emphasizes the distinction between the writing of individual software components and the construction and configuration of a system from a set of components. A modular structure is used to separate programming from configuration. Typed entry and exit ports are used to clearly define module interfaces. Ports, analagous to the plugs and sockets of hardware components, permit modules to be interconnected in different ways. On-line modification and extension of the system is supported by permitting the dynamic creation and interconnection of modules. Message passing primitives are provided to permit modules to coordinate and synchronize control actions.     

A control architecture for continuous production processes based on industry 4.0: water supply systems application

Journal of Intelligent Manufacturing - Industry 4.0 (I4.0) brings together new disruptive technologies, increasing future factories’ productivity. Indeed, the control of production processes...     

A re-configurable multi-agent system architecture for error recovery in production systems

Multi-agent systems for manufacturing systems appear to provide adequate response to abrupt disturbances on the shop floor. To date, most of the work has been focused on planning and scheduling but very little work has been done on issues pertaining to monitoring, diagnostics and error recovery. Our approach addresses the issue of combining the discipline of hierarchical systems with the agility of multi-agent systems. Within the context of a hierarchy, the focus is on the workstation level and, in particular, the construction of a re-configurable system having production agents, error recovery agents, and a mediator agent structure connecting production and recovery agent hierarchies. In addition, the relationship to a multi-level, multi-layer hierarchy control is established. This latter hierarchy, based on Petri Net constructs, serves, in one sense, as a retrieval based resource for process planning and generation of recovery plans for production and recovery agents within the proposed multi-agent system. An objective of this effort is to provide a test-bed for comparison of hierarchical systems, heterarchical, and a hybrid combination which is the focus of the investigation presented here.     

Agent-based architecture for designing hybrid control systems

When designing very complex control strategy using hybrid technology, one usually faces the challenge of balancing effective realization of multi-control modeling with design simplicity. To better manage this difficulty we have used the agent paradigm as a simple and powerful bridge between asynchronous/distributed computation and Matlab environment. The proposed architecture has been used to design a complex hybrid control environment using multi-objective, fuzzy c-means, and genetic algorithms optimization to design hybrid control strategies suitable for the energy flows management on board of hybrid electric vehicles.     

An object-oriented architecture for knowledge-based production scheduling systems

This paper describes an object-oriented architecture to support decision making in production scheduling environments. An object-oriented world view is used to integrate concepts from discrete event simulation, conventional scheduling logic and artificial intelligence to produce capacity-feasible schedules. The architecture was implemented as a collection of loosely coupled reusable software objects by extending the functionality of software objects from BLOCS/M (Berkeley Library of Objects for Control and Simulation of Manufacturing). Our experience with an industrial prototype is presented.     

A computer network based control architecture for autonomous distributed multirobot systems

This paper presents the specification and implementation procedure using a microcomputer network based autonomous distributed control architecture for industrial multirobot systems. The procedure is based on the concept of data flow network controlled by communicating sequential processes to perform coordinated tasks. Robots and other computerized industrial devices such as conveyors and manufacturing machines are defined as object-oriented Petri nets. A modular and hierarchical approach is adopted to define a set of Petri net type diagrams which represent concurrent activities of control processes for such devices. Asynchronous and synchronous interactions are modelled by places and transitions, respectively, in global process interaction nets. The control software is implemented on a computer network using Inmos transputers with true parallel processing and message passing primitives efficiently handled in hardware. Petri net based models are directly and efficiently transformed to corresponding codes in occam, the high level parallel programming language defined for the transputer.     

Event-based architecture for diagnosis in control advisory systems

Artificial intelligence research on diagnostic systems is focused mainly on static problems in which the object of interest is assumed to be in a stable condition that will remain stable until the diagnostic agent has completed its deliberation. Control systems, on the other hand, cannot afford the luxury of ‘suspended time’; faults must be detected, isolated and reconfiguration implemented within one computation cycle to prevent departure from stable control envelopes.A new class of system is emerging from recent programs directed toward vehicle operator aids for fighter aircraft, submarines and helicopters. These systems are neither static off-line aids nor real-time controllers; instead they are control advisory systems which span the time scales of both regimes. These systems interface with controllers to interpret the error codes and to conduct tests and implement reconfigurations. On the other hand, these systems also interact with the vehicle operator to prioritize their activity consistent with the operator's goals and to recommend diagnostic/emergency procedures.Pilot's Associate (PA), a complex system of software and cockpit displays currently being developed to aid Air Force fighter pilots, is used to exemplify a control advisor and to display the requirements for real-time equipment diagnosis in such a system. It is shown that the required functionality challenges the capabilities of current diagnostic algorithms.The PA diagnostic architecture is presented. This architecture is centred around the concept of a diagnostic event which begins when abnormal data are detected, and which is treated as an interruptable computational process which may exist in conjunction with other event processes in multiple fault situations.The architecture solves a number of problems such as interruptability, and integrating the different time-scales of the pilot and the local system controllers, and the architecture provides a means for fusing statistical fault detection methods such as Kalman filters with artificial intelligence techniques such as model-based reasoning and classification. The result is an approach which exploits the strengths of each technique and provides a mechanism for automated reasoning using both quantitative and qualitative information.     

H2CM: A holonic architecture for flexible hybrid control systems

Several studies typically arise from the interaction of discrete planning algorithms or control and continuous processes, normally called hybrid control systems. It consists in three distinct levels, the controller, the plant and the interface. Hybrid control systems are conventionally modeled by switching patterns using the whole system instead of atomic resource. Therefore, the reconfiguration process is complex because it must take into account the system as a whole, making the hybrid control systems inflexible and more susceptible to uncertainties. The need for flexibility thus leads several teams to investigate the application of holonic paradigm to hybrid control systems. The objective of this paper is to demonstrate the possibility to apply almost directly a holonic discrete-event based reference architecture to hybrid control systems. A case study of industrial electricity generation process was taken, specifically a combined cycle plant (CCP) for verifying the proper operation of the proposed architecture.     

A blackboard architecture for control

The control problem—which of its potential actions should an AI system perform at each point in the problem-solving process?—is fundamental to all cognitive processes. This paper proposes eight behavioral goals for intelligent control and a ‘blackboard control architecture’ to achieve them. The architecture distinguishes domain and control problems, knowledge, and solutions. It enables AI systems to operate upon their own knowledge and behavior and to adapt to unanticipated problem-solving situations. The paper shows how opm, a blackboard control system for multiple-task planning, exploits these capabilities. It also shows how the architecture would replicate the control behavior of hearsay-ii and hasp. The paper contrasts the blackboard control architecture with three alternatives and shows how it continues an evolutionary progression of control architectures. The paper concludes with a summary of the blackboard control architecture's strengths and weaknesses.     

Small-world architecture of networked control systems

The paper proposes a method to generate communication structures for networked controllers that have the small-world property. This property, which is known from complex networks, means that in a large ring of agents, a small number of shortcut links substantially improve the performance of the overall system. To investigate the consequences of this property for control systems, the paper considers multi-agent systems with leader–follower structure where additional communication links among the local control stations are selected by the agents in a random way. The performance of the overall system is evaluated in terms of the delay with which the agents react on leader commands. The main result provides a relation between the probability p of the existence of additional edges in the communication graph and the performance of the overall system. It is proved that for any probability p > 0 there exists an upper bound of the performance index and that this bound is independent of the number of agents. Furthermore, a quantitative analysis shows that it is sufficient to connect the agents with a very low probability p ≈ 10% in order to get a satisfactory performance. These results are illustrated by a robot formation example.     

A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback

In this work, we introduce a two-tier control architecture for nonlinear process systems with both continuous and asynchronous sensing and actuation. This class of systems arises naturally in the context of process control systems based on hybrid communication networks (i.e. point-to-point wired links integrated with networked wired or wireless communication) and utilising multiple heterogeneous measurements (e.g. temperature and concentration). Assuming that there exists a lower-tier control system which relies on point-to-point communication and continuous measurements to stabilise the closed-loop system, we propose to use Lyapunov-based model predictive control to design an upper-tier networked control system to profit from both the continuous and the asynchronous measurements as well as from additional networked control actuators. The proposed two-tier control system architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The theoretical results are demonstrated using two different chemical process examples.     

A program specialiser for meta-level compositions of logic programs

Metal-level compositions of object logic programs are naturally implemented by means of meta-programming techniques. Metainterpreters defining program compositions however suffer from a computational overhead that is due partly to the interpretation layer present in all meta-programs, and partly to the specific interpretation layer needed to deal with program compositions. We show that meta-interpreters implementing compositions of object programs can be fruitfully specialised w.r.t. meta-level queries of the form Demo (E, G), where E denotes a program expression and G denotes a (partially instantiated) object level query. More precisely, we describe the design and implementation of declarative program specialiser that suitably transforms such meta-interpreters so as to sensibly reduce — if not to completely remove — the overhead due to the handling of program compositions. In many cases the specialiser succeeds in eliminating also the overhead due to meta-interpretation. Antonio Brogi, Ph.D.: He is currently assistant professor in the Department of Computer Science at the University of Pisa, Italy. He received his Laurea Degree in Computer Science (1987) and his Ph. D. in Computer Science (1993) from the University of Pisa. His research interests include programming language design and semantics, logic programming, deductive databases, and software coordination. Simone Contiero: He is currently a Ph. D. student at the Department of Computer Science, University of Pisa (Italy). He received his Laurea Degree in Computer Science from the University of Pisa in 1994. His research interests are in high-level programming languages, metaprogramming and logic-based coordination of software.     

Distributed bottleneck control for repetitive production systems

A bottleneck control problem for general periodic job shops with blocking where each machine has an input buffer of finite capacity is investigated. The job shop considered consists of a set of workflows competing with each other for access to common machines. A distributed buffer control policy that restricts a job entering an input buffer of a local machine in a specific sequence is proposed. The conditions sufficient for design and allocation of dispatching rules are presented. The system time and the rate of machine utilization are considered as the evaluation criteria. Finally, the procedure aimed at scheduling periodic job shops is provided.