Object-based Intelligence in Office and Production Processes: A View on Integration

Transcription

1 Object-based Intelligence in Office and Production Processes: A View on Integration Gregory Mentzas Department of Electrical and Computer Engineering National Technical University of Athens Published in: "Information and Decision Technologies" (Volume 19, pp ) Abstract. This paper reviews the recent advances in object-orientation and distributed artificial intelligence and puts forth the view that these advances open up the prospect of systems which will enable the true integration of computational facilities, in both the production and office environments. In an attempt to identify domain requirements and match them with research achievements, the paper examines the current literature and distinguishes fourteen features that are common in both environments. Then, it argues that effective enterprise-wide support could be greatly facilitated by the existence of intelligent software entities with autonomous processing capabilities (labelled Active Intelligent Objects, AIOs), that possess coordination and negotiation facilities and are organised in distributed, hierarchical societies. Keywords: production systems; office systems; object-oriented systems; expert systems; distributed artificial intelligence. Address for correspondence: Dr Gregory Mentzas Assistant Professor Department of Electrical and Computer Engineering National Technical University of Athens 42, 28th October str., Athens, Greece.

2 1. INTRODUCTION The automation of the manufacturing enterprise requires the understanding of four basic concepts: the transfer of information, the transform of information, the transfer of material and the transform of material [11]. Hence, the ultimate goal of support to both the bluecollar and white-collar functions of an enterprise might be: first, the automated creation and transformation of both logical data and the physical material; and second, the provision of a synergistic integration of the automated functions into a cohesive computer-supported environment. The automation of information-related functions in an enterprise is the objective of office information systems, while the automation of production-related aspects of an enterprise is the goal of production management systems. The advent of production management systems (PMS) has seen a proliferation of research that aims at supporting work using advanced computer-based systems. Flexible manufacturing systems are currently used for the integration of numerically-controlled machines with the real-time decision-making capabilities of supervisory computers and the automated transportation facilities of material handling systems, in order to reduce flow- and set-up times and achieve efficiency in manufacturing. The recent trend of integration at the enterprise level has been to provide support to a distributed range of activities, which start from the design department (with CAD software), move to the production planning department (producing the work schedules), then to the production control office (issuing shop orders) and, finally, to the NC machines. The role of artificial intelligence has been vital in this context; see [19] and 25]. On the other hand, the advent in office information systems (OIS) has been relatively slow. In an attempt to compare the automation level at the production and office environments it has been argued that a number of tools (such as word-processing, spreadsheets, graphics, database systems and electronic mail) provide the office worker with the equivalent of machine tools and power drills. In addition, various systems have been proposed for supporting the mechanisation of repetitive, routine aspects of office work, in which inputs and outputs are well defined and the procedures followed are relatively clear; such systems could be considered analogous to robots in manufacturing systems. A significant part of office work, however, refers to high-level tasks which are neither routine nor clear, since they involve cooperation among many agents, negotiation among parties, confrontation and argumentation, and the abilities to learn and reach goals. Again, the role of artificial intelligence can be crucial [28]. Four reasons have been given as an explanation for the differences that the advent in office information systems demonstrates, in comparison to the respective explosion in the automation of manufacturing systems; see also [45]. The mechanisation of office activities came, historically, later than that of the factory; office work is far less structured than that of factory work and rather difficult to automate; intellectual office work is not visible (although it may have visible effects) and that poses difficulties in its comprehension, and hence its automation; and finally, office work is considered an overhead in many organisations, and is not receiving as close attention as factory work. Current information technology support addresses OIS and PMS as separate fields, although some computational requirements are the same, and despite the fact that the basic underlying technology is based on common frameworks [31]. On the other hand, it is - 1 -

3 accepted that the ultimate goal of enterprise-wide decision-making systems is to streamline the process for greater productivity, reduced cost, improved quality, and faster responses to customer needs. Within this framework, information needs to be rooted constantly among the marketing, accounting, finance and sales departments, as well as the engineering, inventory control, quality control, production scheduling and manufacturing departments. The (paper) work done in some of these departments is currently handled by OIS, while (production) work done in other departments is supported by PMS. Hence, we should envision the integration of the automation facilities found in office systems, with those of production systems, with the aim to provide consistent and coherent support for overall planning. This paper puts forth the view that recent advances in object-orientation and distributed artificial intelligence open up the prospect of systems which will enable the true integration of automation, both from the production and the office systems perspective. In an attempt to identify the potential synergistic elements in the computational models used in office information systems and production management systems, the paper reviews research trends in the OIS and PMS areas. The conclusion from this review is that enterprise-wide automation could be greatly facilitated by the existence of intelligent software entities with autonomous processing capabilities, that possess coordination and negotiation facilities and are organised in distributed, hierarchical societies. The paper presents a conceptual definition of such entities (labelled Active Intelligent Objects, AIOs), outlines their structural characteristics, and describes a framework for research towards the development and population of AIO societies. In the next section we review the current research trends in the office automation and manufacturing systems areas with the goals of detecting the advents made and the gaps remaining in each domain and attempting to lay down the issues that arise. In the third section we argue that the information systems used for supporting office and production management should exhibit a number of common features. These system requirements can be summarised as follows: parallelism; specialisation; communication; transformation; distribution; learning ability; adaptation; semantic services; reconfigurability; extensibility. In the fourth section we give a behavioural definition of active intelligent objects and sketch the basic characteristics of an integrated architecture. Finally, the fifth section presents the conclusions and directions for future research. 2. REVIEW OF OIS AND PMS 2.1 Issues in Office Information Systems Office Information Systems have been modelled as encompassing three domains: passive office objects; office procedures; and office tasks; see [28]. Office objects are the primitive office elements; examples of office objects are documents, files, printers, etc.; hence, office objects provide metaphors that represent their actual counterparts in the physical office. Office procedures can be considered a set of mappings among office objects; office procedures are routine sequences of operations that are used to manipulate office objects. They model the event-driven behaviour of office work and are triggered upon completion of some awaited event, e.g. the arrival of a message, the completion of a form, or the modification of a document. Finally, office tasks are goal-directed and cannot necessarily be - 2 -

4 encoded to a precise procedure to be followed. Their intention is to model cooperation among many office agents, negotiation among parties, confrontation and argumentation, and the abilities to learn and reach goals. Two important requirements of OIS with reference to office objects concern the need to support concurrent sharing (e.g. the fact that several office workers from different departments may wish to examine the same information at the same time) and referential sharing; e.g. a purchase order might be replicated within the purchasing department as well as the department from which it originated; nevertheless, the status of the order should be maintained consistent in both departments. Office procedures model dynamically changing work-flow within an office; they focus on the handling of information flow within corporate processing involved in performing a particular process. Office procedures need to be executed in a parallel manner, so that concurrency of office work is achieved, and they need to be stored and retrieved in a "dynamic" manner, so that queries of the present state of a procedure would give a persistent picture. In addition, message-passing protocols between office procedures guarantee the modelling of work interdependencies. Finally, office tasks, being knowledge-intensive, since they represent the corporate level rules and procedures, lend themselves to artificial intelligence techniques. Some example OIS systems are described in the following. In an attempt to satisfy the need for automated office assistants, that manipulate fragments of knowledge with their own rules and negotiate, cooperate, and learn, Tsichritzis et al have built a prototype, the KNOs (KNowledge acquisition, dissemination and manipulation Objects) office system [45]. KNOs encapsulate knowledge and goals as knowledge objects. A set of cooperating KNOs exists within a context (typically a context is physically associated with a workstation). KNOs communicate within a context by reading messages from or writing messages onto a blackboard. All KNOs are autonomous, except for the case that one KNO may grow another KNO as a limb; the latter is by definition dependent on the KNO that grew it. KNOs communicate with one another by posting messages on a blackboard that is managed by an object manager. The implementations of KNOs use object-oriented versions of LISP [45]. In these implementations KNOs belong to one or more KNO classes; the latter specify the (initial) structure and behaviour of KNOs. The KNO structure refers to the instance variables contained within one KNO, while a KNO's behaviour is determined by the operations is can perform. The KNOs system encodes office tasks as a set of production rules each consisting of a name, a trigger condition, and a series of actions. An application that addresses office planning issues is the POLYMER office system [9]. The POLYMER architecture includes the Object Management System (the knowledge base) and the Task Manager (which includes a planner, an execution monitor, an exception analyst, a plan critic and a negotiator). The basic cycle of the POLYMER for a single user is as follows: the user posts a goal; the planner generates a plan to achieve the goal, with the result of planning being a procedural net that specifies the sequence of office activities required; the execution monitor selects an activity and either sends a message to the appropriate object in the knowledge base, or notifies the user; once the activity is completed the execution manager compares the actual action to the expected one; if there are no differences the planner takes over and planning continues, otherwise the exception handler is - 3 -

5 invoked to find an explanation for the exception and the negotiator determines whether the explanation is valid or some information is missing. Each POLYMER activity description contains the following attributes: goal; preconditions; effects (side-effects or secondary effects); decomposition (of an activity into steps); control (temporal and causal constraints between the steps of the activity decomposition); agents (that are responsible for the activity); and constraints (on potential plans). In an attempt to achieve knowledge reusability by organising knowledge in abstract entities Tueni, Li and Fares employ the idea of a memory organisation package for office activities in their AMS system [47]. Actually, in AMS each concrete office process performed by an office worker is modelled by the activity concept. The preconditions of an activity and their effects are among the most important slots. Three types of information are captured by the activity concept in AMS: start-state, describing the information to be checked before performing the activity; caused state, describing the effect (or the reached goal) caused by the activity execution; and body that explains how the activity will be performed. The MOPA concept organises memory in different levels of abstraction, allowing the sharing of high-level knowledge to construct concrete structures. Abstract activities, i.e. activities which are free from context knowledge, are defined as empty slots that should be filled by the current MOPA. The MOPA concept has two links: the first one points to abstract office procedures, while the other points to a list of activities. This list represents the knowledge about how to accomplish the sequence of tasks at the current level of abstraction. In this way particular office activities and their activation conditions are represented explicitly. The WooRKS prototype [1] is an example of a tool that aims to assist a group of office agents in their activities, by scheduling and monitoring the execution of all the actions that each member of the group has to accomplish for meeting a collective goal. WooRKS acts as both an integrator, able to invoke classical office tools when needed for the execution of an office action, and as a group work scheduler, able to assign actions to agents and monitor their execution. The components of WooRKS include the following: an organisation model, that represents actors of a group and their roles, so that actions can be assigned to correct actors; a reference model that represents objects handled externally to WooRKS; an operator model, that implements an abstraction of office application invokations; an information model, that represents the semantics of information manipulated by office procedures; a time model, that represents time-related concepts; a procedure model, that captures the definition of office processes; and an event model, that recognises external events, and associates them to new, or existing office procedures. Finally, communication and coordination issues have been also analysed in the context of group support in the office environment in [46]. In an attempt to examine coordination in the organizational environment Mentzas argues in [29] that a number of (conflicting) options exist and classifies them in the following axes: specification (i.e. activity description languages and concepts) and implementation of coordination; use of synchronous and asynchronous working phases; information exchange and information sharing; support of sequential and concurrent processing; support of negotiation and conflict resolution; support of analytical modelling; and description of the organizational environment

6 2.2 Issues in Production Management Systems There has recently been a heightened awareness of the role of advanced manufacturing technologies in the manufacturing information system; see [38]. The actual implementation of computer integrated manufacturing systems in most companies, however, is based on myriad (and often incompatible) hardware and innumerable software applications. The latter can be grouped into three distinct categories: 1. The first category groups together applications in design, drafting, process planning, engineering, etc. It can be labelled "Computer-aided Design and Engineering". 2. The second category, labelled "Manufacturing Planning and Control", consists mostly of applications relating to production scheduling, aggregate capacity planning, shop floor control, etc. 3. The third category can be labelled "Manufacturing Automation"; this category incorporates numerically controlled (NC) machines, computer-aided inspection, automated materials-handling, and warehouses and flexible machines systems. It has been argued that decision support in manufacturing assumes that an information transition, associated with changing an entity's measured attributes, is accompanied by a material transition, changing the physical attributes of the machined workpiece. That is, the logical transformation of operand data by one or more processors into a finished information product is accompanied by a concurrent physical transformation of the workpiece into a financial product [11]. The intelligent functions that automated manufacturing systems should perform have been classified as: reaction, learning and problem solving. Reaction is the most primitive form of intelligent function in which the manufacturing system does some form of pattern recognition and provides some response to it. Learning requires that the manufacturing system recognises significant experiences, data or generated plans and incorporates this new information into its control structure; actually, learning incorporates a capability in the manufacturing system to modify its knowledge base. Finally, problem solving activities in manufacturing include modelling the problem domain as a set of states and then describing the process of change as a transition from one state to another, generating plans for such transitions, monitoring the execution of the plans, generating optimal resource allocation plans, etc. Generation of such plans is a nontrivial task, because most problems of practical interest in manufacturing would be NP-complete. Hence problem solving techniques should attempt to develop heuristics which generate 'reasonably good' but not necessarily optimal solutions. In the following paragraphs we briefly review some examples of prototypes that attempt to implement the aforementioned issues. In the area of distributed factory control YAMS (Yet Another Manufacturing System) is a prototype system that apportions tasks by negotiation; see [35] and [36]. YAMS views the problem of factory control as a search through a space whose dimensions include the equipment available at a site, the products to be manufactured and the available resources such as time, inventory and storage space. YAMS models a manufacturing enterprise as a hierarchy of workcells, or functional groups of machinery. This model corresponds closely to the traditional manufacturing view of a corporation as a hierarchical system of plants, - 5 -

7 FMS and machines. YAMS employs the contract net protocol of negotiation in order to accomodate the stochasticity inherent in manufacturing. Each node in the contract net corresponds to a workcell of the manufacturing company. This hierarchy models composition, not control. Each node is a component of its parent, and is in turn composed of its children. YAMS views each node as a negotiating entity that can communicate not only with its parent and children, but also with its siblings. Each node has a library of proccess plans describing the processes it knows how to perform. For example, assume that the Engine_Plant is a contractor to execute the "make_engine" process. after consulting its own process library, it finds that the first step in making an engine is the process "make_block", and that it does not know how to perform this operation. It broadcasts a task announcement for the block process and receives bids for other nodes. After an evaluation of the bids it awards the most successful bidder. In order to support the characterisation and management of a heterogeneous system (such as the factory control system) YAMS adopts the CSP formalism [23] for concurrent processing as an analytical communications model. In the context of flexible manufacturing systems Shaw and Winston view each FMS as a multi-agent system in which each agent is a flexible manufacturing cell and develop a system that includes a bidding process based on the contract-net protocol for introducing an element of competition and for recording an agent's performance [41]. In addition, by viewing a DAI system as an adaptive system capable of learning to improve its performance, they used a genetic algorithm as a competitive learning process. They applied these methods to the scheduling problem of flexible manufacturing systems (FMS). Within a process-based perspective of the manufacturing system Parthasarathy and Kim in [34] extend the Actor model of concurrent computation and develop an actor + model of the manufacturing system. The superscript plus (+) in actor + refers to the model's ability to simulate physical flows and transformations in addition to information flows and computations. In addition, in order to incorporate the ability to reason about time in actor +, they extend the work on point and interval representations of time and temporal reasoning. In applying the concepts of actors in a manufacturing systems, Parthasarathy and Kim faced the following problem: the creation of actors, which is a concept crucial to the Actor model, initially seems irrelevant to manufacturing systems, since machines and robots cannot create copies of themselves. In order to overcome this difficulty they consider the production environment as a highly dynamic one, in which machines are versatile and capable of performing a variety of operations with different set-ups. Then, once they assign a unique address (or identifier) to each machine, robot, etc of the manufacturing system, they can define an actor in the actor + model as any distinct operation or transformation that can be performed at any of the addresses within the system. Hence the concept of actor creation can be retained and operations can "create" new operations. 3. ELEMENTS OF MODELS 3.1 Common features Since an importance step in the formalisation of concepts is a model of the system itself, the models used for the description of computational support in both the office and production environments must represent the inherent characteristics of the latter. The paper argues that - 6 -

8 the computational models used for supporting office and production management work exhibit a number of common features. These features are classified in two groups that correspond to the two different levels of the system designed: the level of the single, independent element, and the system level. Following other authors in the distributed artificial intelligence literature (e.g. [18]) we distinguish between the system architecture (i.e. the global, top-level design of the entire system) and the internal structure (i.e. the features of single computational elements). Fourteen features of the abstract models are examined; seven for each level. The features referring to the internal structure are: specialisation; representation; effectuation; learning; adaptability; planning; and intentionality. specialisation. It refers to ensuring that each computational element can efficiently solve a part of a given problem, i.e. that it has a specific area-of-expertise; for example robots that handle specific production processes, or automated office assistants that perform pre-specified calculations. Another issue here relates to knowledge organisation; distinctions have been made between static and dynamic knowledge. It seems, however, that the open systems perspective [22], in which there may not be any global control, goals, shared knowledge or success criteria, is the most appropriate for both OIS and PMS. However, some of the systems reviewed, like KNOs, adopt a more centralised approach, using blackboards as a common repository for knowledge and information exchange. representation. This feature attempts to capture the requirement that each element represents semantically loaded information. Data abstraction, encapsulation and inheritance are crucial in this context. In some cases the computational elements represent "experts" (e.g. KNOs in the office environment) and lead to approaches similar to cooperating expert systems. In other cases (e.g. actor + in the production environment), mechanisms from object-oriented programming are borrowed; such systems represent software elements as agents or objects of the real world. effectuation (transformation). For example, in an office system a certain document gets filled in, checked, approved and signed; hence it follows a pre-specified procedure, during which it changes "states". The KNO system, for example, models effectuation through the act action, that invokes a designed operation and traces its effect. An analogue situation in a production system is the use of various machines for the transformation of raw materials to final products. The actor + model views change as the heart of manufacturing activity; the manufacturing system conducts three basic activities: conversion, transportation and storage. According to Parthasarathy and Kim their transformation model classifies conversion activity into three basic types: geometric transformations (associated with size and shape dimensions); quality transformations (associated with changes in the structure and properties of raw materials); and union transformations (invoving the combination of two or more physical resources, e.g. assembly transformations) [34]. A topic arising here relates to the reactive versus deliberative nature of effectuation; see [18] for a discussion. The term deliberative implies that an agent posseses reasonably explicit representations of its own beliefs, plans and/or goals that is uses in deciding which action it should select - 7 -

9 at a given time. On the contrary, non-deliberative behaviour implies that the agent's beliefs, pland and/or goals are embedded (or precompiled) into the agent's structure. learning. The office and factory are environments in which information dynamically changes, hence they generate problems which could only be solved by advanced learning abilities. Learning is defined here as the ability to perform new tasks that could not be performed before. For example, agent FMS in [41] adopt a learn-bybeing-told strategy, in which succesful bidder FMS spawn additional agents; the learning technique use genetic algorithms. On the other hand, the learn action in the KNOs system facilitates learning; using this action, a certain KNO takes an imported operation and adds it to its operation list; removing of operations can be done with the unlearn action. adaptation. We differentiate between adaptation and learning in that by learning a computational element incorporates new operations, while by adapting it changes its current behaviour, in order to account for newly acquired information and perform old tasks better, i.e. faster, more accurately, etc. planning. Planning capabilities are needed, when prior to taking action, a data structure representing the intention to take the action is developed; such a data structure corresponds to a plan or a schedule. Planning and scheduling activities are needed for organising efficiently both office and production work; see e.g. the generation of procedural office nets by the planner facility in POLYMER [9]. intentionality. This feature refers to the ability (and need) of a computational element to model other agents' behaviour; see e.g. [18] in which intentionality is defined, after Searle, as that feature by which mental states are directed at or about objects and states of affairs in the world; in this sense beliefs, desires and intentions are intentional states, while e.g. anxiety and depression are not. The intentionality issue is related to learning capabilities; for example, in the case of learning by updating numerical parameters, or acquiring new situation-action rules, as in the case of KNOs, no intentionality is needed. The opposite happens when learned information is modifying a database with lists of acquaintances as in [33]. The features referring to the system architecture are: parallelism; distribution; modularity; heterogeneity; communication; organisation; and human interaction. parallelism. It exploits the philosophy of breaking a problem to be solved into manageable tasks and allocating these tasks across several computational elements; for example, a set of robot welding machines in an automobile company or automated office assistants for office planning. In the KNO system, for example, "... there is at least as much concurrency within a context as there are KNOs (the internal operation of a KNO might itself be concurrent)..." [45]. distribution. Corporations of a significant size face the problem of ensuring consistency and correctness of information residing at various physical sites or processors, while production systems have to guarantee the proper scheduling of work in geographically distributed systems. For example, the KNOs system facilitates distribution by defining a complex KNO, as a head KNO and its limbs; such complex KNOs can be used - 8 -

10 for ccordinating activities that occur in different, geographically distributed, workstations. heterogeneity. Although most OIS and PMS consist of homogeneous elements, e.g. KNOs, office activity networks in AMS, manufacturing cells in FMS, etc., there is a need for the consistent integration of heterogeneous elements, i.e. elements with different specialisations and structures. Technically speaking, homogeneous elements are usually modelled as knowledge sources, which may have different knowledge bases, but share the same design principles. Research attempts towards integrating heterogeneous elements have seen e.g. the MACE system [17], in which the community of system agents includes predefined elements, with varying complexity, specialising in different fields. modularity. The permission of structural changes can be facilitated by designing a system using concrete basic blocks, whose interconnection supports a modular approach. Such a feature should permit both the reconfigurability and expansion of the system. The issue of easing expansion is nicely modelled by the reference model of WooRKS [1], in which entry points to the "external world" are provided. communication. It is the glue that makes it possible for working together; e.g. a robot welder would be of little use if it had no way to signal that it has completed its task and that it was ready for the next one, while an automated office assistant should coordinate with other, similar, assistants to achieve a global goal. Note that most systems use common access methods that can be implemented independently from internal representations, following an object-oriented approach. One of the most commonly used and formally specified communication and negotiation framework is the contract net protocol [10] [43], which has been applied in YAMS [35] and in the system of Shaw and Whinston [42]. organisation. The representation of organisational issues is crucial in the system architecture of computational models. The modelling of authority structure, element groupings and coalitions, is essential both in the OIS and PMS fields. Examples of such organisations are hierarchical, nested groupings, etc.; see e.g. the hierarchical groupings of machinery in YAMS [36]. human interaction. Woods and Roth in [48] provide three design metaphors for manmachine systems: man as cooperating participant of a problem solving/decision making system man as a supervisor of a technical system that is partly automatically controlled man as a user of tools; this includes the use of communication tools which could be any type of technology-mediated human-human interaction. These methaphors can be regarded as orthgonal design dimensions. Research efforts in Human-Computer Cooperative Work (e.g. [12]) would involve the cooperation dimension (with the development of intelligent interfaces), the supervision dimension (i.e. in association with control applications involving human and system agents) and the tool dimension (for facilitating human-human cooperative work with intelligent tools). In our case the cooperation and supervision dimensions are the most - 9 -

11 significant. Can humans be considered themselves computational elements of the overall system architecture? In the majority of OIS and PMS, although humans should be coupled within the overall system, the approach followed is to provide user interfaces for system developers and users, hence considering human agents as "external" entities and modelling only the cooperation dimension. 3.2 Technological basis With the aim to support the above mentioned features the paper argues that the intersection of mainly two research fields could provide the basic technological background: concurrent object-oriented programming, and distributed artificial intelligence. Concurrency in object-oriented languages further enhances the original intent of the Simula language to describe simulations of real systems, since the real world is concurrent and distributed. One of the earliest formalisms for concurrent object-based programming is based on the actor formalism proposed by Hewitt [21]. Hewitt's model uses messagepassing between actors to represent control structures such as request/reply and recursion; see also [2], [3] and [4]. The message-passing metaphor treats objects as autonomous entities that synchronise and exchange information with one another only by explicitly sending messages. This view has been considered equivalent to stating that each object "encapsulates" some local state that may be accessed only by methods that are somehow associated with the object; objects may access the local state of another object only by requesting that the recipient of a message execute some method [44]. This model has an advantage for concurrent programming over essentially shared-memory approaches that separate the universe into passive data structures and active processes. The shared-memory programming model introduces synchronisation constructs such as semaphores, that must be used with care in conjunction with data structure accessing. In contrast, message-passing combines information transfer (access to data) and synchronisation into a single construct. Distributed artificial intelligence, on the other hand, proposes a very different approach to the design and construction of intelligent systems, to that advocated by the more traditional centralised approaches that are currently prevalent. It proposes the provision of intelligence via a federation of co-operating intelligent 'agents'. The characteristic of each agent is that it possesses expertise in a particular area, or that it provides the capability to effect a particular function. Bond and Gasser [6] divide work into DAI in two primary areas: Distributed Problem Solving (DPS) and Multi-Agent Systems (MAS); Parallel AI will not be considered here. DPS considers how the work of solving a particular problem can be divided between a number of processing "nodes", while MAS research is concerned with coordinating intelligent behaviour between a collection of (possibly pre-existing) autonomous intelligent "agents", which can coordinate their knowledge, goals, skills, and plans jointly to take action or to solve problems. The agents in a MAS may be working towards a single global goal, or towards separate individual goals that interact. MAS agents must share knowledge about problems and solutions. In addition, they must reason about the processes of coordination among the agents. No firm definition of an "agent" is given in [6]. Instead, Bond and Gasser state that they rely on a simple and intuitive notion of an agent as a computational process with a single locus of control and/or "intention". In order to define a consistent framework that would address the issues inherent in both office information and production management, while at the same time alleviating practical

12 implementation and deployment difficulties, we shall try to combine object-oriented concepts with the relatively sophisticated approach followed by MAS. More specifically, in the following section, we propose a research framework that tries to exploit the following benefits of an object-oriented representation; the decentralised management of data resources that allows simplified implementation (because operations can be added, or modified, without affecting other parts of the system); the use of common access methods that can be implemented independently from internal representations; the use of the message-passing paradigm for information exchange; other benefits like information hiding and functional decomposition. On the other hand, the research framework adopts main points of MAS research, in the sense that it exploits the knowledge representation, reasoning and communication techniques and problem solving facilities, that are inherent in a MAS framework. 4. AN ARCHITECTURE FOR OBJECT-BASED INTELLIGENCE Effective enterprise-wide decision-making could be greatly facilitated by the existence of software entities with autonomous processing capabilities, which own a private data- and knowledge-base, and which act on their environment on the basis of information they receive, perceive, process, retain and recall. We label these entities Active Intelligent Objects (AIOs). In this section we give a behavioural definition of active intelligent objects, and attempt to incorporate the concepts used by different authors; note, however, that all of them have tried to capture a notion of object intelligence, and of the object as an encapsulation of an asynchronous locus of activity. Finally, we present a conceptual message-based architecture, that could provide the common background for the consistent integration of the computational models used in OIS and PMS. An AIO is defined as a goal-directed computational process which is rational, autonomous, resource-bounded and whose effectuation structure is hybrid. Rationality refers to the production of actions that further the goals of the object, based upon its conception of the outer world. Autonomous behavior refers to an object that has goals and is able to select among a variety of goals thai it is attempting to achieve. Resource boundedness refers to the fact the objects are constrained by limited resources and capabilities. A hybrid effectuation architecture can be realised by combining deliberative and non-deliberative techniques; see the discussion in the previous section and [14] [18]. An agent is deliberative if it selects actions by explicitly deliberating upon various options (e.g. by using an internal symbolic world model, by searching a plan space, or by assessing the expected utility of various execution methods); it is non-deliberative when its choice of actions is pre-programmed, given the occurence of certain environmental conditions. Each individual AIO is characterized by specific domains of expertise, has the capacity to solve complex problems and can work independently for problems tailored to its contextual subject matter. Information sharing and information exchange is required to allow systems of AIOs to create consistent views of problems and arrive at right solutions. AIO systems (AIOS) are groupings of AIOs which coordinate their knowledge, goals, skills and plans jointly to take action or to solve problems; i.e. in an organizational setting they behave like MAS

13 Although the complexity of each AIO can be reduced, the overall complexity of the system must be considered. A planning capability should be introduced, so that a global problem addressed to the AIO system could be decomposed into a variety of sub-problems and subsequently each sub-problem should be distributed to the relevant AIO. In addition to such 'query (or task) decomposition' facilities, there is also the need for 'answer synthesis' strategies that would reconcile partial solutions and rationalise the information flows and the answers to a particular problem. Such planning facilities should be opportunistic and take cognisance of things such as those activities which are taking place, and which AIO is performing these activities. There has been some discussion in the distributed artificial intelligence literature over the benefits and drawbacks of global and local planning facilities. The former seems to be more appropriate for less dynamic environments than the manufacturing enterprise, while the latter, although they allow optimal usage of agents, they do not guarantee overall optimal usage [10]. Hence a two-level planning capability seems to be the most appropriate [32]. In order to alleviate the problem of cooperation between agents hierarchical structures may be employed; inter-aio control flows downwards this structure and information flows upwards. Local partial solutions are interface and combined with those of other AIOs solving dependent tasks; such a system resembles the ICIS agents proposed by Papazoglou et al [33]. Such a structure, however, raises the need for consistent cooperation in a decentralised environment. Decentralisation implies that both control, knowledge and data are logically and spatially distributed. The system lacks global control, as well as global data storage; hence, no AIO has either a global view of the problem examined, or a global view of the activities carried out within the overall system. In addition, AIOs are loosely coupled, in the sense that each AIO is mainly occupied in processing individual computational activities and not in communicating with other AIOs, except when this is necessary. The AIO architecture described in the following paragraphs draws from findings in the use of object-oriented and distributed artificial intelligence research in office information systems [28], decision-support and management information systems [30], and manufacturing systems [25] and from reviews of applications in cooperation and coordination [13] [15] [29]. The specific approach is influenced from relevant research that addresses multi-agent systems in cooperative database systems [33] and manufacturing systems [32]. 4.1 Structure of an Active Intelligent Object The structure of an active intelligent object is graphically depicted in Figure 1. Each AIO consists of a local knowledge base, a deductive capability, a planning facility and a communication mechanism, that would enable it to interact with other AIOs of its community. Consequently each knowledge base would be relatively small, and the inference engine which operates upon this corpus of knowledge would not require high degree of sophistication. Independent AIOs are modelled as active objects capable of reasoning to external stimuli. The latter may be requests for information, processing or reasoning. The knowledge base of each AIO models two types of knowledge: the local area of expertise, in which the specific AIO and its close acquaintances are 'experts'; and the types of expertise in which remote AIOs are 'experts'. The knowledge base includes detailed information about the former, but only partial and abstract knowledge about the latter

14 Communication with other AIOs is enabled with the exchange of messages. These may be of two general types: specific requests to proceed with the solving of a problem, in which the current AIO is an 'expert', or requests to solve problems in which the current AIO is not an 'expert'. In the latter case, a negotiation mechanism is initiated; this is based on the contract net protocol; see the following subsection. 4.2 Cooperation and negotiation facilities AIOs are organised in clusters, in terms of their area of expertise. One useful organisation is to group AIOs in corporate divisions, similar to the divisions of actual enterprises. Hence, one would be expected to form communities of marketing AIOs, strategic planning AIOs, scheduling AIOs, etc. It is interesting to note that such an architecture, would (relatively) easily facilitate the incorporation of already available knowledge and inferencing mechanisms for specific enterprise domains. Hence, the wealth of existing expert system applications for relevant parts of an enterprise could be re-used, after some modifications, that would permit their incorporation in the AIO system. Although, functional decomposition can be the organizational principle for structuring groups of AIOs, there is no need to assume the existence of a one to one mapping between functions and AIOs. It would seem more natural to expect that such a mapping is one to N as in [32]. A hierarchical structure is adopted for the organisation of AIO groups into societies, similar to the one proposed in [24]. Such a hierarchical tree-like structure can be achieved by successive functional decompositions; in this manner, groups of AIOs are permitted to communicate only with their immediate ancestor and descendants and AIOs at the same level of the particular subtree. Inter-AIO communication and task decomposition takes place by means of the well-known in the distributed artificial intelligence research literature contract net protocol (CNP) [10] ]43]. CNP follows a negotiation scheme in which worker agents submit their bids on subtasks, or which they are suited, to a manager agent. The manager agent awards the contract for solving the subtasks to the most appropriate worker agent based on their bids. Contracting involves an exchange of information between agents, an evaluation of the information by each member from its own perspective, and a final agreement by mutual selection. The negotiation protocol adopted for AIO societies is similar with CNP. However, the hierarchical structure employed for the organisation of AIO societies, generates two differences with CNP. First, AIOs are bound by decisions of their superordinate AIOs in a negotiation, while agents in CNP are always free to exit from a contracting process. Second, global optimisation of subtasks is achieved by the manager agent; for a similar approach see also inter-agent cooperation of ICIS [33]. 4.3 Illustration in the Production Environment In order to illustrate the possible role of AIO systems within a production environment we first assume that the hierarchical structure of an AIO society is similar to that of human agents; a possible form of such a hierarchy is given in Figure 2. Further, we analyse the information and material flows between the various divisions of the enterprise (and hence of various AIO groups); a schematic representation is given in Figure

15 Since the majority of manufacturing enterprises now accept the importance of customer requirements, we start from the latter as the driving force of the company's operations. In such companies the life cycle starts with marketing research, where customers' needs and competitors' strategies are studied. Collected information will help to set up strategies and develop business plans. Information work-flow could be easily supported by intelligent AIOs that represent goal-directed office tasks, calculations can be supported by intelligent spreadsheets and their outcome can be combined with AIOs that capture knowledge in strategic enterprise planning; see e.g. [5] for an illustration of a similar approach in the traditional office activities. The marketing research and business plans determine the kinds of products the company wants to produce. The specific descriptions of the product rely on design and manufacturing considerations. Recently developed concurrent engineering concepts encourage the design and manufacturing divisions to participate in the design process, so that the final design will be both manufacturable and functional. Currently, expert systems that are able to support this function consider the intelligent support of the design process, the automatic generation of classification codes, etc. [19]. Then the process control plan are developed to ensure quality during operations. On the other hand, forecasts of demand and information on available capacities and resources are used to generate manufacturing planning schemes. In this area, AIOs could include traditional decision support systems that have practically demonstrated their ability in modelling, 'what if' simulation and quantitative analysis, in order to help users reason on alternative paths and make decisions. Such AIOs could exploit the integration of mathematical programming and optimisation techniques of operations research with the rules and frames of expert systems [20] [37]. The master production schedules lay out a time-phased product demand, and the material requirements planning functions determine the requirements for each level of the product's structure (bill of material). At the shop floor, the production schedule is adjusted, based on short-term available capacities, resources and production orders. The complexity of computations and the scarcity of expertise in production scheduling, can be treated with the use of AIOs that incorporate either simulation-assisted scheduling expertise, or dynamic knowledge acquisition (through machine learning); for reviews of expert systems approaches see [8] and [26]. Operations at the production lines usually emphasize efficiency, productivity and high quality. AIOs can play a significant role in the automated planning and control of flexible manufacturing systems. The loosely-coupled structure of AIO societies fits nicely with the distributed organisation of autonomous, asynchronous, cooperating manufacturing cells; see e.g. [40] for the use of an augmented Petri net model, that is isomorphous to a controlled production system. 5. CONCLUSIONS In this paper we argue that there exist some major common characteristics in the information needs for advanced computing support, in both the production and office environments. A potential solution to the satisfaction of these needs in a uniform way may be based on the use of active intelligent software entities with autonomous processing capabilities, which we