Intelligent Agents based Manufacturing Systems: A ...

Intelligent Agents based Manufacturing Systems: A formal specification derived from FRABIHO F. Aguayoa, J. R Lamaa, M. S. Carrilerob, V.M. Solteroa, M. Marcosb a

Escuela Universitaria Politécnica. Universidad de Sevilla, c/ Virgen de África nº 7, E-41940, Sevilla, SPAIN b Departamento de Ingeniería Mecánica y Diseño Industrial. Universidad de Cádiz. Escuela Superior de Ingeniería. c/ Chile s/n, E-11003, Cádiz, SPAIN, E-mail: [email protected]

Abstract FRABIHO constitutes a reference protomodel for distributed and intelligent engineering production systems, derived from the synthesis of the models proposed in the frame of the fractal, bionic and holonic intelligent manufacturing systems. FRABIHO formalizing becomes necessary as a previous task to the construction of tools for attending the engineering steps of intelligent systems in all its life cycle (design, implementation, production, redesign). This formalization seeks to describe the elements that will constitute the environment of modelling and simulation of an intelligent production system, conceived from the principles of Fractal, Bionic and Holonic varieties. These varieties have given rise to intelligent systems in the environment of Next Generation of Manufacturing Systems, Agile Company, Extend Company and Virtual Company.

1. FRABIHO protomodel The proposals of models and protomodels for the manufacturing systems have come from:

some design methodology, giving rise to wellknown manufacturing models: LEAN, Agile, Flexible, Adaptive, Concurrent, etc.

a) Focus of Bionic inspiration [1-7]. Based on the identification of organizational structures, processes and contexts in those that the natural objects are evolutionarily stable. Later, they must be implemented (as a whole or as a part) into the organization of manufacturing systems. This focus has given place to the fractal, bionic and holonic manufacturing protomodels, Fig. 1, which have been developed mainly inside the Next Generation of Advanced Manufacturing Systems. b) Functional focus [8]. This focus is related to the identification from the required features (flexibility, agility, adaptability, etc) to the manufacturing systems for operating in certain markets. These are later deployed by means of

Fig. 1. Informational view for implementing FRABIHO with multiagent system technology.

Starting from these perspectives a research line has been developed, in the Manufacturing Research Group of the University of Cádiz. As a result of the first researches in this line a proposal of a protomodel synthesis called FRABIHO was formulated [8-11]. This protomodel integrates the models and reference architectures of both Bionic and Functional inspiration, proposed for the design and development of distributed intelligent systems in the context of the design and production, under the new possibilities of the information technologies and operational injunctions of a global economy and markets.

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Teleological, self-assertiveness and integrative orientation. Performance for cooperation and autonomy as in the Domains of Cooperation as in those of Collaboration.

Holonic existence using the management of the variety gradient in relation to the environment. So selforganized processes and self-optimisation can be assumed for canon and strategies.

After the analysis of the required variety for the modelling and simulation of the manufacturing systems from the perspectives of Asbhy’s theory of variety [8], Life-Cycle engineering, complexity views and the generality of their structure levels it was concluded that the pattern of required variety that should support the models was the Holonic paradigm offered by Koestler [12] to support the variety of the Bionic and Functional focuses. In this contribution a formal specification of FRABIHO is exposed [8-11] as a protomodel of a manufacturing system in the informational view, with an ontological grade of generality from the perspective of the life-cycle.

Fig. 2. Main elements of FRABIHO protomodel.

2. FRABIHO formalization 2.1. Formalization of the Holonic agent as a part The formal definition of a Holonic multiagent manufacturing system for the informational view, with integration of FRActal, Bionic and HOlonic variety, is the origin of the FRABIHO protomodel [10-11]. Its main elements are shown in the Figure 2. Starting from them, the following model of Architecture of Multiagent System is derived. SMAholonic ::= Functional foundations and processes characterizing the dynamic character (behaviour) of an Holonic entity [13-23] has been considered close to these main elements of the Architecture of the Multiagent Manufacturing System with holonic inspiration has been considered. Among them can be highlighted:

The model starts up from the internal perception of the external world that each Holonic agent receives They can choose to act on the environment in an adaptative way from their perceptions and internal states. Each holon should possess a group of possible states, a group of perceptions of their environment and a group of actions that allow acting. They will have got a feature for decision taking that determines the acting mode associated to their average states and their current perceptions, as well as a feature that allows learning and adapting into the environment. A Manufacturing Holonic agent Hi with FRABIHO variety in its informational view vinf, in all area of Holonic analysis AHi, and holonic domain DHi, determined by the levels n and n+1 can be described by the holon AHi/pvinf as a part, integrated in one or more holons of n+1 level:

Level

Holonic Domain

Analysis Area A

n+1

n

n-1

Fig. 3. Holonic modeling Areas and Domains.

=:: < si , pi , ai , φi , Σ, Π , Λ, Ψ , Ε, DColab, CV > Where: -

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-

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si: Possible states group of the hi holon. A state is characterized by a state vector: Ψ : si × pi → si +1 pr[0,1]n

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pi: They form input perceptions group of the hi holon. They are characterized by a vector of perception and inputs (input holarchy). ai: They are the group of Holonic permitted actions (holon). They allow operating on the environment, forming its different competencies and capacities. They are represented by a vector of outputs (output holarchy).

φi : They form an operational set of functions, processes, activities of decision making of the holon. They are carried out from it state and the perception of the environment, which it not only determines the action but rather it activate the impact evaluation functions over the environment and the probability evaluation functions of transition to the new state, this feature represents the Holonic agent's metacompetencies. A formal specification: φi : si × pi → si × ai × Ψ

Σ : They are the group of Ehi environment states. They are specified by a state vector, like the states of Holonic agents of the immediate environment. In these, the Π perception function is applied to obtain the vector of perception of the environment. Π : They constitute a perception function or input that determine the Holonic agent's perceptions/ inputs, through its Ifi interfaces (domain of the holonic environment DEhi). They are receptive to the environment information.

Π : Σ → ( p1 × .......... × p n ) -

∆ : It is an impact or modification function of the Ehi environment for the Holonic agent. The status of the modified world is e' = ∆ (e1, a1, ..... an) that give place to the new state e'. ∆ : Σi × (a1 × .......... × an ) → Σi +1

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Ψ : It is the transition probabilities function of the internal states other Holonic agent, that they are upgraded in the operation phases and/or the life-cycle of the Holonic agent. Ψ : si × pi → si +1 pr[0,1]n

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Ω : It is a function or algorithm for the upgrade of the probabilities function and of the internal states probabilities of the Holonic agent based on the results obtained in the environment. This allows the adaptation from the Holonic agent to

i =n

the environment and to model learning processes. This function represents the metacapacities of the Holonic agent. Ω : si × pi × ∆ → pri +1si

Vci = I p1 ×... × pn i =1

i =n

Mmci = I Bchi i =1

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DColab: Collaboration domain of an Holonic agent hi, which is defined as the different forms of this agent's collaboration like part of the holarchy for the n level, in the Holonic agents n, n+1 (as they have a hierarchical structure or network structure). It is characterized by the Holonic entity set hi of n+1 level or n in that it participate with a φi role with the goals to develop. CV (Life-Cycle): The time is intended like a succession of regular events. For each time interval all hi Holonic agent receives from their p ∈ Σ environment and from their own

si ∈ Si state, their local perception. Starting from this perception, the holon determines its new state and its next action. A succession of events that drain the state space Ee with the required information for its management constitute the life-cycle of the Holonic agent. The proposed model assures that the Holonic agent have the properties posited by Koestler so that an entity (intelligent agent) constitutes a holon, like: a) Autonomy. A Holonic agent is autonomous if and only if its behaviour depends on its state and of its perceptions, that is to say φi = si × pi → si × ai . Conditions that complete the proposed model, in the case that the behaviour depends on the actions or behaviour of another holon the autonomy will restricted  → ⊂ si × ai . be partially φ r = si × p i  b) Cooperation. A holonic agent is cooperative when its action plan and its perceptive outline are reached by the convergent integration of the perceptions of another holons of its environment, of its perceptions and potentials actions, that is to say, the decision function of the model should settle down in the following way: φi = Mmci × Vci → Mmci × Ai , being φi decision making function, Mmci mental shared models, and Vci shared vision, where:

In those that Bchi is the knowledge base of the hi Holonic entity and pi is the holon perception. Remember that Ω : Mmci × Vci × ∆ → pri +1 ( si ) is the function of upgrade that model learning processes. c) Self-assertiveness. A Holonic agent have selfassertiveness when its performance, perception and state are not couple to other Holonic agents, therefore: in φi = si × pi → si × ai the performance only depends on its state ei and its perception pi. This way starting from its perception the holon determine its new state and its next action, in a uncoupled mode for other Holonic agents. The cooperation modes are drivers of the different classes of making decisions functions, giving place to the swinging among cooperation and assertiveness required according to the class of task. d) Self-organization. A holon has self-organization capacity if it has the function Ω : si × pi × ∆ → pri +1 ( s ) that allow it to obtain new probabilities function or state space probabilities and it generate adaptative behaviour to the new conditions of the environment.

2.2. Formalization of the Holonic agent like whole

An manufacturing Holonic agent Hi in its informational view, in all area of Holonic analysis AHi, and Holonic analysis domain Dhi, enclosed by the n and n-1 levels, it can be described like a Holonic whole AHi / tvinf that integrate the holons of n-1 level: =::

Where: -

Group of Holons as a part (n-1 level): This is made up by a Holonic group, where: H i / p = {h1 ,..., hn } are the group of the holons

like part belonging to the level n-1 or n (as they have a hierarchical structure or network structure) in those that it are carried out a partition for processes or functional areas that it constitute a subset of holons called Cooperation Domain π ( H i / t , p) → DCoopi . All holon as a part in the n-1 level are specified in the Cooperation Domain DCoopi: ::= -

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ID: it is the specification of the identity and goals of the holon of n-1 level or n belonging to the cooperation domain DCoopi that integrate the holón like whole of n level. RDCoop: They are the resources required by the collaboration domain to develop the tasks object of their responsibility. These are constituted by communication resources, coordination, control and operational characteristic of the task.

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Role: The role is the function that are assigned to the holon in the cooperation domain and that it carries out as a part hi of n-1 level. They are the specification of the functions, processes, activities or operations that the holon support, constituting its responsibility environment, the realizations of its competition and the required competitions for it. It can be defined as: ::=

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F: Set of functions or processes associated to the role.

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Dr: Domains and responsibility environments of the role.

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R: Realizations associated to the combined role of activities and operations that integrate it.

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C: Competencies associated to the role and its classes of associate knowledge.

3. Conclusions The Companies and the Distributed Manufacturing Systems supported for Information Technologies and Knowledge Technologies require of environments and tools that attend in all the stages of their life-cycle for their agile and flexible conception, implementation,

reconfiguration and reusability of the experience and the knowledge acquired in their operation. FRABIHO constitutes a protomodel that establish the formal previous frame to the development of tools that they configure an environment of Engineering of Companies and Distributed Intelligent Manufacturing Systems, starting from which can be derived the required architectures with flexibility, agility and robustness, giving the required resilience to operate in a chaotic environment. Acknowledgements This work has received financial support by the Andalusian Government (III Plan Andaluz de Investigacion). References [1] Okino N., Bionic Manufacturing Systems (BMS), BioModel on base systems design, CAM-I, Annual International Conference, New Orleans (USA), 1980. [2] Ueda K., Ohkura K., A biological approach to complexity in manufacturing systems, in Proc. of the 27 th CIRP International Seminar on Manufacturing Systems, Ann Arbor, 1995, pp. 69-78. [3] Warnecke, The Fractal Company: A revolution in corporate culture, Springer, Berlin Germany, 1993. [4] Sihn W., The fractal factory: A practical approach to agility in manufacturing, Proceedings of Second Word Congress on Intelligent Manufacturing Processes &Systems, Budapest, Hungary, June 10-13, 1997, pp. 617-621 [5] Ulieru M., Walkel SS., Brennan RW., The holonic enterprise as a collaborative information ecosystem. Documento Internet, Junio 2002, URL: http://igs.enme.ucalgary. ca/. [6] Tharumarajah A., Welles AJ., Nemes L., Comparisons of bionic, fractal and Holonic manufacturing systems concepts, International Journal Computer Integrated Manufacturing, 9 (3) (1996) 217-226. [7] Molina A., Sanchez JA., Kusiak A., Handbook of life cycle engineering concept, models an technologies, Ed. Kluwer Academic Publishers, London (UK), 1998. [8] Aguayo F., “Design and Manufacturing of Product in Holonic Systems. Application to a Holonic Design Module” D. Phil. Thesis, University of Cádiz, Cadiz (Spain), 2003. [9] Marcos M., Aguayo F., Carrilero MS., Sevilla L.;, Ares J. E, Lama JR., “Toward the Next generation of Manufacturing Systems, FRABIHO: A Synthesis Model for distributed Manufacturing, 1st I*PROMS Virtual International Conference 4-15 July 2005, Intelligent Production Machines and Systems 35-40 ELSEVIER.

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