Crisis Control Mechanism in Holarchy Architectureaeuso.org/jkbei/wp-content/uploads/2016/06/26-Crisis-Control-Mecha… · domains where goals can be recursively broken down into subtasks

Vol. 2(4), Jan. 2016, pp. 239-249

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Article History: JKBEI DOI: 649123/11035 Received Date: 23 Sep. 2015 Accepted Date: 14 Dec. 2015 Available Online: 11 Jan. 2016

Crisis Control Mechanism in Holarchy Architecture Imane Basiry and Nasser Ghasem-Aghaee

Dept. of Computer Eng., Sheikh Bahaee University, Isfahan, Iran *Corresponding Author's E-mail: [email protected]

Abstract oday, scheduling is a basic activity in large scale systems which have unexpected and high complexity demands. Instances of these complex systems can be found in manufacturing, logistics, economics, traffic control, and biology (e.g. in social insects and immune systems). Scheduling is more important

in crisis conditions for large scale systems. Industrial crises such as economic and demands changes, breakdown, repair or upgrade of machines in industrial environment, have side effects on produce or to do services, so system controllers must have robust strategy to solve crises in large scale systems. Multi-agent based architecture is distributed collections of interacting entities which function without a supervisor. The advantage of holonic self-organization concepts lies in the fact that they help to achieve more efficient performance. According to these principles, several approaches have been designed; however, these approaches are too weak to handle emergency demands in an industrial environment. This chapter addresses the concepts of multi-agent and holonic systems and discusses their advantages and weak points. Afterwards, a holonic control architecture is proposed to improve weak points. The main objective of this architecture is to solve crises in large scale systems for basic holarchy architecture that presented in previous article, without time and complexity overload. To reach this purpose, the concept of behavioral self organization and task priority are attempted. Task priority in an unexpected situation cause reduce time delay to handle critical tasks. The architecture was tested in a simulation environment.

Keywords: Multi-agent systems, Holonic systems, Self-organization, Task priority, parallel processing, Scheduling.

1. Introduction Multi-agent systems are systems consisting of multiple interacting computing factors known as agents.

Agents are computer systems with two important abilities. First, they are, at least to some extent, capable of the autonomously deciding what they need to do to achieve their goals. Second, they are able to interact with other agents not by exchanging data, but by engaging in analogues of the kind of social activities which everyone does in everyday life: cooperation, negotiation, coordination, and the like [26]. In the literature, the agent paradigm demonstrates a viable solution in complex systems modeling. In particular, Multi-Agent Systems have been used with success in a large variety of distributed intelligence settings [20]. The organization of a multi-agent system is the set of roles, authority and relationship structures which control its behavior. All multi-agent systems contain some or all of these features and therefore they all have some form of organization, although it might be implicit and unofficial. Just as with human organizations, such agent organizations determine how the population members interact with others, not necessarily on a moment by moment basis, but in the long-term course of a particular goal or some goals. This guidance might affect data flow, authority relationships, coordination patterns, resource allocation, or any number of other system features [32, 19, 3 and 22]. Many researchers have demonstrated that the organizational design that is used in an agent system may have a significant effect on its quantity of performance. A series of

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Imane Basiry et al. / Vol. 2(4) Jan. 2016, pp. 239-249 JKBEI DOI: 649123/11035

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organizational strategies has appeared from this line of research, of which each strategy has different strengths and weaknesses. Some kinds of organization for multi-agent systems are hierarchy, holarchy, and absolute autonomous organization. A holon, just like an agent, is an intelligent entity able to interact with the environment and to take decisions to solve a specific problem. Significantly, Holons are able to play the role of a whole and a part at the same time. This exerts an effect on the organizational level: holarchy functions firstly as autonomous wholes in supra-ordination to their parts, secondly as affiliate parts in sub-ordination to controls on upper levels, and thirdly in coordination with their environment [20]. A comparison between holarchies and hierarchies in organizations reveals that holarchies are more easily applied to domains where goals can be recursively broken down into subtasks which can be allocated to individual holons. The degree of autonomy in an individual holon is undefined and could differ between levels or even between holons at same level. There is the assumption that the level of autonomy is neither complete nor completely absent [5].

The hierarchy or hierarchical organization is the earliest kind of organizational structured design for multi-agent system and earlier distributed artificial intelligence architectures [24]. The data made by lower-level agents in a hierarchy typically go upwards to get a general view, while the control signal or supervisory orders flow from higher to lower agents [1].

Holonic organizations are derived primarily from the partially autonomous and encapsulated nature of holons. Holons are usually endowed with enough autonomy to define how to most efficiently address the requests they receive. Because the requester does not need to know exactly how the order will be completed, the holon potentially has a great flexibility degree in its choice of behaviors, which can enable it to nearly coordinate potentially conflicting or complementary tasks. This feature decreases the knowledge burden placed on the requester and allows the holon’s behavior to adapt dynamically to new situations without coordination in future, so long as the original commitment’s needs are met [18].

Many architectures and developments in holonic manufacturing are presented with different application domains, such as scheduling, manufacturing and control, materials handling, machine controllers and assembly systems, as referred to in [17].

2. Related literature The concept of Agent-oriented programming and centering software on the concept of agent was first used by YoavShoham in 1990. His agents have a specific paradigm as one method with a single parameter [37]. Multi-agent systems contain agents and their environment. Typically, multi-agent systems research addresses software agents. However, the agents in a multi-agent system could equally well be robots or humans teams [9]. Topics of research in MAS contain scientific ([7]; [16]), agent-oriented software engineering, and robotic issues [28].

Horling and his group conducted a survey on multi-agent organizational paradigms, which includes hierarchies, holarchies, teams, coalitions, congregations, societies, federations, and matrix and market organizations. It provides an explanation of each, takes into consideration their advantages and disadvantages, and offers examples of how they may be instantiated and maintained [5].

The term ‘holon’ was first defined by Arthur Koestler in his book, ‘The Ghost in the Machine’. According to his description, a holon (Greek: ὅλον, holon neuter form of ὅλος, holos "whole") is something which is simultaneously a whole and a part [2].

Holonic organizations have proven to be effective and efficient solutions to many problems with hierarchical and self-organizing structures [33] and have been successfully used in a large range of complex systems; for example, in transportation [4], manufacturing systems [34], adaptive mesh problem [35] and health organizations [25].

Traditionally, manufacturing control systems use hierarchical control structures that centralize the processing power of a shop-floor control in one central node. They increase optimization and performance; however, when conditions change, they do not function appropriately and lack predictability and scalability.

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These rigid, monolithic control structures are not sufficient to confront the current requests coming from manufacturing environments which demand flexibility, robustness, responsiveness and re-configurability. New manufacturing paradigms have thus appeared, of which the common denominator is the distribution and decentralization of processing power over many entities, albeit with reduction of system performance in process optimization. The early instances of such paradigms were Reconfigurable Manufacturing Systems (RMS) [38], and Multi-agent Systems (MAS) [15]. Afterwards, Bionic Manufacturing Systems (BMS) and then Holonic Manufacturing Systems (HMS) [31] were developed. Finally, Evolvable Production Systems (EPS) [23] were established in 2006. RMS is a concept suggesting speedy change in the factory’s structure using modifications in software and/or hardware so as to control the production functionality and capacity quickly [10]. An RMS system should demonstrate the following features [38]: integrability, customization, modularity, diagnosable and convertibility. An MAS [15] is both a technology and a paradigm which advocates the design of systems based on the societies of distributed, decentralized, intelligent and autonomous entities known as agents. In these systems, each agent has a sectional view of the environment and must therefore cooperate with others in order to achieve the global goals; the behavior of the global system stems from the cooperation between individual agents. An HMS [31] is a paradigm which interprets the concepts of living organisms and social organizations developed by Koestler [2] for the manufacturing.

MetaMorph [8] and its deputy MetaMorph II [36] were projects which, in the first place, attempted to provide an agent-based approach to the management and creation of agent communities in distributed manufacturing environments, and, secondly, to integrate cross-enterprise activities such as planning, design and scheduling. AARIA (Autonomous Agents at Rock Island Arsenal) was presented in 1997, the early years of agent based architectures, with the particularity of using internet as communication between agents, for military production [12]. One of the best-known practical samples using multi-agent systems is probably on one of the Daimler Chrysler production lines ([30], [29]). This architecture attempted to use agent technology for both flexible and dynamic transportation systems and control systems. One of the most notable HMS samples is the PROSA (Product-Resource-Order-Staff Architecture), which describes the main guides for developing a generic manufacturing control layer [11]. A real application of PROSA was implemented at Cambridge University using a packaging cell [21], where cooperation was organized between resource and order holons in order to satisfy clients’ demands. Order holons use negotiation techniques to ensure reliable and fast production and are also accountable for tracking production progress. On the other side, the main goal of resource holons is to maximize the return on the implementation of their services, and, finally, the product holon is responsible for buying and selling goods.

The ADACOR (ADAptive holonic COntrolaRchitecture for distributed manufacturing systems) [27] is a holonic architecture which aimed to represent an adaptive production control approach equilibrium from a static state to a transient state, in unexpected and normal situations, respectively, merging the advantages of hierarchical and heterarchical control structures using an adaptive mechanism. Jose Barbosa and his coworkers propose an evolution to the ADACOR holonic control architecture inspired by biological and evolutionary theories. In particular, a two-dimensional self-organization mechanism was presented for behavioral and structural vectors under consideration. Behavioral self-organization is at micro level, where each individual holon can change its behavior in different external conditions. Structural self-organization is at macro level and helps the system to evolve drastically by changing the relationships between the holons[14]. Jose Barbosa evaluated his model with the case study system known as AIP-PRIMECA Flexible Manufacturing System (FMS) [6].

All holonic researches have attempted to present a flexible and dynamic model for large scale systems; however, time and complexity over load in models and approaches are of great significance. In this section, a new model is proposed for large scale, complex and dynamic systems. This model uses behavioral and structural self-organization in holonic structures to provide flexible, robust and nonstop systems in critical conditions without time and complexity over load.



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3. Suggested model In this research a new model is designed for control structures of large scale, complex and dynamic systems. A model with holarchy architecture and task priority that has self-organization which activates in crisis conditions and make the system more robust with better scheduling. The presented model in this article is improved the model in [13] for behavioral aspect for prevalent crises in large scale systems. A graphical explanation diagram is presented in Fig. 1, which shows architecture of the model.

Figure 1: Suggested model diagram.

3.1. Model components

According to [13], in this model there are three level of holarchy and four basic holons too:

• Holon type I (Entrance Holon),

It receives production orders from environment, breaks each product to sub-products, sends name of each sub-product as an ACL message to holon type II.

• Holon type II (Divider Holon),

It receives message(s) from Entrance Holon and sends name of each sub-product to related maker holon. It can match orders to maker holons according to its knowledge about the system.



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• Holon type III (Sub-product Maker Holon),

It receives message from Divider Holon, puts first task for the sub-product manufacturing according to its instruction in common data source. It will be waiting for one machine controller holon in common data source to declare: one machine performed this task. Then this holon continues its work to the end of sub-product manufacture instruction. Instruction enables this holon to scheduling and planning about machines, specifies input and output parameters and sequence of tasks implementation for manufacturing sub-product.

• Holon type IV (Machine Controller Holon ),

This kind of holons have the highest percentage of autonomy and never receive direct order or command from other holons. Each holon in this group has relation with a workstation or machine in environment and is able to order it to do activity. These holons have authority to choose tasks from common data source according to their related machine skills and sends notification about the end of task for common data source.

3.2. Preparing the model for critical conditions

One of the prevalent crises in large scale systems is the breakdown of machines. Therefore, the suggested model must offer a strategy to confront this kind of crisis. Since machine controllers can sense machines changes, they are the best holons for informing the system of this situation. In the suggested model, when a Controller Holon informs adjacent holons (holons at the same level) of a crisis, all Controller Holons respond with messages including their common skills with breakdown machine.

Afterwards, the breakdown machine controller selects the most similar machine for replacement and sends the message “Delivery common tasks” to it. If the replacement holon (according to all machine skills) knows of at least one other holon which can perform non-subscriber tasks, its own skills reduce to common skills of the breakdown machine. Therefore, it can professionally perform these tasks. This act of the system can prevent bottlenecks. If the breakdown machine is repaired and returns to system, its controller sends a “crisis removal” message to the replacement holon. As a result, the replacement holon reverts to its own skills and normal condition. Fig. 1 in third level, shows behavioral changes introduced to Controller Holons in order to implement the strategy to solve prevalent crises.

Sub-holon 9 is responsible for finding the best replacement for the related machine confronted with crisis. Sub-holon 10 is responsible for changing (increasing or decreasing) machine skills. This is a kind of behavioral self-organization for the system.

This model supports structural self-organization, too. Based on the following cases, structural self-organization is important and necessary for large and dynamic systems:

• If according to environment changes, a workstation is added to the system, its Controller Holon must be added to level 3 of holarchy.

• If a workstation is going out of the system forever, its Controller Holon is counted useless and is removed from the system after a certain period of time. Controller Holon will be removed by itself.

• If the model is allowed to create new skills for machines (devolve M2 skills to M5), agents can be able to change their holons and there could exist structural self-organization.

4. Implementation and numeric evaluation The model performance is evaluated numerically with this test case. The model is used for workstations scheduling in a factory which produces English words for teaching language to children. The case study used in this work is available in details in ([14]; [6]). The case study system is AIP-PRIMECA Flexible Manufacturing System (FMS) and includes five workstations which are linked by a conveyor system. The system is able to



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manufacture three products, namely the words ‘‘AIP’’, ‘‘BELT’’ and ‘‘LATE’’, each of which comprises sub-products (e.g. the product ‘‘AIP’’ is composed of the sub-products ‘‘A’’, ‘‘I’’ and ‘‘P’’). The parts are assembled by the workstations according to a process plan or instruction which is presented for each part, as shown in Table 1. The workstations can perform a set of operations matching their skills, as shown in Table 2 (also including the processing time, in seconds, of each operation). A product is completed if its latest job is consummated. A client order serves as a collection of products and each of the products is considered a collection of jobs. In addition, each of the jobs is considered a set of machine tasks. Thus, the due date of the client order can be assigned to this total set of jobs. Experimental simulation scenarios were developed to evaluate the proposed model. In the first scenario, the system has a client order which is a set of two jobs (“BELT” and “AIP”) with any perturbations. The product delivery time is computed. (According to scenario #0 of the benchmarking platform [6]). Hence, in the model, EH holon receives orders and sends 7 ACL message to DH holon, each message including a letter (“B”, “E”, ”L”, “T”, “A”, “I”, ”P”). DH receives messages, and, according to its knowledge of the system, sends each sub-product to one maker. For practicability reasons, the model and the test case were executed in JAVA and the agents were created by the JADE platform. The communication uses a direct interaction message exchange using the FIPA Request

Table 1: Production sequence for each type of job.

B E L T A I P

#1 Plate

loading

Plate

loading

Plate

loading

Plate

loading

Plate

Loading

Plate

loading

Plate

loading

#2 Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

#3 Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

Axis

mounting

#4 Axis

mounting

Axis

mounting

Axis

mounting

r_comp mounting

Axis

mounting

I_comp mounting

r_comp mounting

#5 r_comp mounting

r_comp mounting

I_comp mounting

L_comp mounting

r_comp mounting

Screw_comp mounting

L_comp mounting

#6 r_comp mounting

r_comp mounting

I_comp mounting

Inspection L_comp

mounting Inspection Inspection

#7 I_comp mounting

L_comp mounting

Screw_comp mounting

Plate unloading

I_comp mounting

Plate unloading

Plate unloading

#8 Screw_comp mounting

Inspection Screw_comp

mounting

Screw_comp mounting

#9 Inspection

Plate unloading

Inspection Inspection

#10 Plate

unloading

Plate unloading

Plate

unloading



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Protocol. The main control loop in the agents is executed using JADE’s cyclic behavior to increase the reactiveness of the system. The programs were tested on Intel Core i5- 2410M, 6GB RAM and runningWindows7 64b.

our experimental simulation scenarios were developed to assess the proposed architecture.

1- The system has a client order that is a set of two jobs (“BELT” and “AIP”) with no perturbations. Time for product delivery is computed. (According to scenario #0 of the benchmarking platform [6]).

2- On the basis of the first scenario, failures occurred in workstation M2 at a rate of 25% (according to scenario #PS12 of the benchmarking platform [6]). This means that the machine will breakdown every four tasks. In this situation, the work order is affected and the subsequent ones need to be re-scheduled. In manufacturing duration for “BELT” and “AIP”, at least two failures occurred and M3 tolerated all the pressure in these times.

3- In the third scenario, the model used behavioral self-organization to handle crises in the second scenario. Corresponding to the model features, the system can sense M2 situation. In each period, when M2 is breaking down, according to the skills table, M3 (the most similar machine to M2 in skills) as M2 alternate, devolves Screw_comp task completely to machine #4 and reduces its duties to axis and r_comp (common skills with M2) for handling the situation. After 60s, M3 will revert to normal skills. This scenario can show behavioral self-organization and high level of autonomy in the system.All scenarios are implemented completely on case study system with the suggested model as a holarchy control structure for machines and the results are recorded. In this work, the comparison between the control approaches considers the manufacturing of ‘‘BELT’’ and ‘‘AIP’’ products. The objective is to reduce the time range to finish orders (Cmax). Fig. 2 presents a GANTT diagram for scenario#1 in the best implementation. There are jobs and types of operation tables as the legend on the figure. Each row in Fig. 2 for M1-M5 displayed work sequence and each task duration in the system. This figure is comparable with Fig. 9 in the benchmark article [6].

Table 2: Machine skills and processing times (in seconds)

operation M1 M2 M3 M4 M5

Loading 10

Axis 20 20

r_comp 20 20

I_comp 20

L_comp 20 20

Screw_comp 20 20

Inspection 5

Unloading 10



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Figure 2: GANTT diagram for scenario#1.

This comparison demonstrates that machines M2 and M3 have better scheduling and they do not waste time in a system with the suggested model; therefore, Cmax is reduced for this approach in the first scenario. In each execution experience, the suggested model present different Cmaxs, due to lack of memory storage, to save the best sequence of handling sub-products and tasks. Therefore, the maximum time in 10 implementations is reported in table 3 for each scenario.

The results reveal that M2 breakdown exerts an effect on system performance; it can increase the make span by 16.48%. However, self-organization can change each individual holon behavior according to the external conditions and it can decrease the impact of perturbations to 11.43%. Fig. 3 presents a GANTT diagram for a scenario with M2 critical conditions and self-organization in the best implementation.

Table 3: Suggested model results.

Suggested model

Cmax with no perturbations (s)- scenario#1 404.0

Cmax with perturbations (s)-scenario#2 470.6

Impact of perturbations (%)in compare with scenario #1 16.48

Cmax with perturbations (s)with behavioral self-organization 450.2

Impact of self-organization(%)in compare with scenario #2 4.34

Impact of self-organization (%) in compare with scenario #1 11.43



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Figure 3: GANTT diagram for scenario#3.

Fig. 3 illustrates M2 breakdown and M3 activity as a replacement holon in self-organization process. Table 4 summarizes the results for each control approach in Jose Barbosa [14] and compares it with the suggested model in each case. As illustrated, there is 11.28% improvement for the suggested model in scenario #1(with no perturbations) in comparison with ADACOR2. In the scenario with perturbations (scenario #3 in this article) where approaches use self-organization, the suggested model can reduce performance degradation due to M2 breakdown by approximately 3.37%. 5. Review of suggested model features The suggested model is designed to find optimized scheduling and planning for several workstations or machines in an environment. Optimized scheduling and planning has some parameters which are considered in the model. In the system with this control structure model: • Machine time is managed; in machine activated time, it has high performance. In other words, due to lack of proper planning, unit inactivity makes a loss for the whole system. On the other hand, when a machine is busy, there are not untimely orders to disrupt machine process. • The system has maximum usage of parallel task implementation according to real environment facilities and instruction limits. • The system senses the state of each machine in order to solve machine breakdown problems. • Autonomy level of each entity is flexible during working. It is necessary for the self-organization step to work in crisis conditions. • Task priority degree of each task can be defined. It is essential for handling orders with time limit to deliver.

Table 4: Results experiments.

ADACOR ADACOR2 Suggested model

Cmax with no perturbations (s) 455.4 455.4 404.0

Cmax with perturbations (s)with behavioral self-organization

535.4 522.8 450.2

Performance degradation (%) 17.6 14.8 11.43



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Conclusions and future research directions In this research a new model was designed; a model with holarchy structure and self-organization. The model consider characteristics of large scale systems—largeness of scale, complexity, heterogeneity, flexibility, trustworthiness, and distributed operation and administration to reach robust strategy to solve prevalent crises conditions. The presented model can be flexible in expected and unexpected conditions. Four experimental simulation scenarios were developed to assess the proposed architecture. In addition, the results show that the suggested model does not have time and complexity overload for the system. Even in the case study for scenario #1 it reduced make span 10.14%. As mentioned previously, the success factor in reducing make span was changing the data transfer method in the model and combining traditional ACL messaging between agents and the common data source. Sometimes designers can increase holons or agents autonomy and define some rules to replace the order method with selection method for them, because messaging in multi-level hierarchy is time-consuming. Therefore, in the environments, where work quality of workstations is the same, and there is no requirements supervisor, the common data source is useful for selection method. This architecture can be a base for other scheduling problems. Future research shall attempt to incorporate machine learning algorithms into this model, guaranteeing that the system uses maximum resource capacity without wasting it in every implementation. The mission is preparing an autonomous system in high level with high performance. References [1] H. Bond and L. Gasser, "An analysis of problems and research in DAI," J. Distributed artificial intelligence, pp. 3-35, 1988. [2] Koestler, The ghost in the machine. New York: Macmillan, pp. 547- 560, 1968. [3] Uhrmacher and D. Weyns, Multi-agent systems. Boca Raton: CRC Press/Taylor & Francis, 2009. [4] B urckert, H.-J.,Fischer, K.,andG.Vierke, "Transportation scheduling with holonic mas- the teletruck approach," Proc. 3rd Int.

Conf. Practical Applications of Intelligent Agents and Multiagents, pp. 577–590, 1998. [5] Horling and V. Lesser, "A survey of multi-agent organizational paradigms," The Knowledge Engineering Review, vol. 19, no. 04,

p. 281, 2004. [6] Trentesaux, C. Pach, A. Bekrar, Y. Sallez, T. Berger, T. Bonte, P. Leitão and J. Barbosa, "Benchmarking flexible job-shop scheduling

and control systems," Control Engineering Practice, vol. 21, no. 9, pp. 1204-1225, 2013. [7] F. Cucker and S. Smale, "On the mathematics of emergence," Jpn. J. Math., vol. 2, no. 1, pp. 197-227, 2007. [8] F. Maturana, W. Shen and D. Norrie, "MetaMorph: An adaptive agent-based architecture for intelligent manufacturing," Int. J.

Production Research, vol. 37, no. 10, pp. 2159-2173, 1999. [9] G. Kaminka, "Robots are agents, too!" Proc. 6th Int. joint Conf. Autonomous agents and multiagent systems - AAMAS, 2007. [10] H. ElMaraghy, "Flexible and reconfigurable manufacturing systems paradigms," Int. J. Flexible Manufacturing Systems, vol. 17,

no. 4, pp. 261-276, 2005. [11] H. Van Brussel, J. Wyns, P. Valckenaers, L. Bongaerts and P. Peeters, "Reference architecture for holonic manufacturing systems:

PROSA," Computers in Industry, vol. 37, no. 3, pp. 255-274, 1998. [12] H. Van Dyke Parunak, A. Baker and S. Clark, "The AARIA agent architecture," Proc. 1st Int. Conf. Autonomous agents – AGENTS

'97, 1997. [13] I.Basiry, N.Ghasem-Aghaee,”Task priority and self-organization mechanism in holarchy architecture,” 2nd IEEE Int. Conf.

Knowledge-Based Engineering and Innovation – KBEI – Iran, Tehran, p.p. 872 – 879, 2015. [14] J. Barbosa, P. Leitão, E. Adam and D. Trentesaux, "Dynamic self-organization in holonic multi-agent manufacturing systems:

The ADACOR evolution," Computers in Industry, vol. 66, pp. 99-111, 2015. [15] J. Ferber, Multi-agent systems. Harlow: Addison-Wesley, 1999. [16] J. Shen, "Cucker–Smale Flocking under Hierarchical Leadership, " SIAM J. Appl. Math., vol. 68, no. 3, pp. 694-719, 2008. [17] K. Fisher, "Agent-based design of holonic manufacturing systems," Robotics and Autonomous Systems, vol. 27, no. 1-2, pp. 3-

13, 1999. [18] L. Bongaerts, "Integration of scheduling and control in holonic manufacturing systems," Ph.D. [Dissertation], PMA, K. U. Leuven,

Leuven, Belgium, 1998. [19] L. Yilmaz and T. Ören, Agent-directed simulation and systems engineering. Weinheim: WILEY-VCH, 2009. [20] M. Calabrese, A. Amato, V. Lecce and V. Piuri, "Hierarchical-granularity holonic modelling," J. Ambient Intell Human Comput,

vol. 1, no. 3, pp. 199-209, 2010. [21] M. Fletcher, D. McFarlane, A. Lucas, J. Brusey and D. Jarvis, "The Cambridge Packing Cell — A Holonic Enterprise Demonstrator,"

Lecture Notes in Computer Science, pp. 533-543, 2003. [22] M. Kazemifard, N. Ghasem-Aghaee and T. Ören, "Emotive and cognitive simulations by agents: Roles of three levels of

information processing", Cognitive Systems Research, vol. 13, no. 1, pp. 24-38, 2012. [23] M. Onori, J. Barata and R. Frei, "Evolvable Assembly Systems Basic Principles," Information Technology For Balanced

Manufacturing Systems, pp. 317-328, 2006.



249



[24] M. S. Fox, "An organizational view of distributed systems". IEEE Transactions on Systems, Man, and Cybernetics, vol. 11, pp.70–80, 1981.

[25] M. Ulieru and A. Geras, "Emergent holarchies for e-health applications: a case in glaucoma diagnosis," IEEE Annu. Conf. Industrial Electronics Society IECON, vol. 28, 02, 2002.

[26] M. Wooldridge, An introduction to multiagent systems. Chichester: Wiley, p. 366, ISBN 0-471-49691-X, 2002. [27] P. Leitão and F. Restivo, "ADACOR: A holonic architecture for agile and adaptive manufacturing control," Computers in Industry,

vol. 57, no. 2, pp. 121-130, 2006. [28] S. Ahmed and M. Karsiti, "A testbed for control schemes using multi agent nonholonomic robots," IEEE Inte. Conf.

Electro/Information Technology, 2007. [29] S. Bussmann and J. Sieverding, "Holonic control of an engine assembly plant: an industrial evaluation," IEEE Int. Conf. Systems,

Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace, 2001. [30] S. Bussmann and K. Schild, "Self-organizing manufacturing control: an industrial application of agent technology," Proc. 4th Int.

Conf. MultiAgent Systems, 2000. [31] S. Deen, Agent-based manufacturing. Berlin : Springer, 2003. [32] S. Hayden, C. Carrick and Q. Yang, "A catalog of agent coordination patterns," Proc. 3rd Annu. Conf. Autonomous Agents –

AGENTS '99, 1999. [33] S. Rodriguez, V. Hilaire and A. Koukam, "Formal Specification of Holonic Multi-Agent Systems Framework," Lecture Notes in

Computer Science, pp. 719-726, 2005. [34] S.M.Deen, "A cooperation framework for holonic interactions in manufacturing," Proc. 2nd Int Conf. CKBS, pp. 103–124 ,1994. [35] S.odriguez, V.Hilaire, A.Koukam, "Towards a methodological framework for holonic multi-agent systems," Proc. 4th Int.

Workshop of Engineering Societies in the Agents World, London, pp. 179–185, 2003. [36] W. Shen, F. Maturana and D. Norrie, "MetaMorph II: an agent-based architecture for distributed intelligent design and

manufacturing," J. Intelligent Manufacturing, vol. 11, no. 3, pp. 237-251, 2000. [37] Y. Shoham, "Agent-oriented programming," Artificial Intelligence, vol. 60, no. 1, pp. 51-92, 1993. [38] Y.Koren, F.Jovane and U.Heisel, "Reconfigurable Manufacturing Systems. A keynote paper," Ann. CIRP, pp. 527-540, 1999.

Authors

Imane Basiry received her B.Sc. in Information Technology Engineering and M.Sc. in Artificial Intelligence both from the university of Sheikh Bahaee, Iran, in 2016 and 2013. Her main research topics focus on the development of self-organizing and manufacturing control architectures following the holonic and multi-agent system paradigms. She has a technical report in Human-Computer Interaction (HCI) technologies and services. She develops Digital Image Processing (DIP) software which has the ability to simulate

house decoration. She likes cooperation in scientific activities and group researches.

Dr. Nasser Ghasem-Aghaee is a co-founder of Sheikh Bahaee University of Higher Education in Isfahan, Iran, as well as Professor in the Department of Computer Engineering at both the Isfahan University and Sheikh Bahaee University. He received his Ph.D. & M.Sc. degrees from the University of Bradford and Georgia Institute of Technology, respectively in 1987 and 1977. He has been a visiting Professor at the Ottawa Center of the McLeod Institute for Simulation Sciences at the School of Information Technology and Engineering of the University of Ottawa, respectively in 1993-1994 and 2002- 2003. He has been active in simulation since 1984. His research interests are modeling and simulation, cognitive simulation including simulation of human behaviour by fuzzy agents, agents with dynamic personality and emotions, artificial intelligence, expert systems, fuzzy logic, object-oriented analysis and design, multi-agent systems and their applications. He published more than 140 documents in Journals and Conferences. http://www.eng.ui.ac.ir/~aghaee/ http://www.shbu.ac.ir/aghaee


http://www.eng.ui.ac.ir/%7Eaghaee/

http://www.shbu.ac.ir/aghaee

Documents

Crisis Control Mechanism in Holarchy Architectureaeuso.org/jkbei/wp-content/uploads/2016/06/26-Crisis-Control-Mecha… · domains where goals can be recursively broken down into subtasks