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Intelligent Automation and Soft Computing, Vol. 10, No. 4, pp. 337-348, 2004 Copyright © 2004, TSI® Press Printed in the USA. All rights reserved

AN INTELLIGENT DISCRETE EVENT APPROACH TO MODELING , SIMULATION AND CONTROL OF AUTONOMOUS AGENTS SHAHAB SHEIKH-BAHAEI*, JINGYU LIU, AND MO JAMSHIDI Autonomous Control Engineering (ACE) Center University of New Mexico, 87131 Albuquerque, NM USA

PAOLO LINO Dept. of El. Eng. Tech. Univ. of Bari 72015 Bari, Italy

ABSTRACT—In this paper we investigate an intelligent discrete event method suitable for modeling, simulation and control of hybrid systems. More in detail, a Fuzzy logic approach based on Discrete Event System Specification (DEVS), which is a main part of Intelligent-DEVS (I-DEVS), is emphasized in the present work. We show that the resulting Discrete Event Fuzzy Logic (DEFuL) is faster than conventional implementations. A case study is set up, which involves some robots that cooperate to complete a common task, illustrating how the above concepts can be fruitfully applied. Key Words: DEVS, I-DEVS, V-Lab, Hybrid Systems, Fuzzy Logic, Modeling, Simulation, Discrete Event

1. INTRODUCTION Work on mathematical establishment of discrete event dynamic modeling and simulation initiated in the 70s by Zeigler, when DEVS (discrete event system specification) was introduced as an abstract formalism for discrete event modeling [1]. DEVS is a universal formalism for discrete event dynamical system (DEDS), which can be used to specify systems whose input, state and output trajectories are piecewise constant [1]. Recently, the Control of Discrete Event Systems is receiving ever more attention [2]. Discrete-Event Systems (DES) control theory developed by Ramadge and Wonham, has been used to control several different robotic problems including manufacturing and assembly tasks, the coordination of mobile robotic agents [3],[4], a grasping task performed under the supervision of a vision system[5], modeling dexterous manipulation [6] a hybrid discrete event dynamic systems approach to robot control [7],[8]. On the other hand, soft computing techniques, e.g. fuzzy logic, genetic algorithms and neural networks are proven to be useful to control of autonomous agents. Fuzzy logic control provides a human-like decision making methodology, which has been used widely in the field of robotics and automation [9]. In this paper we propose an Intelligent Discrete Event (I-DEVS) approach to control autonomous agents, i.e. we combine Fuzzy Logic Control with DEVS. The purpose of this research is to investigate an intelligent discrete events system approach to modeling, simulation and control design. The rest of the paper is organized as follows. In Section 2, we will review the background information of modeling and controllability of discrete event systems. In Section 3, discrete event control, fuzzy package and contributions to IDEVS are presented. In Section 4,

*

Corresponding author: [email protected]

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we will define the case study and describe IDEVS implementation1. Simulation and experimental results are presented in Section 5. In Section 6 some conclusions are given.

2. DISCRETE EVENT MODELING VIA DEVS Discrete Event Modeling has its origin in job shop scheduling simulations in operations research. DEVS is claimed to be able to represent any causal dynamical system, which has piecewise constant input and output segments [1]. This class of systems are called DEVS-Representable[10]. Differential Equation Specified Systems (DESS) are usually used to represent the plant in hybrid systems (mixed discrete-continuous) and are controlled by high-level, symbolic, event-driven control schemes. Zeigler [11] showed that these kinds of plants are representable by DEVS since they have piecewise constant input and output trajectories when viewed within the frame of their sensing/actuator interface. Likewise, the controller has natural DEVS representation. An atomic DEVS is a structure [1] DEVS=(X, Y, S, δext, δint, λ, ta) where is the set of inputs, X is the set of outputs, Y S is the set of sequential states, δext : Q×S×X →S is the external state transition function, where Q={e | 0 ≤ e < sigma }, e is the elapsed time since last event. sigma is time to the next internal transition. δint : S → S is the internal state transition function, λ : S →Y is the output function, ta : S → ℜ+∪{0,∞} is the time advance function. Atomic models compose the functionality of the basic units in a DEVS model. Using these atomic models as building blocks, coupled models build up the simulation by linking them together. In addition to containing atomic models, coupled models may also be used as building blocks in other coupled models. Simulations using DEVS are collections of models composed in a Outputs Inputs hierarchical fashion. Coupled models are formalized in the DEVS formalism as well [1]. Figure 1 shows a graphical Figure 1. Graphical notation for atomic/coupled models. notation of an atomic/coupled model. Zeigler [11] demonstrates how the DEVS representation applies to special subclasses of dynamical systems – those whose state dynamics is defined by a set of first order differential equations and whose input and output trajectories are piecewise constant – introducing DEV&DESS. DEV&DESS is an extension of DEVS formalism for combined discrete/continuous modeling [11, 1, 12, 13]. DEV&DESS, which includes both DEVS and DESS classes of system, offers a powerful modeling formalism for simulating hybrid systems. In this paper, we use the same graphical notation as Zeigler’s, shown in figure 2, where (a) represents an external transition. An input event is specified using ‘?’. For instance input event in?m means that a message ‘m’ is input at the input port “in”. Figure 2(b) denotes an internal transition. An output event is specified using ‘!’. A dotted line represents an internal state transition specified by the internal transition function. A solid line represents a state transition specified by the external transition function. Several software tools have been developed for the simulation of DEVS models. The most popular are DEVS-Java [14] and DEVSim++[15]. In this work we use DEVS-Java to implement our DEVS models.

1 It should be mentioned here that, although the case study is on cooperative-object-pushing task, the intention of this paper is not solving such a problem.

An Intelligent Discrete Event Approach to Modeling, Simulation and Control of Autonomous Agents

si

p?m

sj

si

(a)

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sj

p!m (b)

Figure 2. Graphical notation: (a) external transition, and (b) internal transition.

2.2 Controllability Definitions for DEVS Models The concept of controllability is formulated to show whether or not a discrete event controller exists that can implement a desired state trajectory [11]. Given a discrete event plant M=(X, Y, S,δext,δint,λ,ta), a path is a sequence of elements of S. Definition 1 (weak controllability) A path ST is weakly controllable if a series of internal and external transitions can take each state on the path to the next state on the path. See [11] for a more formal definition. Definition 2 (strong controllability) A path is strongly controllable if either an internal or external transition can take each state on the path to the next state on the path. A more formal definition is presented in [11]. The states on a strongly controllable path never turn away from the desired path. Definition 3 (very strongly controllability) A strongly controllable path ST is a very strongly controllable if: For all si, si+1 such that si+1 follows si in ST, either: a) δint(si) = si+1, or b) there is an input x=p?m such that δext(si,e,x)=si+1 for all 0 < e < ta(si). Figure 3 illustrates the above three definitions. Filled circles represent states on the desired path, ST=( s0 , s1 , … , si , si+1 , … , sf ). Thick lines denote internal or external transitions that eventually lead one state on ST to the next. δint(si) S0

Si

weak Si+1

Si+1

Sf

S0

δext(si,e,x)

weak

(a)

Sk

(b)

Sf

Si+1 S0 ta(s0) = ∞

ta = ∞

Sf

ta(si) = ∞

(c)

Figure 3. Controllability Definitions: (a) weakly controllable (b) strongly controllable (c) very strongly controllable path.

2.2 Discrete Event Control (DEC) Ramadge and Wonham [2] were the first control theorists to develop an approach to design discrete event controllers. They modeled the plant as an automaton and used language theory to design a controller that forces the plant to perform the desired behavior. In order to connect means of control to the system, they classified events into two categories: uncontrollable events, which can be observed, but cannot be prevented from occurring (e.g. obstacle detected) and controllable events that can be enforced to or prevented from occurring (e.g. stop or move) [2]. In this framework a system is said to be controllable when you can reach any desired state of the system, knowing the current state. Consequently, a controller (supervisor) exists if the system is controllable. Zeigler [11] reformulated the Ramadge-Wonham approach to controller design within the DEVS formalism. The resulting methodology is clamed to be both more general and more intuitively appealing than the original. Since it is universal for general piecewise constant systems, DEVS, can represent timing information about the plant that avoids automaton formalism [11].

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A methodology for design of a discrete event controller based on the DEVS formalism is shown in [11]. Zeigler showed that if the plant is strongly controllable, then the desired path could be mapped to a DEC using a transformation called “Inverse-DEVS” (see [11] for details).

2.3 Intelligent DEVS (IDEVS) Control IDEVS has been introduced in [16], to make connection between soft computing tools and discrete event system specifications (DEVS). In this section we focus on Discrete Event Fuzzy Logic (FuzzyDEVS), which can be used as a discrete event controller on a hybrid (discrete/continuous) DEVS plant2. A fuzzy logic controller consists of three operations: (1) fuzzification, (2) inference engine, and (3) defuzzification. The input sensory (crisp or numerical) data are fed into fuzzy logic rule based system where physical quantities are represented into linguistic variables with appropriate membership functions. These linguistic variables are then used in the antecedents (IF-Part) of a set of fuzzy “IF-THEN” rules within an inference engine to result in a new set of fuzzy linguistic variables or consequent (THEN-Part) [9] A typical Mamdani rule can be composed as follows

IF x1 is A1i AND x2 is A2i THEN y i is B i , for i = 1, 2,..., l

(1)

where A1i and A2i are the fuzzy sets representing the ith-antecedent pairs, and B i are the fuzzy sets representing the ith-consequent, and l is the number of rules. We define DEVS-Fuzzifier as follows: A DEVS-Fuzzifier is an atomic DEVS model: AMF =(X, Y, S, δext, δint, λ, ta, µ) where The input set, X, a set of real values. The output set Y = [0,1], since the output of this atomic model is a fuzzy value. S is a sequence of fuzzified input values: S={si | si = δext(q,si-1,x) }. δext(q,s,x)=µ(x), where µ is the membership function associated with this fuzzifier. δint(s) and λ(s) are the identity functions. ta (s) =0 , since there is no time advance in this model. µ(x) is the membership function associated with this fuzzifier. This atomic model represents an antecedent membership function. A consequent membership function can be defined as: CMF=(X, Y, S, δext, δint, λ, ta, µ) where X is a set of fuzzy values. X=[0,1] The output of this model is a fuzzy set, so Y is a set of fuzzy sets. Y = {Σ | Σ is a fuzzy set} S is a sequence of discrete fuzzy sets (membership functions). S={…, Σi-1, Σi, Σi+1,…} δext(q,s,x)= min( µ (ai ), x ) , where µ is the membership function associated with this model. This function in fact cuts ∑ ai those values of the fuzzy set Σµ(ai)/ai where their membership values are greater than x (see Figure 4). δint(s)=s, λ(s)=s and ta=0.

X

µ

Y

Figure 4. A typical consequent membership function (CMF) model.

2

Zeigler [1] has introduced a Fuzzy-DEVS formalism for modeling, whereas ours is for control.


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In a typical fuzzy rule connectives are used to make connection between antecedent pairs. An “AND” connective takes the minimum, while an “OR” connective returns the maximum value of two pairs [9]. Connectives are defined in DEVS as atomic models: CNTV=(X1, X2, …, Xn, Y, S, δext, δint, λ, ta, µ) where X1=…= Xn =[0,1] are input sets. (A connective can have n inputs.), Y = S = [0,1], δint(s) = s, λ(s) = s, ta = 0, δext(q,s,x1,…xn) = min(x1,…,xn), in case of “AND” and, δext(q,s,x1,…xn) = max(x1,…,xn), in case of “OR”. A fuzzy rule can be composed as a coupled model using the above three atomic models. For instance, consider the following fuzzy rule: “IF x is A OR x is B THEN z is C”. Figure 5 shows how this fuzzy rule can be composed using two Fuzzifiers, one Connective and one CMF (consequent membership function). x

A OR

y

B

C

z

CMF

FZ Figure 5. A DEVS model for a typical fuzzy rule “IF x is A OR y is B THEN z is C”.

In order to fully implement a Fuzzy Logic Controller, a Defuzzifier is needed, which can be defined in the same way. i.e. takes fuzzy sets as input and calculates the defuzzified value (see [9] for further details about defuzzification methods). DeFZ==(X1, X2, …, Xn, Y, S, δext, δint, λ, ta) where Inputs of this atomic model are fuzzy sets so X1=X2=…=Xn ={x | x is a fuzzy set} Output is a real number, Y = ℜ, S = ℜ, δint(s) = s, λ(s) = s, ta = 0, δext(q,s,x1,…xn) = Center_of_Gravity (x1 ∪ x2 ∪ … xn ) Center_of_Gravity(x) is a function which calculates the defuzzified value of fuzzy set x using center of gravity method [5, page 193]: Center_of_Gravity (x) =

∫ xµ ( x)dx ∫ µ ( x)dx

Note that the time advance function (ta) is zero for all the above models, since we don’t want to have any delay time in any of the models. Fuzzy-DEVS, i.e. the discrete event implementation of Fuzzy Logic Conrol, runs faster than conventional methods. The reason is that the three operations of the fuzzy system in Fuzzy-DEVS are activated only when there is an input event. In other words the DEVS-Fuzzifiers (see Figure 5) are activated only when they receive a new input event. Consecutively, they only generate output events if the new fuzzified value is different from the previos one. As a result the connective models are activated only when there is a change in their inputs, and the story continues. This kind of behavior saves a lot of unnecessary calculations in a large system. However, for small number of rules the performance is not significantly improved due to change-detection calculations performed in Fuzzy-DEVS.

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In practice, Fuzzy parameters can be tuned in two ways: first, by means of adding extra input ports to the DEVS models and sending the parameters to them via input ports. Second, directly changing the parameters internally, or having parameters accessible from other models. The first method makes the Fuzzy-DEVS tunable by connecting it to other DEVS models, such as GADEVS and NN-DEVS, while the second method can be used to tune the parameters by conventional tuning algorithms such as GA and NN.

3. CASE STUDY In order to test the effectiveness of I-DEVS and in particular Fuzzy-DEVS, as an appropriate tool for modeling, simulation and control of complex systems, we present a case study showing how it is possible to apply the concepts described in above sections. Using V-Lab3, we build a virtual environment including a certain number of rovers, which have the task of taking some objects to a common storage area. In solving this problem, we adopt a simplified hierarchical control structure for suitable distribution of single objectives, allowing us to achieve the final goal in the easiest and fastest way. In particular, a supervising station is responsible for properly allocate robots by taking into account objects dimensions and possible failures, while robots decide for themselves how to push objects showing a human inspired behavior, which can be easily expressed by a fuzzy inference system. Since our purpose is to show how concepts on the basis of I-DEVS work, we formulate a simplified description of the problem by making the following assumptions: • The supervisory system has knowledge of initial position of objects to be collected and can communicate it to each robot; this could be obtained by a previous exploration of the environment. • Robots are non-holonomic rovers; besides they know their position and orientation and are supplied by sensors that permit them to determine their relative position with respect to the objects; • The supervisory station can communicate with robots getting information on their position and task accomplishment level; it can eventually receive help request from robots; • Objects have circular geometry and the center of gravity position is known. The first task is to properly distribute the robots for each object to be collected. At the beginning of the procedures one rover for each object is assigned, provided that the number of robots is enough; otherwise, in order to avoid path obstructions, the supervisory system chooses to collect the closest objects to the storage area, first. Once the assignment is made, robots approach and then position themselves for pushing objects towards the final goal, and finally they start to push along the normal direction to the object surface. Consider two mobile robots are assigned to push a circular obstacle at initial position B to goal position G (Figure 6). First, robots should place itself properly with respect to the obstacle in order to push it toward the goal. These desired positions are denoted by A1 and A2 in Figure 6, where rA and rB are radiuses of the robots and obstacle, respectively. After reaching points A1 and A2, robots will slowly start to push the obstacle. In order to properly push the obstacle toward the goal, robots should always remain at the opposite side of obstacle with respect to goal. To ensure this, robots should remain inside the pushing regions shown in Figure 6. The boundaries of

JG

JG

these regions are the lines going through the center of the obstacle with norms wl and wr . After accomplishing this task, the rover communicates the station that it is available for the next mission. If one rover is partially or not able to fulfill its job, due to the object weight, the supervisory station can detect this condition, evaluate the task difficulty level and eventually choose to call another rover to help, although it is still engaged. In order to reach the maximum efficiency, the rovers will try to push the object in the same direction; if it isn’t possible, for the trajectory error computation we consider the direction of the sum of rovers speeds (Figure 6), so that each rover doesn’t need the knowledge of the other rover’s action.

3

See [16],[17] for details about V-Lab®


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JG G

JG1 JG rA1 wl A 1 JG1 wr

JG B rB

JG r A2

A2

JG 2 wl

Pushing regions

JG 2 wr

Figure 6. Pushing task by two robots.

If one rover is not able to fulfill its job, due to the object weight, the supervisory station can detect this condition, via evaluating robots jobs. Eventually, the supervisor can call another rover to help. Figure 7 shows a DEVS model to perform the pushing task, where states are defined as:

“in”

“in”?object_reached

“out”

“in”?command “in”?object_lost “out”!goal_reached “out”?object_touched “in”?outside_pushing_region “out”?object_reached_the_goal

“in”?everybody_ready

Figure 7. A DEVS model for path-planning and cooperative pushing task.

• •

• • •

Standby: is the initial state. Robot is waiting for supervisor to send an order. Path Planning: this state indicates that robot is trying to go from its initial position to the goal position, avoiding other obstacles and rovers. After reaching the goal, the robot changes its state to either Standby or Placement, depending on the type of command received from supervisor (i.e. if a pushing command is received it changes to Placement state, otherwise goes to Standby state. Placement: this state indicates that the robot is placing itself inside the pushing region touching the object (see Figure 6). Ready_to_push: in this state, robot is ready to push the object, waiting for other robots to touch the object and be ready to push. Pushing: after supervisor confirmed that all other robots are in the state of “ready_to_push”, rover goes to pushing state, trying to push the object toward the goal.

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Zero

Pos

error If error is Zero Then w is Zero If error is Pos Then w is Pos If error is Neg Then w is Neg

error

Figure 8. A Fuzzy Logic Controller to keep the robot inside its pushing region.

A desired path can be considered as: {Standby, Path_planning, Ready_to_push, Pushing, Standby}, which according to our assumptions, this path is weakly controllable 4(since unwanted events such as going outside of pushing region can turn the system away from its desired path), therefore it can’t be controlled using an inverse-DEVS 5. A fuzzy logic controller is designed to keep the robot inside its pushing region, controlling robot’s angular velocity w, as shown in Figure 8. The forward velocity v increases from zero to vmax during the pushing situation, to avoid striking the object. Obstacle avoidance is also performed by fuzzy logic, Figure 9.

b) Angle of sensor (th)

a) Distance measured by

c) Forward velocity (V) "IF distance is Close And angle is Pos "IF distance is Close And angle is Zero "IF distance is Close And angle is Neg "IF distance is Near And angle is Pos "IF distance is Near And angle is Zero "IF distance is Near And angle is Neg "IF distance is Far And angle is Pos "IF distance is Far And angle is Zero "IF distance is Far And angle is Neg

d) Angular Velocity (W) Then V is VSlow W Is Then V Is Stop " Then V Is VSlow W Is Then V Is Slow W Is Then V Is VSlow W Is Then V Is Slow W Is Then V Is Fast W Is Then V Is Fast W Is Then V Is Fast W Is

Neg " Pos " SNeg" Sneg" Spos" Zero" Zero" Zero"

e) Fuzzy rules Figure 9. A Fuzzy Logic Controller to perform obstacle avoidance behaviour.

4

5

See Section 2.

See [11] for details on inverse-DEVS.


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4. SIMULATION AND EXPERIMENT RESULTS In this section we present the simulation and real world experiment results. The algorithms described in Section 4 are implemented on a Pioneer AT2 robot from ActivMedia Robotics™. The robot has two independent motors and four wheels. The two left wheels are driven by one motor and two right wheels by the other motor. Figure 10 shows the simulation results for the cooperative object pushing task carried out by two robots, implemented in V-Lab® [15] and DEVS-Java[14].

goal

×

×

×

×

×

×

×

Figure 10. Cooperative object pushing.

Figure 11 illustrates the results. The robot is assigned to push the object located at point (7,4) to the location (4,8), starting from initial position (2,2, π/2).

Y

x (a)

(b)

Figure 11. Experiment results: a) V-Lab® real time simulation b) Real robot pushing the object.

In the algorithm it is assumed that the position of the object is known continually, however in reality this is not the case. To solve this problem, another DEVS model called “objectLocationEstimator” has been used, which estimates the position of the nearest object to the robot, using sensory information. Figure 12 compares the amount of activities (calculations) done at each time-step by conventional Fuzzy and Discrete Event Fuzzy. As shown in the figure the amount of activities in a discrete event fuzzy is much less than non-discrete-event methods. However, for small number of rules the performance is not significantly improved due to change-detection calculations performed in Fuzzy-DEVS.

5. CONCLUSION In this paper we presented a way to enhance Fuzzy Logic Control by means of Discrete Event tools. In particular, we showed how it is possible to incorporate fuzzy reasoning methods in the DEVS formalism obtaining what we call Intelligent-DEVS (I-DEVS). The resulting Discrete-Event Fuzzy-Logic (DEFuL) is faster than conventional algorithms, when a large number of rules and membership functions are employed. In order to show the ability of Fuzzy-DEVS in controlling real-time systems, we employed this method in a case study that involved a certain number of robots to be coordinated in order to achieve the

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Activities

Activities

Time

Time

(a)

(b)

Figure 12. Conventional Fuzzy Logic (a) vs. Discrete Event Fuzzy Logic (b).

common task of pushing some objects toward a storage area; we explained how to represent and solve this problem in the DEVS formalism. Fuzzy-DEVS was successfully used to perform the pushing task and obstacle avoidance in simulation for multi-robots and a real world robot.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

B. P. Zeigler, H. Preahofer, and T.G. Kim, Theory of Modeling and Simulation, 2nd edition. Academic Press, San Diego, CA, 2000 P.J.G. Ramadge and W. M. Wonham. The control of discrete event systems, Proceedings of the IEEE, 77(1):81-98, 1989. B. A Brandin, W. M. Wonham, and B. Benhabib. Manufacturing cell supervisory control – a modular timed discrete-event system approach, Proceedings of the IEEE International Conference on Robotics and Automation, 846-851, 1993. B. J. McCarragher and H. Asada. A discrete event approach to the control of robotic assembly tasks, proceedings of the IEEE International Conference on Robotics and Automation, 1993. T. M. Sobh and R. Bajcy, Autonomous observation under uncertainty, Proceedings of the IEEE International Conference on Robotics and Automation, 1992. S. L. Ricker, N. Sarkar, and K. Rudie, A Discrete-Event Systems Approach to Modeling Dexterous Manipulation. External Technical Report, Queen’s University, 1995 M. Huber and R. A. Grupen, A Hybrid Discrete Event Dynamic Systems Approach to Robot Control. Technical Rebort #96-43, Department of Computer Science, University of Massachusetts, 1996. J. Kosecka and R. Bajcsy, Discrete Event Systems for Autonomous Mobile Agents. Dep. of Computer and Information Science, University of Pennsylvania. Available online at http://citeseer.nj.nec.com/kosecka93discrete.html M. Jamshidi and A. Zilouchian, Intelligent Control Systems using Soft Computing Methodologies, CRC Press, Boca Raton, FL, 2001. B. P Zeigler and W. H. Sanders, Preface to special issue on environments for discrete event dynamic systems, Discrete Event Dynamic Systems: Theory and Application, 3(2):110-119, 1993. B. P. Zeigler, H. S. Song, T. G. Kim, and H. Preahofer. DEVS Framework for Modeling, Simulation, Analysis, and Design of Hybrid Systems. Available online at http://citeseer.nj.nec.com/ zeigler95devs.html H. Preahofer, System Theoretic Foundations for Combined Discrete-Continuous System Simulation. Ph.D. thesis, Johannes Kepler University of Linz, Linz, Austria, 1991. Fr. Pichler and H. Schwaertzel, editors, CAST Methods in Modeling, chapter 3, 123-241. SpringerVerlag, 1992. B. Zeigler and H. Sarjoughian, Introduction to DEVS Modeling and Simulation with JAVA: A Simplifed Approach to HLA-Compliant Distributed Simulations. Arizona Center for Integrative Modeling and Simulation. Available at http://www.acims.arizona.edu/.


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[15] T. G. Kim. DEVSim++ User’s Manual. C++ Based Simulation with Hierarchical Modular DEVS Models. Korea Advance Institute of Science and Technology, 1994. Available at http://www.acims.arizona.edu/. [16] M. Jamshidi, S. Sheikh-Bahaei, J. Kitzinger, P. Sridhar, S. Xia, Y. Wang, L. Liu, E. Tunstel, Jr., M. Akbarzadeh, A. El-Osery, M. Fathi, X. Hu, and B.P. Zeigler, “A Distributed Intelligent DiscreteEvent Environment for Autonomous Agents Simulation”, Chapter 11, “Applied Simulation”, Kluwer Publications 2003. [17] A. El-Osery, J. Burge, M. Jamshidi, A. Saha, M. Fathi, and M. Akbarzadeh-T. “V-Lab – A Distributed Simulation and Modeling Environment for Robotic Agents – SLA-Based Learning Controllers,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 32, No. 6, pp. 791-803, 2002

ABOUT THE AUTHORS S. SHEIKH-BAHAEI received his B.S. degree in electrical engineering from Isfahan University of Technology, Iran in 1999 and his M.S. degree in electrical engineering from University of New Mexico, Albuquerque, NM, USA in 2003. From May 2002 till December 2002, he was a research assistant at the Autonomous Control Engineering (ACE) Center at the University of New Mexico. Shahab’s research interests are in the areas of mobile robotics, control systems, discrete event modeling and simulation, and soft computing.

J. LIU received her B.S., M.S. degrees in electrical engineering from Northern Jiaotong University, China in 1994 and 1997, respectively. She worked as a teacher in Department of Material Engineering in Northern Jiaotong University from 1997 to 2000. She was a Ph.D.. student of electrical and computer engineering department and the ACE Center at the University of New Mexico. She received her Ph.D. from the University of New Mexico in August 2004. Her research interests are system identification and soft computing algorithms applied to control scenarios as well as biomedical dynamics. M. JAMSHIDI (Fellow IEEE, Fellow ASME, Fellow AAAS, Fellow TWAS, Fellow HAE, Fellow NYAS) received the BSEE from Oregon State University in June 1967 and the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign in February 1971. He holds an honorary doctorate degree from Azerbaijan National University, Baku, Azerbaijan, 1999 and an honorary doctor of engineering for the University of Waterloo, Canada, 2004. Currently, he is the Regents Professor of Electrical and Computer Engineering, the AT&T Professor of Manufacturing Engineering and founding Director of Center for Autonomous Control Engineering - ACE (http://ace.unm.edu) Center and was a co-founder of the PURSUE Program (http://pursue.unm.edu) at the University of New Mexico, Albuquerque, NM, USA. His work on optimal control and optimization of large-scale systems has had a global reach since the early 1980’s with the first edition of his seminal book on Large-Scale Systems – Modeling and Control in New York. This book and its 1997 edition have been translated into 5 languages and have been adopted in 55 nations. He was a member of the NASA Minority Businesses Resource Advisory Committee (MBRAC), he is a Senior Research Advisor at US Air Force Research Laboratory, KAFB, NM and was a Consultant to Department of Energy’s Office of Industrial Technologies on effects of robotic automation on 10 “Industries of the Future”. He is also on the USA National Academy of Sciences NRC's pre-doctoral Fellowship Board for

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Ford Foundation. He was a member of the NASA JPL Surface Systems Track Review Board. He was a member of the advisory committee of NASA JPL's MARS Pathfinder Project mission which landed on July 4, 1997. He was on the USA National Academy of Sciences NRC's Integrated Manufacturing Review Board for DOE. Previously he spent 6 years at the US Air Force Research (Weapons or Phillips) Laboratory working on large-scale systems, control of photonics systems and adaptive optics. He has been a consultant with Department of Energy’s Los Alamos National Laboratory, Sandia National Laboratories and Oak Ridge National Laboratory. He has worked in various academic and industrial positions at many national and international locations including with IBM and GM Corporations. He has been advisor to 33+ Ph.D. and 42+ M.S. students since 1984. As ACE Center Director, he has lead a large academic team of researchers and educators which has resulted in over 75 MS (65 are ethnic minorities) and 23 Ph.D. (18 are ethnic minorities) graduates in engineering. In 1999 he was a NATO Distinguished Professor of autonomous control of complex systems assigned for a short 3-weeks educational tour in Portugal. He has over 525 technical publications including 53 books and edited volumes. Six of his books have been translated into at least one foreign language. He is the Founding Editor or Co-Founding Editor or Editorin-Chief of 5 journals (including Elsevier's International Journal of Computers and Electrical Engineering 1989-Present), IOS Press’s Intelligent and Fuzzy Systems (1992-2003), and TSI Press’s Intelligent Automation and Soft Computing, 2002-present, and one magazine (IEEE Control Systems Magazine, 19801984). He also has been on the executive editorial boards of a number of journals and two encyclopedias. In 1986 he helped launch a specialized Symposium on robotics, which was expanded to International Symposium on Robotics and Manufacturing (ISRAM) in 1988. In 1994 it was expanded into World Automation Congress (WAC, http://wacong.com) where it now encompasses four symposia and one forum on Robotics, Manufacturing, Automation, Control, Soft Computing, Multimedia, Financial Engineering, Bio-medicine, and Image Processing. He has been the General Chairman of WAC from its inception. He is a co-founder of TSI Enterprise, Inc., a 25-year old NM Corporation specializing in control technologies for complex systems including energy systems, water treatment systems and space robotics, educational tools and publishing. Dr. Jamshidi is a Fellow of the IEEE, a Fellow of the ASME, Fellow of the American Association for the Advancement of Science (AAAS), an Associate Fellow of Third World Academy of Sciences (Trieste, Italy), Member of Russian Academy of Nonlinear Sciences, Honorary Fellow, Hungarian Academy of Engineering, a fellow of the New York Academy of Sciences and recipient of the IEEE Centennial Medal and IEEE Control Systems Society Distinguished Member Award and the recipient of the IEEE Control Systems Society Millennium Pin. Currently, he is a Vice-President and member of the Board of Governors of the IEEE Systems, Man and Cybernetics Society. He is an Honorary Professor at three Chinese Universities and is on the Board of Nobel Laureate Glenn T. Seaborg Hall of Science for Native American Youth in the Navajo Nation.

P. LINO received the M.S. degree in electrical engineering from the Polytechnic of Bari, Italy, in 2000. He is currently pursuing the Ph.D. degree within University of Catania with cooperation of Polytecnic of Bari. In 2003 he was a visting scholar at the ACE Center of University of New Mexico, USA. His main research interests are in the field of intelligent control and automotive systems.