Journal of Robotics, Networking and Artificial Life, Vol. 3, No. 1 (June 2016), 61-64

Moving Robots Lies and Their Minds with Degree of Confidence
in a Decentralized Autonomous FMS
Hidehiko Yamamoto

Department of Mechanical Engineering, Gifu Univ., 1-1, Yanagido
Gifu-shi, Gifu, 501-1193, Japan
Takayoshi Yamada

Department of Mechanical Engineering, Gifu Univ., 1-1, Yanagido
Gifu-shi, Gifu, 501-1193, Japan
Shizuka Tanaka

Mitsubishi Electric Mechatronics Software Corporation, Nagoya, Aichi, Japan
E-mail: yam-h@gifu-u.ac.jp, yamat@gifu-u.ac.jp
www1.gifu-u.ac.jp/~yamlab/

Abstract
According to the previous study of a decentralized autonomous FMS using a mind, it is found that the production
outputs decreased because an agent sometimes lies. To solve this problem, we study an AGV’s lie. The study is thatAGVs recognize the lie that each AGV sends. To do this, we propose to give a degree of confidence to the mind
that each AGV has. By performing the production simulations with the degree of confidence, we improve theincrease of the production outputs.
Keywords: Decentralized Autonomous FMS, Lie, Mind, Degree of Confidence, AGV.

1. Introduction
We’ve developed the computer control systems torealize
adecentralized
autonomous
flexiblemanufacturing system (FMS). To do this, it is necessary
to avoid the path interference of automated guided
vehicles (AGVs) in a decentralized autonomous FMS.
To solve this problem, we inserted an arrogant mind anda modest mind into AGVs to control AGVs’ moving.
In a decentralized autonomous FMS with minds, it
is assumed that the information provided by the agentsis always correct. However, this is not the case in a realfactory, where incorrect information can sometimes besent as a result of machine failures. In this study, wedefine and find this incorrect information as a "lie" of anAGV. Then, in the decentralized autonomous FMS
including AGVs lies, we perform the moving control of
the AGVs in order not to have a low productivity.

Specifically, we propose the method of finding lies
using the AGV’s position information. We also propose
the method of controlling the AGV's moving using adegree of confidence.
2.

Mechanism of Decentralized Autonomous FMS

2.1. Description of Decentralized Autonomous FMS
Fig.1 shows the example of a decentralized autonomous
FMS factory. The factory floor has been divided into agrid pattern where AGV moves on the line. The systemof the automated factory performs the production bycarrying the product to parts of the warehouse andmachining centers (MCs). A decentralized autonomous
FMS does not have a management mechanism that
controls the whole system and the agents corresponding
to the elements of the FMS determine their behaviors by
the knowledge exchanges.

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Hidehiko Yamamoto, Takayoshi Yamada, Shizuka Tanaka

Fig.1. Example of decentralized autonomous FMS

2.2 AGVs’ Behaviors and A Mind ModelIn the decentralized autonomous FMS, all AGVs use theposition information received from the other AGVs anddetermine each behavior autonomously. In order torealize the autonomous behavior, we give a mind toeach AGV. In the present study, in order to perform thebehavior efficiently, we give an arrogant mind or amodest mind to AGVs. AGV with an arrogant mind
takes action approaching the destination forcibly. AGV
with a modest mind takes the action to make way toother AGV.
This mind is expressed in the mind model in Fig.2,
Minimum Unit of Mind (MUM)[1]. A1 and A2 of
MUM are called as unit, X is load, and the arrow is a
stimulation vector. The threshold is determined by theunit. If the internal value reaches the threshold, we callit "excited". And if it does not reach the threshold, wecall it "normal”.
When a signal is sent to the unit, it sends a signal tothe direction of the arrow if it is excited, and it is notsent if it is normal. Load has the function to change theinternal value of the unit. When the signal is sent to theload, the value of the unit is increased by the value of X.
Stimulation vector is a line connecting the load and theunit, and it gives a signal to the load or unit if the signal
comes. When A1 is kept excited, we call it "Modestmind", and when normal, we call it “Arrogant mind”.
By the amount of the signal input to the unit, MUM
frequently changes the mind to an arrogant or a modest.

Next, we describe the internal functions of MUM.
When the arrogant AGV has a path interference, A1 isincreased by 1. Keeping the situation of the interference
and being increased by 1, the A1 value becomes thethreshold, and the AGV is changed to Modest mind.
When AGVs with Modest mind keep making way,the value of A2 is increased by 1. When the situation isrepeated at optional times, A2 becomes excited and asignal is sent to a load. The received load decreases thevalues of units A1 and A2 by optional values. Owing tothis, A1 and A2 are returned to normal and Modest
mind is changed to Arrogant mind. In this way, AGV
avoids the path interferences by the change of mind.
3 How to Find AGV’s Lies
In the decentralized autonomous FMS using the mind,
MUM, from our previous studies, each agent transmits
the information, treats the received information as allcorrect and works in cooperation each other. However,breakdowns such as short-period stops occur in the real
FMS factory. To realize this real factory in this study,
we adopt the decentralized autonomous FMS wherefailure occurs. We define the AGV as “liar” when theAGV sends an incorrect information and "honest" whenthe AGV sends a correct information.
AGV’s lies are defined as the following three types.
1. Although the current position is correctly taken, anAGV sends an incorrect information to other AGVs. We
call this as "USO1".
2. An AGV sometimes takes the current position for adifferent one and sends it. We call this as "USO2".
3. An AGV sometimes fails to read the current positionand does not update the information. We call this as"USO3".
The state of the USO1 is shown in Fig 3. When
USO1 occurs, a correct position information is saved inthe AGV that lied. However, an incorrect information issaved in the other AGVs.

A１

Pathinterference‐X
Keep making way

MUM

A２
Fig.3. USO1

Fig.2. MUM

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Moving Robots Lies andThe state of the USO2 is shown in Fig 4. When
USO2 occurs, both the AGV that lied and other AGVs
save an incorrect information.

Fig.4. USO2

The state of the USO3 is shown in Fig 5. When USO3
occurs, the AGV that lied fails to read the current position.
The AGV does not update the location information anddoes not send the information to other AGVs.

Fig.5. USO3

We find the three methods of the above lies.
[1] The AGVs except for the liar AGV receive theinformation from the liar AGV, compare the liar AGV’scurrent position with its previous positions and find theincorrect information. This is the method to find USO1.
When the information transmitted from the otherAGV satisfies Equation (1), the AGV that has
transmitted the information is defined as USO1.
|(Past X-coordinate)-(Current X-coordinate)|+ |(Past Y-coordinate)-(Current Y-coordinate)| > 5 (1)[2] When the difference between the own current
position and the previous position becomes big, thesituation is called as USO2. When the AGV’s
information satisfies Equation (2), the AGV realizes as
USO2.
|(Past X-coordinate)-(Current X-coordinate)|+ |(Past Y-coordinate)-(Current Y-coordinate)| >10 (2)[3] This is the method to find the USO3. When theAGV’s information satisfies Equation (3), the AGV
realizes as USO3|(Past X-coordinate)-(Current X-coordinate)|+|(Past Y-coordinate)-(Current Y-coordinate)| = 10 (3)4 New Mind with Degree of Confidence
If there are AGVs that lie, an AGV that uses theincorrect information takes the wrong action. As aresult, the number of production is reduced. In thisstudy, we adopt an honest AGV to move preferentiallyto solve this problem. To do this, we find the AGV thatlie by distinguishing honest AGVs from liar AGVs. Inorder to achieve the distinguishing, we propose the newmind model that MUM includes the degree ofconfidence. Because of the new mind model, we cancontrol that the higher confidence AGV preferentiallymoves.
In this study, we define the degree of confidence as"the degree which an AGV can correctly work without alie". When an AGV passes through the intersection
without a lie, the confidence of the mind of the AGV isincreased. The higher the confidence, the more arrogant
the AGV becomes. Because of this, the AGV does thework preferentially.
The way to give confidence is shown as thefollowing.
1. When an AGV is passed through an intersection
without a lie, the mind of confidence is increased by 1.
2. If the confidence reaches a threshold, the thresholdvalue of the unit A1 is increased by 1, and the mind sets
the confidence as 0.
The new mind model of Fig.6. is generated byadding MUM of Fig2 to a unit C, load Y, Z, and thestimulus vector. We call it as MUMS (MUM with Selfconfidence degree). Unit C of MUMS is called degreeof confidence; unit C has a threshold C[T] andexcitement value C[E]. Load Y indicating by thetriangle of Fig.6 sends a signal to the threshold A1[T].
Load -Z sends a signal to the excitement value C[E] ofthe unit C.
Further, the functions of the MUMS are shown
below. When an AGV passes through the intersection
A2[E]Path interference

A2[T]

-X
A1[E]

Stay
awaygivingfrom the destination
Keepa way

A1[T]

MUMS

Y
C[E]

PassGothroughtheanintersection
intersection
Fig.6

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C[T]

MUMS

-Z
Degree ofconfidenceHidehiko Yamamoto, Takayoshi Yamada, Shizuka Tanaka

without a lie, a signal is sent to C[E]. In other words, thedegree of confidence is increased. When C[E] continues

to receive the signal, the unit C becomes excited andsends a signal to the load Y and load -Z. The load Y
increases A1[T] and C[E] is reduced by the load -Z. In
this way, when an AGV passes through the intersection
without a lie, A1[T] is increased. When A1 [T]increases, the unit A1 is hard to become the excitedcondition, that is, it is easy to have an arrogant mind. On
the other hand, when an AGV passes through theintersection with a lie, the signal is not sent to the unit C.
Because of it, A1[T] is not changed.
5. Simulation Results and Considerations

We simulated the production results of FMS including
MUMS. We set AGVs lie with a certain probability andcarried out the production simulations by using thefollowing four conditions.
・FMS just using honest AGVs
・FMS including AGVs that lie
・FMS including AGVs that lie, and the AGVs with MUMS
(Set the threshold value C [T] shown in Fig.6 as 500)
・FMS including AGVs that lie, and the AGVs with MUMS
(Set the threshold value C [T] shown in Fig.6 as 900)
The production simulations were carried out seventimes under these conditions. Table.1 shows the averageof the output of simulation production, the total numberof collisions and the efficiency of each AGV. Table.2
also shows the adopted stimulus values of the degrees ofconfidence of AGVs.
From Table.1, comparing the honest FMS with theFMS including liar AGVs, the latter FMS reached to aTable.1. Simulation Results

EfficiencyOutputs
Collision
NumberAGV1
AGV2
AGV3AGV4AGV5Degree ofDegree ofConfidence Confidence

HonestAGVs

liarAGVs

158.7

133.7(500)138.4(900)140.402159.92385.12236.7

0.591
0.901
0.847
0.686
0.774

0.414
0.716
0.646
0.506
0.559

0.408
0.707
0.622
0.495
0.551

0.424
0.718
0.642
0.507
0.569

Table.2. Stimulus values of the degree of confidence
C[T]=500
C[T]=900

AGV1

AGV2

AGV3

AGV4

AGV520116948694

bad production results, 25 products lower than theformer FMS. Comparing the latter FMS with the FMS
including AGVs with MUMS, the number of production
for the FMS with MUMS was increased. The number ofproduction for each condition, 500 and 900, wasincreased. The number of production with MUMS wasclose to the number of production with the honest FMS.
Because AGVs that have the characteristics easy to
become arrogant and modest by the confidence, the timerequired path interference avoidances was reduced.
In addition, comparing 500 with 900, the number ofproduction for 900 is two more than 500. Table.2
shows that the case of C[T]=500 gave a lot ofstimulations to the mind of AGVs. If C[T] is too small,many signals are sent to A1[T] and A1[T] becomes toolarge. The bigger A1[T] is, the longer the time that thechange of mind starts is. As a result, the time toperform the judgment of the path interference avoidance
becomes longer. Because of this, the magnitude of C[T]affects the ease of the change of mind and the numberof production.
However, even using the degree of confidence, it
was not possible to reduce the total interference number.
This is because the interference occurs together with thelie. Even if using the degree of confidence, it is notpossible to reduce the number of the lie itself.
6. Conclusions
This study found how to find the lie and to decide thedegree of confidence useful for improving theproduction number in a decentralized autonomous FMS
including AGVs that lie. This study also found that themagnitude of C[T] affects the ease of the change ofmind and the number of production. We can establish
the foundation of the way how to handle the lie.
In this study, the threshold value of the degree ofconfidence was determined in accordance with a certain
probability of the lie happening. In the actual FMS
factory, however, the agents that lie are not only AGVs
but Machining Centers. We will study MCs lies in thefuture to realize the real FMS.
References
1. Hidehiko YAMAMOTO, Takayoshi YAMADA andShinsuke KATO, AGV Mind Model and its Usage for
Decentralized Autonomous FMS by Change of Mind,
Proceeding of Third KES International Symposium, KESAMSTA 2009, Agent and Multi- Agent Systems :Technologies and Applications, Lecture Notes in Artificial
Intelligence, Edited by Anne Hakansson, Ngoc Thanh
Nguyen etc. , ISSN 0302-9743, Uppsala, Sweden, pp.744753, June(2009), Springer.

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