Integrating Mobile Agent with Multi-agent System
for Intelligent Parking Negotiation and Guidance
Wang Longfei, Chen Hong, Li Yang
School of Highway
University of Chang’an
Xi’an, China
[email protected], [email protected], [email protected]
Abstract—In metropolises, parking fees vary widely and
change dynamically in different periods of time and may not
always match the drivers’ expectation price. In order to provide
the consumers a bargaining platform, an intelligent parking
negotiation and guidance system is proposed through integrating
the mobile agent with multi-agent system. The use of mobile
agent allows the dynamic, stable and fast negotiation between
cars and parks. Mobility can reduce negotiation time and data
transmission over the wireless network. A negotiation algorithm
based on human’s bargaining process is also proposed in this
paper. Simulation shows that the predetermined negotiation time
can greatly influence the negotiation times.
Index Terms—parking, mobile agent, multi-agent system,
negotiation, guidance
I.
INTRODUCTION
As parking demand is generally much greater than supply
in metropolises, the parking problem is higher than ever before
and drivers generally need to drive around their destination to
search and wait for available parking spaces. Moreover,
parking fees for temporary parking vary widely and change
dynamically in different periods of time and existing parking
information systems usually do not provide bargaining space
for parking price. Consumers thus lose their bargaining
position to obtain a better and cheaper parking [1]. To solve
this problem, the negotiation mechanism for price bargain can
be adopted. Through negotiation, the drivers can get a low
price and the parks can gain a valid match between supply and
demand.
In recent years, agent technologies have shown great
potential in solving the problems in the dynamic distributed
and complex traffic environment [1], [2], [3], [4], [5] and are
expect to be a key method to establish the automation
mechanism in the parking negotiation and guidance system [1].
The significance of our work consists of providing a bargaining
and guidance platform by exploring multi-agent and mobile
agent technology for developing an intelligent parking
negotiation and guidance system.
Agent has useful characteristics such as autonomy,
reactivity, adaptability, proactivity, and social ability. Such
characteristics are very suitable for problems with highly
dynamic and interactive behaviors. A multi-agent system is a
modeling approach devised to represent systems whose entities,
978-1-4244-2800-7/09/$25.00 ©2009 IEEE
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namely coined agents, exhibit intelligence, autonomy, and
some degree of interaction, both with one another and with the
environment [6]. Mobile agents are able to migrate from one
node in a network to other nodes and to be executed on any
nodes in the network. Mobile agents can be created
dynamically at runtime and dispatched to destination systems
to perform tasks with most updated code and algorithms.
Mobile agent can reduce data transmission over a network and
enhance flexibility, adaptability and stability to the multi-agent
system [7].
In this paper, we have developed a multi-agent system
called agent-based intelligent parking negotiation and guidance
system (ABIPNGS), which integrates mobile agent technology
with multi-agent systems and takes advantages of both
stationary agents and mobile agents. The ABIPNGS facilitates
the search for available parking spaces, negotiation of parking
fees, reservation of parking spaces, and the derivation of
optimal paths from the current location to the intended car park
as well as from the car park to the final destination.
The rest of the paper is organized as follows. Section 2
presents the abstract architecture of ABIPNGS. Section 3
describes the dynamic negotiation algorithms for Park-Agent
and Car-Agent. Section 4 presents the details of algorithm
simulation. Conclusions are drawn in the last section.
II.
OVERVIEW OF ABIPNGS
A. Sytem architecture
The ABIPNGS is designed to assure the following
functionalities: first, to assure the dynamic negotiation between
the car and park, and secondly to guide the car to the proper
routes according to the guidance information generated by the
system. The organization model of ABIPNGS is shown in Fig.
1. The city is divided into a number of parking districts each of
which is equipped with a parking information service center
(PISC) that manages all of the local parking lots. PISC
communicates with local cars and other PISCs through the
wireless network and provides parking services for local and
other districts’ cars which have parking demands.
ABIPNGS is a multi-agent system that supports both
stationary agents and mobile agents. The Car-Agent is the only
mobile Agent in ABIPNGS, which is generated in the Vehicle
Information Communication System (VICS) with all the
ICIEA 2009
necessary parking information and negotiation rules and can
move between PISCs to execute unforeseen negotiation task.
Park-Agent, the park’s agency in the PISC, holds the real-time
parking information, status, price mechanisms and negotiation
rules of the park and negotiates directly with the mobile CarAgents. Park-Agent can also update the parking information of
the real park’s information system with new booking
information resulting from the negotiation. In the PISC,
communication service agent (CS), geographic information
systems agent (GIS), local management agent (LM) and
Negotiation management agent (NM) are used to provide the
necessary negotiation support. CS-Agent maintains the
communication link for the transmission of mobile Car-Agents
and messages between two PISCs. GIS-Agent maintains all the
geographic information and serves as a park address server that
can generate guidance information for the Car-Agent. LMAgent is designed to perform the following tasks: recruiting
new Car-Agents; asking the GIS-Agent for the geographic
information service; dispatching the Car-Agent to the CSAgent; receiving the parking booking information from the CSAgent and send the information to the VICS. The NM-Agent is
responsible for the negotiation management between CarAgents and local Park-Agents. It relies on the service of GISAgent that provides support for finding the relevant parks with
which the mobile Car-Agent will negotiate.
Figure 1. EOrgnization model of ABIPNGS
holds all the Car-Agents which are from other districts’ PISCs,
waiting for negotiations with local Park-Agents.
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B. System operation mechanism
The ABIPNGS operation mechanism is shown in Figure 3
from step 1 to step 17. In step 1, when the driver wish to park
the car, he needs to input the current location, destination,
parking time, Car type and reserve parking price through the
GUI of VICS which will initiate the Car-Agent using those
parameters and dispatch this mobile agent to local PISC. The
Car-Agent will be pushed into the LCAB by LM-Agent. In step
2 and 3, the LM-Agent sends request message to the GISAgent to search for the destination’s PISC to which the CarAgent will route. GIS-Agent will accept the request and sends
the destination PISC’s information back to the LM-Agent.
Then LM-Agent makes a copy of the Car-Agent and sends it to
the local CS-Agent which will transmit the Car-Agent to the
CS-Agent of its destination’s PISC. When the Car-Agent
arrives at its destination, it will be sent to the FCAB, waiting
for the negotiation schedule. During step 7 to 9, the NM-Agent
gets the Car-Agent from the FCAB and sends it to the GISAgent which can list all of the relevant Park-Agents that will
participate in the following negotiation. Negotiation starts at
step 10 by organizing the relevant Park-Agents the Car-Agent.
Later, the NM-Agent receives the negotiation results and
parking information (which park, parking price, parking space)
in step 11 and by the same time the Car-Agent will free itself
locally and all the results will be sent back to the real park’s
information system to update local parking information. During
step 12 to 14, the negotiation results and parking information is
sent back to the car’s local LM-Agent. LM-Agent gets the
guidance information from the GIS-Agent through step 15 and
16. In the final Step 17, LM-Agent sends the parking booking
message and routes guidance message back to the car’s VICS
through the wireless network.
Figure 3. Operation mechanism of ABIPNGS
Figure 2. Architecture of PISC
The architecture of PISC is shown in Fig. 2. Local Car
Agent Base (LCAB) holds all the Car-Agents from the VICS in
local area. Local Park Agent Base (LPAB) holds and manages
all the local Park-Agents. Floating Car Agent Base (FCAB)
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By dividing the city into a number of districts, the stability
of districts’ communications is enhanced. As the parking price
negotiation process is completed in one PISC, the
communication between the car and PISC can be carried out
asynchronously and do not need to maintain long-lasting links,
which works well with wireless communications. Compared
with the direct wireless connection between the car-agent and
park-Agent [1], through using mobile agents, the average
communication traffic and communicating times are reduced to
a great extent, the negotiation speed is improved, the
uncertainty of the transmission process is decreased and the
security and stability of the whole negotiation is finally
strengthened.
III.
NEGOTIATION ALGORITHM
The parking price negotiation simulates human’s
bargaining process. In the negotiation, the Car-agent is the
buyer and the Park-agent is the seller. At the beginning, both
parties will first propose their initial price and in general the
Park-Agent should provide negotiable space for the Car-Agent.
During the negotiation process, the Car-Agent and Park-Agent
respectively increase/decrease the bid based on their initial
bargaining price to make both parties’ bidding converge. The
time-driven bargaining mechanism is employed in the
negotiation process. The increment (Car-Agent) /decrement
value (Park-Agent) is calculated through the multiplication of
the price range and time change rate. The price range is the
variation range of parking price and the Car-Agent and ParkAgent can propose their own price range base on the estimated
value and the other party’s initial price. The time change rate is
the proportion of the current negotiation time to the
predetermined negotiation time. The negotiation algorithm
described here is motivated by literature [1] and can be divided
into Car negotiation algorithm and Park negotiation algorithm.
Notions:
Tcur: current time.
Tstr: the negotiation starts time.
Tend: the negotiation ends time.
Rmov: time change rate.
RP: the reserve price the driver enters through VICS’ GUI.
Pc_min: the Car-Agent estimates the Park-Agent’s
minimum price.
Pc_max: the Car-Agent estimates the Park-Agent’s
maximum price.
Bc_min: the Car-Agent proposes the acceptable minimum
price.
Bc_max: the Car-Agent proposes the acceptable maximum
price.
Bc: the Car-Agent’s bid.
Sc_i: the Car-Agent’s increment value.
Pp_min: the Park-Agent estimates the Car-Agent’s
minimum price.
Pp_max: the Park-Agent estimates the Car-Agent’s
maximum price.
Bp_min: the Park-Agent proposes the acceptable
minimum price.
Bp_max: the Park-Agent proposes the acceptable
maximum price.
Bp: the Park-Agent’s bid.
Sp_d: the Park-Agent’s decrement value
Step 1: Determine the asking price and the increment
value
Send (Parking demand);
Pc_min, Pc_max = Generate (Parking demand)
Bc_min = Min(Pc_min, RP);
Bc_max = Max(Pc_max, RP);
Bc = Bc_min;
Send (CarMessage [Bc_min])
Receive (ParkMessage [Bp_max])
Step 2: Revise asking price slightly
Rmov = (Tcur - Tstr) / (Tend - Tstr)
Sc_i = (Min(Bc_max, Bp_max) - Bc_min)*Rmov
Bc = Bc_min + Sc_i;
Step 3: Conditions judgment
If(Tcur >= Tend or Bc > Bc_max)
Send(CarMessage["Fail"])
If(Bc < Bp)
Send(CarMessage["Propose"])
If(Bc >= Bp)
Send(CarMessage["Success"])
Step 4: Diagnose the message
Receive (ParkMessage[MType])
Consider the type of ParkMessage
"Propose": go to Step2.
"Success": the bid or proposal was acceptable.
"Fail": this ends the negotiation, typically because the
bid was too low and the deadline time has expired.
B. Park negotiation algorithm
The Park-Agent uses Park negotiation algorithm described
as follows to bargain with the Car-Agents:
A. Car negotiation algorithm
The Car-Agent uses Car negotiation algorithm described as
follows to bargain with the Park-Agents:
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Step 1: Determine the asking price and the decrement
value
Receive (Parking demand);
Pp_min, Pp_max = Generate (Parking demand)
Receive (CMessage[Bc_min])
If(Bc_min > Ppmax)
Send (CarMessage["Success"])
End negotiation
Bp_min = Max(Pp_min,Bc_min);
Bp_max = Pp_max;
Bp = Bp_max;
Send (PMessage[Bp_max])
Step 2: Revise asking price slightly
Rmov = (Tcur - Tstr)/(Tend - Tstr)
Sp_d = (Bp_max -Max(Bp_min, Bc_min))*Rmov
Bp = Bp_max - Sp_d;
Step 3: Conditions judgment
If(Tcur >= Tend or Bp > Bp_min)
Send(ParkMessage["Fail"])
If(Bc < Bp)
Send(ParkMessage["Propose"])
If(Bc >= Bp)
Send(ParkMessage["Success"])
Step 4:Diagnose whether algorithm converges
Receive (CarMessage[MType])
Consider the types of ParkMessage
"Propose": go to Step2.
"Success": the bid or proposal was acceptable.
"Fail": this ends the negotiation, typically because the bid
was too low and the deadline time has expired.
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16
15
Negotiation Price
IV.
18
SIMULATION
The park 8 bid conditions have been taken into
consideration. As shown in Figure 4, Dc is the bargaining range
of the Car-Agent, which is from Bc_min to Min(Bc_max,
Bp_max), Dp is the bargaining range of the Park-Agent, which
is from Max(Bp_min, Bc_min) to Bp_max. In condition (1),
the negotiation will fail by the deadline because the car’s bid is
too low. In condition (2), the negotiation can get success by the
deadline. In condition (3) to (6), the negotiation can get success
within the specified negotiation time.
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13
12
11
10
9
8
7
6
1
2
3
4
5
Negotiation times
6
7
8
Figure 5. Negotiation times increasing with predetermined negotiation time
V.
SIMULATION
A new approach employing mobile agents with multiagent system for intelligent parking negotiation and guidance
is introduced in this paper. A negotiation algorithms based on
human’s bargaining process is employed to bargain on parking
prices. Based on mobile agent, the average communication
traffic and communicating times is reduced to a great extent
and the security and stability of the whole negotiation can be
strengthened. The future work includes enhancing capabilities
of ABIPNGS to handle more complex scenarios such as send
several Car-Agent to several PISCs to negotiation when the
car’s destination is on the edge of several districts. Another
important objective is to add more intelligence to the CarAgent and Park-Agent to deal with parking negotiation.
REFERENCES
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Figure 4. 8 bargaining conditions
In order to test the relationship between the predetermined
negotiation time and the algorithm’s convergence speed, {8, 6,
16, 9, 17} is assigned to {RP, Pc_min, Pc_max, Pp_min,
Pp_max} and the predetermined negotiation time is gradually
prolonged to control the negotiation times grow from 3 to 8.
We can infer from figure 5 that under fixed estimated values,
the algorithm’s convergence speed and the actual negotiation
time is constrained by predetermined negotiation time. When
the predetermined negotiation time is long, the actual
negotiation time will be long, the increment value and
decrement value will decrease and the negotiation times will
increase, so the algorithm’s convergence speed will slow down.
[2]
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[4]
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[6]
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