Negotiating the value of gas price

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Negotiating the value of gas price

• By Hector M Lugo-Cordero, MS
• Saad A Khan, MS
• EEL 6788

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Agenda

• Problem statement
• Challenges
• Design
• Evaluation
• Conclusions

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Agenda

• Problem statement
• Challenges
• Design
• Evaluation
• Conclusions

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Motivations

• Gas prices change with some deviation over
  regions
• How can we know which is the cheapest station?
• Lets say we know it, how can we benefit others
  and ourselves from it?
• Can there be an intelligent entity that
  negotiates with users providing them with the
  best options according to distance, time, and
  money?

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Objectives

• To provide a basic framework for researchers to
  study gas prices negotiation
• To incorporate urban computing in the gas price
  problem in order to solve the lack of information
  on clients side
• To provide a possible new income source
• To develop smart agents that can negotiate gas
  prices with uses successfully

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Related Works

• Automatic collection of fuel prices from a
  network of mobile camera
• A service-oriented negotiation model between
  autonomous agents
• Modeling Agents Behavior in Automated Negotiation
• Netflix game

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Assumptions

• Users have the money and the will to participate
  on sharing the information
• Users work on the weekdays and during the
  weekends may go shopping or stay at home

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Agenda

• Problem statement
• Challenges
• Design
• Evaluation
• Conclusions

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No Existent Framework

• Usage of software engineering to create an easy
  to use framework
• Design patterns for code reusability

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The negotiation set
B
Utility for agent i
Pareto optimal
A
C
Utility of conflict deal for i
E
This circle delimits the space of all possible
deals
Conflict deal
Utility for agent j
D
Utility of conflict deal for j
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Real-life Scenarios

• In order to obtain real results real data was
  needed
• Certain locations were selected for source and
  destinations
• Gas stations data abstracted from real
  observations, i.e. personal and
  http//www.gasbuddy.com

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Nearby Gas Stations

• Distance estimation to avoid using Google maps
  queries
• Great circle distance equation
• RdeltaSigma
• Phi are longitude, Lambda are latitude
• Subscripts s and f stand for the start and final
  locations respectively
• Afterwards Google maps may be used to reach the
  destination

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Agenda

• Problem statement
• Challenges
• Design
• Evaluation
• Conclusions

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The Model

• Server interacts with

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Events

• Basic simulation component used to generate
  messages for communication (negotiation) between
  server and client
• Primary event types
• SEES, ARRIVES, DEPARTS, and NEEDS GAS
• Stucture
• User, location, distance, timestamp

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Scenario Generation

• Selection of random locations to generate three
  sets
• R residential, W work, S shop
• Usage of a transition matrix A(L, d, t) to
  decide the paths
• L is current location
• d is current day
• t is current time

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Scenario Generation (cont.)

• Consult Google to find out the distance, time,
  and stations on the way of the path
• Merge different users according to timestamp

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Example

• USER20 DEPARTS R11 ON 2010-03-22 1353 0
• USER1 DEPARTS R10 ON 2010-03-22 1354 0
• USER20 SEES STATION40 ON 2010-03-22 1354 1.1
• USER1 DEPARTS R10 ON 2010-03-22 1354 0
• USER9 DEPARTS R9 ON 2010-03-22 1354 0
• USER20 SEES STATION9 ON 2010-03-22 1355 1.2
• USER1 SEES STATION40 ON 2010-03-22 1355 0.9
• USER9 SEES STATION10 ON 2010-03-22 1403 1.8
• USER1 SEES STATION59 ON 2010-03-22 1404 1.1
• USER8 DEPARTS R11 ON 2010-03-22 1404 0
• USER20 SEES STATION11 ON 2010-03-22 1404 1.2
• USER1 SEES STATION59 ON 2010-03-22 1404 1.1
• USER8 SEES STATION40 ON 2010-03-22 1405 1.1
• USER9 SEES STATION20 ON 2010-03-22 1417 6.3
• USER1 SEES STATION18 ON 2010-03-22 1418 1.1
• USER8 SEES STATION12 ON 2010-03-22 1418 3.2
• USER20 SEES STATION38 ON 2010-03-22 1418 1.2
• USER1 SEES STATION18 ON 2010-03-22 1418 1.1
• USER9 ARRIVES W6 ON 2010-03-22 1418 6.3

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Server Logic

• Interest in mainly two events, i.e. SEES and
  NEEDS GAS
• Receive request from client
• Analyze for acceptance
• Calculate new value if necessary
• Post result to client
• Client decides based on a probability, i.e. no
  intelligent agent acts on its behalf

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Agenda

• Problem statement
• Challenges
• Design
• Evaluation
• Conclusions

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Types of Servers

• Baseline
• Simple
• Fuzzy Logic
• Probabilistic Neural Network

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Baseline Simulation

• Its serves as a based for additional simulations
• No server exists
• Users get gas from the next station they see when
  needed
• Event is triggered when less than 2 gallons remain

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Simple Simulation

• Both server and users accept offer with a
  probability of p
• Concept of entropy
• minp(-plog(p))
• Values of probabilities represent interest and
  affect the outcome of the negotiations, i.e.
  earnings

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Fuzzy Simulation

• Tries to model the partial agreements using fuzzy
  sets
• Price its changed according to how good or bad
  was the offer
• Acceptance its done through a threshold of
  agreement
• Conditions adapt to a variety of values

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PNN Simulation

• An approximation of the Bayesian networks
• Takes into account the history (statistics) of
  data
• Intelligence its done by layers
• Input one neuron for each controlling parameter
  (i.e. buy price, sell price 2)
• Hidden one neuron for each training sample, uses
  radial basis functions
• Classifier one neuron for output class (i.e.
  reject, accept 2)
• Output the class with the highest contribution
  is the winner

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Results
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Results (cont.)
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Agenda

• Problem statement
• Challenges
• Design
• Evaluation
• Conclusions

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Observations

• The ideal case its an easy to convince user with
  a good negotiator server
• PNN its reliable for the server side since it
  considers the whole history
• Fuzzy logic did not performed well for the server
  because sets are static and dont have memory
• Maybe using adaptation processes like genetic
  algorithms to adjust the sets could improve this
• Negotiation of gas prices can help users to spend
  less money while servers gain some

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Future Work

• Add some intelligence to the user side (e.g.
  Fuzzy Logic)
• Give more analysis to the clients side
• Extend our studies with other real scenarios
  (e.g. include vacation time, seasonal routes,
  etc.)

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References

• An introduction to multiagent systems,
  Wooldridge, 2009 Wiley
• Automated negotiations A survey of the state of
  the , Beam, C. and Segev, A, Wirtschaftsinformatik
  , v 39, n 3, pg 263268, 1997
• Multiagent systems, Sycara, K.P. AI magazine, v
  19, n 2, pages 79--92, 1998
• Bayesian learning in negotiation, Zeng, D. and
  Sycara, K., International Journal of
  Human-Computers Studies, v 48, n 1,
  pages125141, 1998

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Questions