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Negotiating the value of gas price

Negotiating the value of gas price. By: Hector M Lugo-Cordero, MS Saad A Khan, MS EEL 6788. Agenda. Problem statement Challenges Design Evaluation Conclusions. Agenda. 3. Problem statement Challenges Design Evaluation Conclusions. Motivations.

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Negotiating the value of gas price

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  1. Negotiating the value of gas price By: Hector M Lugo-Cordero, MS Saad A Khan, MS EEL 6788

  2. Agenda • Problem statement • Challenges • Design • Evaluation • Conclusions

  3. Agenda 3 Problem statement Challenges Design Evaluation Conclusions

  4. 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? 4

  5. 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 client’s side • To provide a possible new income source • To develop smart agents that can negotiate gas prices with uses successfully 5

  6. 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 6

  7. 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 7

  8. Agenda 8 Problem statement Challenges Design Evaluation Conclusions

  9. No Existent Framework • Usage of software engineering to create an easy to use framework • Design patterns for code reusability 9

  10. 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 10

  11. 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 11

  12. Nearby Gas Stations • Distance estimation to avoid using Google maps queries • Great circle distance equation • R*deltaSigma • 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 12

  13. Agenda 13 Problem statement Challenges Design Evaluation Conclusions

  14. The Model • Server interacts with 14

  15. 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 15

  16. 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 16

  17. 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 17

  18. Example • USER20 DEPARTS R11 ON 2010-03-22 13:53 0 • USER1 DEPARTS R10 ON 2010-03-22 13:54 0 • USER20 SEES STATION40 ON 2010-03-22 13:54 1.1 • USER1 DEPARTS R10 ON 2010-03-22 13:54 0 • USER9 DEPARTS R9 ON 2010-03-22 13:54 0 • USER20 SEES STATION9 ON 2010-03-22 13:55 1.2 • USER1 SEES STATION40 ON 2010-03-22 13:55 0.9 • USER9 SEES STATION10 ON 2010-03-22 14:03 1.8 • USER1 SEES STATION59 ON 2010-03-22 14:04 1.1 • USER8 DEPARTS R11 ON 2010-03-22 14:04 0 • USER20 SEES STATION11 ON 2010-03-22 14:04 1.2 • USER1 SEES STATION59 ON 2010-03-22 14:04 1.1 • USER8 SEES STATION40 ON 2010-03-22 14:05 1.1 • USER9 SEES STATION20 ON 2010-03-22 14:17 6.3 • USER1 SEES STATION18 ON 2010-03-22 14:18 1.1 • USER8 SEES STATION12 ON 2010-03-22 14:18 3.2 • USER20 SEES STATION38 ON 2010-03-22 14:18 1.2 • USER1 SEES STATION18 ON 2010-03-22 14:18 1.1 • USER9 ARRIVES W6 ON 2010-03-22 14:18 6.3 • USER1 SEES STATION15 ON 2010-03-22 14:19 1.1 • USER8 SEES STATION6 ON 2010-03-22 14:19 3.4 • USER20 ARRIVES W1 ON 2010-03-22 14:19 1.2 18

  19. 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 19

  20. Agenda 20 Problem statement Challenges Design Evaluation Conclusions

  21. Types of Servers • Baseline • Simple • Fuzzy Logic • Probabilistic Neural Network 21

  22. 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 22

  23. 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 23

  24. 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 24

  25. 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 25

  26. Results 26

  27. Results (cont.) 27

  28. Agenda 28 Problem statement Challenges Design Evaluation Conclusions

  29. Observations • The ideal case it’s 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 don’t 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 29

  30. Future Work • Add some intelligence to the user side (e.g. Fuzzy Logic) • Give more analysis to the client’s side • Extend our studies with other real scenarios (e.g. include vacation time, seasonal routes, etc.) 30

  31. 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 263—268, 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, pages=125—141, 1998

  32. Questions

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