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 Problem statement Challenges Design Evaluation Conclusions 3 3

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

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

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

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

Agenda Problem statement Challenges Design Evaluation Conclusions 8 8

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

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

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

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

Agenda Problem statement Challenges Design Evaluation Conclusions 13

The Model Server interacts with 14

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

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

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

Example USER20 DEPARTS R11 ON 2010-03-22 13:53 0 USER20 SEES STATION40 ON 2010-03-22 13:54 1.1 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 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 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

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

Agenda Problem statement Challenges Design Evaluation Conclusions 20

Types of Servers Baseline Simple Fuzzy Logic Probabilistic Neural Network 21

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

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

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

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

Results 26

Results (cont.) 27

Agenda Problem statement Challenges Design Evaluation Conclusions 28

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

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

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

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