Galit Haim, Ya'akov Gal, Sarit Kraus and Michele J. Gelfand A Cultural Sensitive Agent for...
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Transcript of Galit Haim, Ya'akov Gal, Sarit Kraus and Michele J. Gelfand A Cultural Sensitive Agent for...
Galit Haim,
Ya'akov Gal, Sarit Kraus and Michele J. Gelfand
A Cultural Sensitive Agent for Human-Computer
Negotiation
1
Motivation
Buyers and seller across geographical and ethnic borders– electronic commerce: – crowd-sourcing: – deal-of-the-day applications:
Interaction between people from different countries
to succeed, an agent needs to reason about how culture affects people's decision making
2
3
Goals and Challenges
Can we build an agent that will negotiate better than the people in each countries?
Can we build proficient negotiator with no expert designed rules?
Culture sensitive agent?
The approach1. Collect data on each country2. Use machine learning3. Build influence diagram
Sparse Data
Noisy Data
The Colored Trails (CT) Game
An infrastructure for agent design, implementation and evaluation for open environments
Designed in 2004 by Barbara Grosz and Sarit Kraus (Grosz et al AIJ 2010)
4
CT is the right test-bed to use because it provides a task analogy
to the real world
The CT Configuration
7*5 board of colored squares One square is the goal Set of colored chips Move using a chip in the
same color
55
CT Scenario
2 players Multiple phases:
– communication: negotiation
(alternating offer protocol)– transfer: chip exchange– movement
Complete information Agreements are not enforceable Complex dependencies Game ends when one of the players: reached the goal or
did not move for three movement phases6
Scoring and Payment
100 point bonus for getting to goal 5 point bonus for each chip left at end of game 10 point penalty for each square in the shortest
path from end-position to goal Performance does not depend on outcome for
other player
7
Personality, Adaptive Learning (PAL) Agent
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Human behavior
model
Take action
8
machine learning
Decision Making
Data from specific country
Learning People's Reliability
Predict if the other player will keep its promise
9
Learning how People Accept Offers
10 Accept or reject the proposal?
Feature Set
Domain independent feature:– Current and Resulting scores– Offer generosity– Reliability: between 0 (completely unreliable) to
1(fully reliable)– Weighted reliability: over the previous rounds in the
game Domain dependent feature:
– Round number
11
How to Model People's Behavior
For each culture:– Use different features – Choose learning algorithm that minimized error using
10-fold cross validation
In US and Israel - we only used domain independent features
In Lebanon we added domain dependent features
12
Data Collection with Sparse Data
Sources of data to train our classifiers:– 222 game instances consisting of people playing a
rule-based agent – U.S. and Israel: collect 112 game instances of people
playing other people– Lebanon: collect 64 additional games
“Nasty agent”: less reliable when fulfilling its agreement
13
The Lebanon people in this data set almost
always kept the agreements and as a
result, PAL never kept agreements
People Learned Reliability
People learned reliability: Dependent case0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
LebanonU.S.AIsrael
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Experiment Design
3 countries: 157 people– Israel: 63 – Lebanon: 48– U.S.A: 46
30 minutes tutorial Boards varied dependencies between players People were always the first proposer in the
game There was a single path to the goal
15
Decision Making
There are 3 decisions that PAL needs to make: Reliability: determine the PAL transfer strategy Accepting an offer: accept or reject a specific offer
proposed by the opponent Propose an offer
16
Use backward induction over two rounds…
Success Rate: Getting to the Goal
17
Performance Comparison: Averages
18U.S Lebanon Israel
0
50
100
150
200
250
PALHuman
Example in Lebanon
2 chips for 2 chips; accepted both sent 1 chip for 1 chip; accepted PAL learned that people in Lebanon were highly
reliable PAL did not send, the human sent
1919
games were relatively shorter
people were very reliable in the
training games
Example in Israel
2 chips for 2 chips; accepted only PAL sent 1 chip for 1 chip; accepted the human only sent 1 chip for 1 chip; accepted the human only sent 1 chip for 1 chip; accepted only PAL sent 1 chip for 3 chips; accepted only the human sent
20
games were relatively
longer
people were less reliable in the
training games than in Lebanon
Conclusions
PAL is able to learn to negotiate proficiently with people across different cultures
PAL was able to outperform people in all dependency conditions and in all countries
21
This is the first work to show that a computer agent can
learn to negotiate with people in different countries
Colored trails is easy to use
for your own research
Open source empirical test-bed for investigating decision making
Easy to design new games Built in functionality for conducting experiments with
people Over 30 publications Freely available; extensive documentation http://eecs.harvard.edu/ai/ct (or Google ”colored trails”)
THANK YOU [email protected]