ai@bgu coordination and collusion in three- player strategic environments ya’akov (kobi) gal...
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AI@BGU 1
Coordination and Collusion in Three-Player Strategic Environments
Ya’akov (Kobi) GalDepartment of Information Systems Engineering
Ben-Gurion University of the NegevSchool of Engineering and Applied Sciences,
Harvard University
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AI@BGU 2
Motivation
• People interact with computers more than ever before.
• Examples: electronic commerce, medical applications.
• Can we use computers to improve people’s performance?
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AI@BGU 3
Encouraging Healthy Behaviors
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AI@BGU 4
Application: Automated Mediators for Resolving Conflicts
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AI@BGUIntroduction 5
“Opportunistic” Route Planning [Azaria et al., AAAI 12]
most effective commute
opportunistic commercedrive home
Route A Route B
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AI@BGU 6
Computers as Trainers
• Good idea, because computers – are designed by experts.– Use game theory, machine
learning.– Always available.
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AI@BGU 7
Computers as Trainers
• Bad idea, because computers – Deter and frustrate
people.– Difficult to learn from.– Do not play like people.
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AI@BGU 8
Questions• How do humans play the LSG?• How will automated agents handle an environment
with humans?• Can automated agents successfully cooperate with
humans in such environment?• Can human learn and improve by playing with
automated agents?
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AI@BGU 9
Methodology
• Subjects to play the LSGin a lab. No subject knows the identity of his opponents.
• Subjects are paid by performance over time. • Used state-of-the-art Automated agents for
training and evaluation purposes.• Show instructions* Testing agent: EAsquared(Southampton). * Training agents:
GoffBot (Brown), MatchMate(GTech).
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AI@BGU 10
Empirical Methodology
• Subject played 3 sessions of 30 rounds each.• The first two sessions were “training sessions”
using – two automated agents– one automated agent– no automated agents
• Testing always included two people and a single “standardized” agent.
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AI@BGU 11
Performance results
• Training with more computer agents = better performance.
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AI@BGU 12
Performance results
• Training with more computer agents = better performance.
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AI@BGU 13
Behavioral Analysis
• People are erratic
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AI@BGU 14
People play erratically
• People simple heuristic – move to the middle of the large gap between the two opponents
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AI@BGU 15
People play erratically
• People simple heuristic – move to the middle of the large gap between the two opponents
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AI@BGU 16
People play erratically
• People simple heuristic – move to the middle of the large gap between the two opponents
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AI@BGU 17
Cooperative Behavior Analysis
• Stick: pos_k[i+1]=pos_k[i]• Follow: pos_k [i+1]=across(pos_j[i]); j not = k
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AI@BGU 18
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AI@BGU 19
Implication
• Difficult for people to identify opportunities for cooperation in 3-player games– In contrast to results from 2-player PD games.
• Computer agents can help people improve their performance, even in strictly competitive environments with three players.
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AI@BGU 20
Other issues and Next Steps
• Does programming an agent increases subjects performance in the game?– YES (see paper)
• How do people behave when there is no automated agent in the testing epoch?– Highly erratic
• Can we make people the basis of the next LSG tournament?
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AI@BGU
Artificial Intelligence Research at BGU
14 Faculty MembersOver 20 graduate studentsCutting-edge research