Foundations of AI Multi Agent Systems Course

EkanshSomani1 92 views 102 slides Jun 21, 2024
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About This Presentation

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Slide Content

[CS 329] Foundations of AI: Multi-Agent Systems
P Manisha
[email protected]
Introduction
16 May 2024

[CS 329] Foundations of AI: Multi-Agent Systems
Link to the Course WebsiteGrading pattern
•1 midsem exam (25%)
•1 endsem exam (35%)
•Two assignments (20% each)
1 / 38

Agenda
•What are Multi-Agent Systems (MAS)?
•Real-world examples with agent interactions•Components of MAS•Why Game Theory?•Applications and role of Computer Science•Game Theory and Machine Learning
2 / 38

Agenda
•What are Multi-Agent Systems (MAS)?
•Real-world examples with agent interactions•Components of MAS•Why Game Theory?•Applications and role of Computer Science•Game Theory and Machine Learning
2 / 38

Agenda
•What are Multi-Agent Systems (MAS)?
•Real-world examples with agent interactions•Components of MAS•Why Game Theory?•Applications and role of Computer Science•Game Theory and Machine Learning
2 / 38

Agenda
•What are Multi-Agent Systems (MAS)?
•Real-world examples with agent interactions•Components of MAS•Why Game Theory?•Applications and role of Computer Science•Game Theory and Machine Learning
2 / 38

Agenda
•What are Multi-Agent Systems (MAS)?
•Real-world examples with agent interactions•Components of MAS•Why Game Theory?•Applications and role of Computer Science•Game Theory and Machine Learning
2 / 38

Agenda
•What are Multi-Agent Systems (MAS)?
•Real-world examples with agent interactions•Components of MAS•Why Game Theory?•Applications and role of Computer Science•Game Theory and Machine Learning
2 / 38

Mutli-Agent Systems
What is a multi-agent system?
3 / 38

Mutli-Agent Systems
What is a multi-agent system?
3 / 38

Mutli-Agent Systems
What is a multi-agent system?
3 / 38

Mutli-Agent Systems
Can you give more examples of multi-agent systems?
•Family - Division of property•Couple - Battle of Sexes•Any organization - Resource Allocation•Sports - Between teams and organizers•Markets - Big Role in shaping the subject
4 / 38

Mutli-Agent Systems
Can you give more examples of multi-agent systems?
•Family - Division of property•Couple - Battle of Sexes•Any organization - Resource Allocation•Sports - Between teams and organizers•Markets - Big Role in shaping the subject
4 / 38

Mutli-Agent Systems
Can you give more examples of multi-agent systems?
•Family - Division of property•Couple - Battle of Sexes•Any organization - Resource Allocation•Sports - Between teams and organizers•Markets - Big Role in shaping the subject
4 / 38

Mutli-Agent Systems
Can you give more examples of multi-agent systems?
•Family - Division of property•Couple - Battle of Sexes•Any organization - Resource Allocation•Sports - Between teams and organizers•Markets - Big Role in shaping the subject
4 / 38

Mutli-Agent Systems
Can you give more examples of multi-agent systems?
•Family - Division of property•Couple - Battle of Sexes•Any organization - Resource Allocation•Sports - Between teams and organizers•Markets - Big Role in shaping the subject
4 / 38

Mutli-Agent Systems
Can you give more examples of multi-agent systems?
•Family - Division of property•Couple - Battle of Sexes•Any organization - Resource Allocation•Sports - Between teams and organizers•Markets - Big Role in shaping the subject
4 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
What is the most important aspect of any multi-agent system?
•Involves more than one person•They are interacting▶Cooperating▶Conflicting▶Something in between
•System evolves (aka life goes on)
What all can it model?
5 / 38

Multi-Agent Systems
Division of Cake
6 / 38

Demonitization
Demonitization: Success or a Failure?
7 / 38

Braess’s Paradox
On Earth Day, New York City’s
Transportation Commissioner decided to
close 42d Street, one of the most
congested roads.
And the traffic improved!
8 / 38

Braess’s Paradox
On Earth Day, New York City’s
Transportation Commissioner decided to
close 42d Street, one of the most
congested roads.
And the traffic improved!
8 / 38

Braess’s Paradox
On Earth Day, New York City’s
Transportation Commissioner decided to
close 42d Street, one of the most
congested roads.
And the traffic improved!
8 / 38

Elections
Is majority voting the right thing to do?
2000 US Presidential Election in Florida
Democratic (Al Gore)
Votes: x + 64000
Repulican (George Bush)
Votes: x + 537 + 33000Clearly, democrats had the
majority
9 / 38

Elections
Is majority voting the right thing to do?
2000 US Presidential Election in Florida
Democratic (Al Gore)
Votes: x + 64000
Repulican (George Bush)
Votes: x + 537 + 33000Clearly, democrats had the
majority
9 / 38

Elections
Is majority voting the right thing to do?
2000 US Presidential Election in Florida
Democratic (Al Gore)
Votes: x + 64000
Repulican (George Bush)
Votes: x + 537 + 33000Clearly, democrats had the
majority
9 / 38

Elections
Is majority voting the right thing to do?
2000 US Presidential Election in Florida
Democratic (Al Gore)
Votes: x + 64000
Repulican (George Bush)
Votes: x + 537 + 33000Clearly, democrats had the
majority
9 / 38

Elections
Is majority voting really fair?
2000 US Presidential Election in Florida
Democratic (Al Gore)
Votes: x
Republican (George Bush)
Votes: x + 537
The Green Party (Ralph
Nader)Figure:
10 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Multi-Agent Systems
Two parts to it
•Analyzing Agent Interactions▶What do the agents want?▶Can they get what they want?▶What must they do to get it?▶How do they interact with each other?•Analyzing System as a whole▶What is the system and its rules?▶Transparency between agents and the system (Chess vs Cards)▶How does the system interact with the agents?▶What is the overall objective?▶Is it possible to achieve the objective? How?
11 / 38

Game Theory
The foundation of MAS is Game Theory - which analyzes and predicts agent behaviour
•Nobel Memorial Prize for Economics: Samuelson(1970), Arrow (1972), Selten,
Nash, Harsanyi (1994), Vickery (1996), Schelling, Aumann (2005), Maskin,
Myerson, Hurwicz (2007), Shapley, Roth (2012), Paul Milogram, Robert Wilson
(2020)
12 / 38

Game Theory
The foundation of MAS is Game Theory - which analyzes and predicts agent behaviour
•Nobel Memorial Prize for Economics: Samuelson(1970), Arrow (1972), Selten,
Nash, Harsanyi (1994), Vickery (1996), Schelling, Aumann (2005), Maskin,
Myerson, Hurwicz (2007), Shapley, Roth (2012), Paul Milogram, Robert Wilson
(2020)
12 / 38

Game Theory
John Von Neumann
Definition (Game Theory)
Game theory is the study of mathematical models
of conflict and cooperation between intelligent,
rational decision-makers. – Myerson
13 / 38

Game Theory
John Von Neumann
Definition (Game Theory)
Game theory is the study of mathematical models
of conflict and cooperation between intelligent,
rational decision-makers. – Myerson
13 / 38

Game Theory
What is the role of Computer Science in Game Theory?
•How does Google make money?•Selling your attention!
14 / 38

Game Theory
What is the role of Computer Science in Game Theory?
•How does Google make money?•Selling your attention!
14 / 38

Game Theory
What is the role of Computer Science in Game Theory?
•How does Google make money?•Selling your attention!
14 / 38

Internet Advertisement
How does Google make money?
15 / 38

Internet Advertisement
How does Google make money?
15 / 38

Internet Advertisement
Google’s Ad Auctions
•For every search, find ads whose keywords
match
•Remove ads that are not eligible (policy,
location)
•Among the remaining select ones with a high
rank (bids, quality)
Auctions at large scale to maximize revenue!
16 / 38

Internet Advertisement
Google’s Ad Auctions
•For every search, find ads whose keywords
match
•Remove ads that are not eligible (policy,
location)
•Among the remaining select ones with a high
rank (bids, quality)
Auctions at large scale to maximize revenue!
16 / 38

Internet Advertisement
Google’s Ad Auctions
•For every search, find ads whose keywords
match
•Remove ads that are not eligible (policy,
location)
•Among the remaining select ones with a high
rank (bids, quality)
Auctions at large scale to maximize revenue!
16 / 38

Internet Advertisement
Google’s Ad Auctions
•For every search, find ads whose keywords
match
•Remove ads that are not eligible (policy,
location)
•Among the remaining select ones with a high
rank (bids, quality)
Auctions at large scale to maximize revenue!
16 / 38

Internet Advertisement
Google’s Ad Auctions
•For every search, find ads whose keywords
match
•Remove ads that are not eligible (policy,
location)
•Among the remaining select ones with a high
rank (bids, quality)
Auctions at large scale to maximize revenue!
16 / 38

Internet Advertisement
Google’s Ad Auctions
•For every search, find ads whose keywords
match
•Remove ads that are not eligible (policy,
location)
•Among the remaining select ones with a high
rank (bids, quality)
Auctions at large scale to maximize revenue!
16 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Markets (e-Commerce)
•Amazon•Uber/Ola•Airbnb/Oyo Hotel room bookings•Resource allocation such as computing power to dynamically arriving requests
17 / 38

Fair Preference Aggregation
•http://robovote.org
RoboVote is a free service that helps users combine their preferences or opinions into
optimal decisions. To do so, RoboVote employs state-of-the-art voting methods
developed in artificial intelligence research.
•http://www.spliddit.org
Spliddit is a not-for-profit academic endeavor. Its mission is twofold:
To provide easy access to carefully designed fair division methods, thereby making the
world a bit fairer. To communicate to the public the beauty and value of theoretical
research in computer science, mathematics, and economics, from an unusual
perspective.
18 / 38

Fair Preference Aggregation
•http://robovote.org
RoboVote is a free service that helps users combine their preferences or opinions into
optimal decisions. To do so, RoboVote employs state-of-the-art voting methods
developed in artificial intelligence research.
•http://www.spliddit.org
Spliddit is a not-for-profit academic endeavor. Its mission is twofold:
To provide easy access to carefully designed fair division methods, thereby making the
world a bit fairer. To communicate to the public the beauty and value of theoretical
research in computer science, mathematics, and economics, from an unusual
perspective.
18 / 38

Surveillance
PROTECTPAWS
https://teamcore.seas.harvard.edu/
19 / 38

Surveillance
PROTECTPAWS
https://teamcore.seas.harvard.edu/
19 / 38

Surveillance
PROTECTPAWS
https://teamcore.seas.harvard.edu/
19 / 38

Machine Learning in Game Theory
•Designing Mechanisms
•Preference Learning - Multi-Armed Bandit
•Designing systems where agents are strategic and learning preferences
•Fair Allocation
20 / 38

Designing Mechanisms
What is a mechanism?
A social planner designs the rules of a game in a way that strategic and intelligent
agents interact but the planner’s objective is achieved.
•Auctions
•Voting
•Crowdfunding
•Crowdsourcing
21 / 38

Designing Mechanisms
What is a mechanism?
A social planner designs the rules of a game in a way that strategic and intelligent
agents interact but the planner’s objective is achieved.
•Auctions
•Voting
•Crowdfunding
•Crowdsourcing
21 / 38

Designing Mechanisms
What is a mechanism?
A social planner designs the rules of a game in a way that strategic and intelligent
agents interact but the planner’s objective is achieved.
•Auctions
•Voting
•Crowdfunding
•Crowdsourcing
21 / 38

Designing Mechanisms
Revenue Optimal Auctions
22 / 38

Designing Mechanisms
Redistribution Mechanisms
23 / 38

Designing Mechanisms
Price prediction in Agriculture
24 / 38

Designing Mechanisms
Fair Resource Allocation
25 / 38

Designing Mechanisms
What if the agents do not even know the value?
26 / 38

Designing Mechanisms
What if the agents do not even know the value?
26 / 38

Designing Mechanisms
Multi-Armed Bandit-based Mechanism Design - Preference Learning
27 / 38

Designing Mechanisms
Multi-Armed Bandit-based Mechanism Design - Preference Learning
27 / 38

Preference Learning
Agent preferences are not very simple to extract!
28 / 38

Preference Learning
Agent preferences are not very simple to extract!
28 / 38

Preference Learning
Agent preferences are not very simple to extract!
29 / 38

Preference Learning
Agent preferences are not very simple to extract!
29 / 38

Game Theory in Machine Learning
•Feature Selection
•Explainability
•Generative Adversarial Networks
•Strategic Data Classification
•Strategic Agents and Private ML
•Recommendation Systems - two-sided matching
•Multi-Agent RL
30 / 38

Feature Selection
•Which features are important for classification/mining?
1
1
Feature evaluation and selection with cooperative game theory by X Sunet.al., Pattern
Recognition, 2012
31 / 38

Explanability
Neural Networks are black-box models!
32 / 38

GANs
Figure:
33 / 38

Strategic Data Classification
Build classifiers robust to manipulations
34 / 38

Private Data Acquisition
Data in exchange for a monetary reward
35 / 38

Recommendation System as Two Sided Matching
Congestions
36 / 38

Multi-Agent RL
•Typical Reinforcement Learning models single agent
•Many real-world situations need multiple agents
▶The agents jointly decide the actions▶The agents are non-cooperative▶Agents are adversarial
37 / 38

Multi-Agent RL
•Typical Reinforcement Learning models single agent
•Many real-world situations need multiple agents
▶The agents jointly decide the actions▶The agents are non-cooperative▶Agents are adversarial
37 / 38

Multi-Agent RL
•Typical Reinforcement Learning models single agent
•Many real-world situations need multiple agents
▶The agents jointly decide the actions▶The agents are non-cooperative▶Agents are adversarial
37 / 38

Summary
•Agent interactions at the core of multi-agent systems
•Game Theory models agent interactions
•Role of Computer science
•ML and Game Theory
38 / 38
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