1. Course Overview and Introduction
Multiagent Systems
Course Overview and Introduction
© Manfred Huber 2018

2. Course Overview Course Description:
Multiagent systems has emerged as an important research area with applications in many fields of computer science, including artificial intelligence, e-commerce, sensor networks, distributed computing and information retrieval, information security, and robotics. In multiagent systems, multiple autonomous entities with their own objectives have to interact and make decisions. This course explores techniques for the modeling, design, decision making, and communication in these systems. While the course will focus on frameworks, methodologies, and algorithms, it will investigate (and illustrate) them in the context of a wide range of application areas, including multi-robot systems, distributed scheduling and resource allocation, sensor networks, distributed information extraction, and network security.
© Manfred Huber 2018

3. Course Overview Course Topics: Representations and modeling
Game theory:
Matrix and repeat games, stochastic and Bayesian games
Auction mechanisms
Sealed bid and Vickrey auctions, English and Dutch auctions, combinatorial auctions
Multiagent Communication
Multiagent Learning
Coalitional Game Theory
Formal reasoning examples:
Father(John, Ben)
Brother(Ben, Jack)
Forall x, y, z (Father(x, y) ^ Brother(y, z) -> Father(x, z))
Question Father(v, Jack)
Problem: even this rule works only part of the time
Reasoning by analogy example:
Doves can fly, eagles can fly
Question: Can sparrows fly ?
Problems: very difficult for computer systems, analogy often does not work.
© Manfred Huber 2018

4. Course Overview Prerequisites:
Many of the techniques covered in this course are based on probabilities and random processes and a basic background in statistics is required for the course (CSE 5301 or equivalent).
In addition, experience with Algorithms (CSE 5311), Artificial Intelligence (CSE 5360), and programming will be useful to perform assignments and projects
© Manfred Huber 2018

5. Course Overview Course Page and Materials Textbook: Course web page:
Y. Shoham, K. Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge Press, 2009.
(Available at )
Course web page:
Tentative Office Hours
M 2:00-2:45, W 7:00-8:00, Th 2:00-3:00 ERB 128 or ERB 522
© Manfred Huber 2018

6. Course Overview Grading Policy: Course Work:
In-class presentation of a technical paper
Two homework assignments:
Two small projects
Final course project
Grading Policy:
Determining the state:
John is coughing but no temperature, 60% of all people coughing have the flue, 80% of all people with the temperature have the flue,
Problem: does John have the flue ?
A robot has received a set of sensor readings of the world with uncertain sensors,
Problem: Where is the robot ?
Predicting the outcome:
Given a bag with 2 white and 3 black marbles.
Problem: what marbles will Jack pick if he randomly takes two ?
Determining optimal actions:
Quiz show problem: Pick a door for a price, the host will open one of the other doors that does not have the prize and asks if you want to change your mind
Problem: what door to pick
Reversal: Two of three prisoners get executed.
Problem: should prisoner ask the guard to identify one of the other two who will be executed to reduce his risk ?
Presentation \& Class Participation
15 %
Assignments
30 %
Projects
Final Project
25 %
© Manfred Huber 2018

7. Multiagent Systems and Distributed Decision Making
A system consisting of multiple agents that interact (directly or indirectly through the environment) and reason and make decisions individually (generally with incomplete local information).
Centralized Systems:
A central coordinator determines the actions that each agent in the
system should take
Distributed Systems:
Each agent has to determine the action to be taken (including the
exchange of information) based on its local information
© Manfred Huber 2018

8. Collaborative and Competitive Systems
Collaborative Multiagent Systems:
Agents have the same desires
Well defined optimality
Issues:
Coordination between distributed agents
Communication and bandwidth
Competitive Multiagent Systems:
Different desires for different agents
Optimality only defined for individual agents
Optimal decision making
Interpretation of communication (agents can lie)
© Manfred Huber 2018

9. Multiagent Decision Making
Agents and Rationality
To make decisions, agents have to be able to determine what action is the best for them.
Rationality:
Rational agents make the decisions that result in the highest payoff for them (self-interest)
Rational agents do not take actions to harm others
Payoff is quantified in terms of utility
Multiagent Systems and Optimality
Maximizing an agent’s utility is not always rational
The Commons problem
© Manfred Huber 2018

10. Multiagent Decision Making
Multiagent Decisions:
In competitive systems (even deterministic ones) optimal decisions often have to be nondeterministic
An agent’s utility achieved depends not only on its own actions but also on the actions of the other agents
Decision Theory:
Combines probability, utility theory and rationality to allow an agent to determine the best action in a given situation
© Manfred Huber 2018

11. Background - Probability
Multiagent Systems
Background - Probability
© Manfred Huber 2018

12. Probability
Bayesian probabilities summarize the effects of uncertainty on the state of knowledge
Probabilities represent the values of statistics
P(o) = (# of times of outcome o) / (# of outcomes)
All types of uncertainty are incorporated into a single number
P(H | E)
Probabilities follow a set of strict axioms
Lottery examples:
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13. Probability Random variables define the entities of probability theory
Propositional random variables:
E.g.: IsRed, Earthquake
Multivalued random variables:
E.g.: Color, Weather
Potentially Real-Valued
E.g.: Height, Weight
Lottery examples:
© Manfred Huber 2018

14. Axioms of Probability Probability follows a fixed set of rules
Propositional random variables:
P(A)  [0..1]
P(T) = 1 , P(F) = 0
P(AB) = P(A) + P(B) – P(AB)
P(AB) = P(A) P(B|A)
xValues(X) P(X=x) = 1
© Manfred Huber 2018

15. Probability Syntax
Unconditional or prior probabilities represent the state of knowledge before new observations or evidence
P(H)
A probability distribution gives values for all possible assignments to a random variable
A joint probability distribution gives values for all possible assignments to all random variables
© Manfred Huber 2018

16. Conditional Probability
Conditional probabilities represent the probability after certain observations or facts have been considered
P(H|E) is the posterior probability of H after evidence E is taken into account
Bayes rule allows to derive posterior probabilities from prior probabilities
P(H | E) =
Run doctor example:
P(meningitis) =
P(stiff neck| menengitis) = 0.8
P(E | H) P(H)/P(E)
© Manfred Huber 2018

17. Conditional Probability
Probability calculations can be conditioned by conditioning all terms
Often it is easier to find conditional probabilities
Conditions can be removed by marginalization
P(H) = E P(H|E) P(E)
© Manfred Huber 2018

18. Joint Distributions
A joint distribution defines the probability values for all possible assignments to all random variables
Exponential in the number of random variables
Conditional probabilities can be computed from a joint probability distribution
P(A|B) = P(AB)/P(B)
© Manfred Huber 2018

19. Inference
Inference in probabilistic representation involves the computation of (conditional) probabilities from the available information
Most frequently the computation of a posterior probability P(H|E) form a prior probability P(H) and new evidence E
© Manfred Huber 2018