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Summary : multi-agent systems

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Summary lectures and reading materials on multi-agent systems, with some examples to explain the material. Be aware!: lecture 8 is not included!

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  • 9 maart 2023
  • 13
  • 2022/2023
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Summary: Multi-agent Systems (MAS)

After completing the course, the student

 Knows the main concepts and abstractions that are used in multi-agent systems (e.g., autonomous agents, environment,
interaction, knowledge, preference, strategy, protocol).
 Is acquainted with the theoretical aspects and problems of multi-agent systems (e.g., preference modelling, interaction analysis,
negotiation, communication, coordination).
 Knows basic concepts from decision and game theory (e.g., decision rules, various equilibria, mechanism design, social choice
functions, voting mechanisms).
 Is able to work with game theoretic concepts (e.g., determining different equilibria, working with different game settings such as
strategic and extensive games, determining winners of various voting mechanism and auctions).
 Recognizes different applications of multi-agent systems (e.g., auctions, voting systems, automatic negotiation systems).
 Has insight into the strengths and weaknesses of multi-agent systems (e.g., distributed nature of multiagent systems, impossibility
results from game theory and its relevance for multi-agent systems).
 Recognizes the interdisciplinary nature of multi-agent systems (e.g., the relations between multi-agent systems with social
sciences, psychology, and economy).




Basic concepts
Lecture 1: Introduction
Lecture 2: Game Theory (part 1)
Lecture 3: Game Theory (part 2)
Lecture 4: Extensive games
Lecture 5: Games and Communication
Lecture 6: Coalitional Theory
Lecture 7: Aggregating Preferences & Social Choice
Lecture 8: Mechanism Design
Lecture 9: Auctions
Exercises




-
Basic concepts Column & row:
Column:

, Row: Autonomous agents sense their environments, learn and reason
to decide and execute actions that maximize the chance to
Normal form games vs. extensive form games: successfully achieve their objectives.
In an extensive-form game, actions are taken in succession. In a
normal-form game actions are taken simultaneously.




Extensive-form game Normal-form game

Autonomous agents are:
Common-payoff games: 1. Pro-active: deliberative/goal-directed behaviors.
Games in which, for every action profile, all players have the -> react and respond to (all) the possibilities and try to find the
same payoff (also know as: pure coordination games or team best outcome.
games). 2. Adaptive: changing its behavior based on experience.
3. Reactive: respond to changes in its environment.
In such games the agents have no conflicting interests; their sole 4. Rational: behaves to maximize its achievements.
challenge is to coordinate on an action that is maximally -> there are different types of rationality, this course focuses
beneficial to all. on economical rationality.
Example: -> economical rationality: when you make a choice, you will
choose the thing you like best.
5. Social: capable of interacting and communicating with other
agents.

Symmetric game: The difference between probabilistic and stochastic is
A game in which if player 1 plays that probabilistic is (mathematics) of, pertaining to or derived
strategy A and player 2 plays strategy using probability while stochastic is random, randomly
B, player 1’s payoff is the same as determined, relating to stochastic.
player 2’s payoff if player 1 were to
instead play B and player 2 were to Multi-agent systems
instead play A and vice versa. A multi-agent system consists of a set of autonomous agents that
interact with each other and their surrounding environment to
Zero-sum games: achieve their (joint) objectives.
Also know as constant-sum games or strictly competitive.
Zero sum game is a game in which the sum of all player’s payoffs Coordination aims at avoiding extraneous activities by
equals zero for every outcome. synchronising and aligning agents activities:
* Cooperation: agents’ can cooperate to solve their problems
A two-player normal-form game is constant sum if there exists a (e.g. task sharing, results sharing).
constant c such that for each strategy profile a ∈ A1 × A2 it is the * Organisation: organisation aims at arranging and managing
case that u1(a) + u2(a) = c. the agents’ interaction (e.g. marketplaces).
* Negotiation: agents negotiate to reach agreements (e.g.
It is called zero-sum because, when not clarified, it is assumed auction bidding, argumentation to convince each other to
that, c = 0, that is, that we have a zero-sum game. agree on an outcome).

Zero-sum games represent situations of pure competition; one In a multi-agent environment, the effect of your decision is also
player’s gain must come at the expense of the other player. affected by other agents decisions.
-> example: an agent opens a door, then another agent closes
Example: the door. Even though the first agent opened the door, the
state of the door right now is closed.




Symmetric zero sum game:
Theorem: in finite, two player, symmetric, zero sum games, each
player’s equilibrium expected utility must be equal to zero.
Note!: equilibrium implies payoffs of 0, not payoffs of 0 imply
equilibrium!

Lecture 1 (Introduction)
Lecture 2 (Game Theory: part 1)
(Individual) autonomous agents Game theory is the mathematical study of interaction among
independent, self interested agents.

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