Multiagent Systems Course Overview and Introduction Manfred Huber

Multiagent Systems Course Overview and Introduction © Manfred Huber 2018 1

Course Overview n 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 2

Course Overview n Course Topics: n Representations and modeling n Game theory: n n Matrix and repeat games, stochastic and Bayesian games Auction mechanisms n Sealed bid and Vickrey auctions, English and Dutch auctions, combinatorial auctions n Multiagent Communication n Multiagent Learning n Coalitional Game Theory © Manfred Huber 2018 3

Course Overview n 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 4

Course Overview n Course Page and Materials Textbook: Y. Shoham, K. Leyton-Brown, Multiagent Systems: Algorithmic, Game. Theoretic, and Logical Foundations, Cambridge Press, 2009. (Available at http: //www. masfoundations. org/downloading. html ) Course web page: http: //www-cse. uta. edu/~huber/cse 6369_multi-agent. E-Mail: huber@cse. uta. edu 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 5

Course Overview n Course Work: n n n In-class presentation of a technical paper Two homework assignments: Two small projects Final course project Grading Policy: Presentation & Class Participation 15 % Assignments 30 % Projects 30 % Final Project 25 % © Manfred Huber 2018 6

Multiagent Systems and Distributed Decision Making n Multiagent Systems: 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). n Centralized Systems: A central coordinator determines the actions that each agent in the system should take n 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 7

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

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

Multiagent Decision Making n Multiagent Decisions: n n n 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: n Combines probability, utility theory and rationality to allow an agent to determine the best action in a given situation © Manfred Huber 2018 10

Multiagent Systems Background - Probability © Manfred Huber 2018 11

Probability n Bayesian probabilities summarize the effects of uncertainty on the state of knowledge n Probabilities represent the values of statistics n n P(o) = (# of times of outcome o) / (# of outcomes) All types of uncertainty are incorporated into a single number P(H | E) n Probabilities follow a set of strict axioms © Manfred Huber 2018 12

Probability n Random variables define the entities of probability theory n Propositional random variables: n n Multivalued random variables: n n E. g. : Is. Red, Earthquake E. g. : Color, Weather Potentially Real-Valued n © Manfred Huber 2018 E. g. : Height, Weight 13

Axioms of Probability n Probability follows a fixed set of rules n n n 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) P(B|A) x Values(X) P(X=x) = 1 © Manfred Huber 2018 14

Probability Syntax n Unconditional or prior probabilities represent the state of knowledge before new observations or evidence n n n 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 15

Conditional Probability n Conditional probabilities represent the probability after certain observations or facts have been considered n n 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 n © Manfred Huber 2018 P(H | E) = P(E | H) P(H)/P(E) 16

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

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

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