Multi-agent Systems

Code:

PRODEI012

Acronym:

SMA

Keywords
Classification	Keyword
OFICIAL	Intelligent Systems

Instance: 2020/2021 - 1S

Active?	Yes
Web Page:	http://paginas.fe.up.pt/~eol/SMA/sma.html
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Doctoral Program in Informatics Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PRODEI	10	Syllabus	1	-	6	28	162

Teaching language

English

Objectives

This course has an engineering bias and proposes a global perspective on the techniques associated with agent-based computing, exploring, on one hand, agent-based complex systems modeling and simulation, and the development of intelligent agents and multi-agent system applications.

Agent-Oriented Programming and Software Engineering are presented as a new metaphor to describe and program distributed computational systems.

The acquired knowledge is consolidated through the use of appropriate software tools, with which students are incentivized to work on the development of small projects. The main goal is that students are able to specify and implement complex, adaptive, distributed, and decentralized systems using the multi-agent systems paradigm.

Learning outcomes and competences

At the end of the course, the student is expected to be able to:

• Model complex systems through computational agents
• Project distributed and decentralized systems following the multi-agent systems paradigm
• Develop intelligent software agents, using different architectures
• Evaluate the execution of an agent-based simulation or a distributed multi-agent system
• Develop multiagent systems with adaptability mechanisms

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge of Artificial Intelligence techniques

Program

1. Agent-based computing

• Motivation and goals
• Agent-based simulation vs multi-agent systems
• Tools for MAS development and agent-based simulation: JADE and Repast

2. Intelligent agents

• Definitions
• Agents and environments
• Basic kinds of agent programs
• Agent architectures. Deductive reasoning agents. Practical reasoning: BDI agents. Reactive agents: the subsumption architecture

3. Multi-agent systems

• Definitions.
• Communication. Speech acts. ACL. FIPA standards.
• Interaction protocols. JADE.
• Agent-Oriented Software Engineering

4. Multi-agent decision making

• Game theory. Utilities and preferences. Dominant strategies. Nash equilibria and Pareto-efficiency. Uncertainty. Cooperative game theory.
• Mechanism design. Social choice theory.
• Auctions. Auction protocols. Combinatorial auctions. Double-sided auctions.
• Negotiation. Negotiation attributes. The alternating offers protocol. Negotiation tactics: time and behavior. Negotiation for task allocation. Deals and utilities. Dominance. Pareto optimal and individual rational deals. The monotonic concession protocol.  Negotiation in resource reallocation.

5. Introduction to agent-based simulation

6. Conversational agents

7. Reinforcement Learning

• Learning agents.
• Reinforcement learning. Policies, rewards, and value functions. Markov decision processes. Episodic and continuing tasks. Discounted returns. Temporal-difference learning. Q-learning.
• Deep reinforcement learning

Mandatory literature

Michael Wooldridge; An introduction to multiagent systems. ISBN: 978-0-470-51946-2

Complementary Bibliography

Yoav Shoham; Multiagent systems. ISBN: 978-0-521-89943-7
Stuart Russel, Peter Norvig; Artificial Intelligence: A modern Approach.
Richard S. Sutton, Andrew G. Barto; Reinforcement Learning: An Introduction, A Bradford Book, 2018. ISBN: 978-0262039246

Teaching methods and learning activities

Oral presentation of the themes of the course in classes, with interaction with students. 
Supervision of assignments during classes. Project-oriented learning.

Software

REPAST
JADE

keywords

Physical sciences > Computer science > Computer architecture > Distributed computing
Technological sciences > Technology > Information technology
Physical sciences > Computer science > Cybernetics > Artificial intelligence

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho laboratorial	70,00
Trabalho escrito	30,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	28,00
Elaboração de relatório/dissertação/tese	20,00
Frequência das aulas	28,00
Trabalho de investigação	36,00
Trabalho laboratorial	50,00
Total:	162,00

Eligibility for exams

Enrolled students are admitted to the exam if they do not exceed the allowed number of non-attendance to lab classes (maximum 25% of non-attendance).

Calculation formula of final grade

Distributed Evaluation comprises:

• intermediate project presentation: 20%
• project (individual or group) implementation: 50%
• scientific article documenting the project: 30%

To successfully complete the course, students have to reach a minimum grade of 35% in each of the components listed above.

Examinations or Special Assignments

Evaluation comprises:

• project (individual or group) implementation: 70%
• scientific article documenting the project: 30%

To successfully complete the course, students have to reach a minimum grade of 35% in each of the components listed above.

Special assessment (TE, DA, ...)

Students with special status have to fulfill all the assessment components.

The assessment at a special season follows the same weights and rules of the assessment of students with regular status.

Classification improvement

Students may improve their grades in the next course edition.