Introduction to IntelligentAutonomous Systems

Code:

CC3042

Acronym:

CC3042

Level:

300

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2024/2025 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Artificial Intelligence and Data Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:IACD	56	study plan from 2021/22	3	-	6	48	162

Teaching language

Suitable for English-speaking students

Objectives

This course presents a global perspective of the techniques associated with intelligent and autonomous systems, exploring the modeling and simulation of complex systems and the development of intelligent agents and Multi-Agent Systems with the ability to adapt/learn to solve complex problems. The main objective is to specify and implement autonomous, complex, and adaptive intelligent systems. At the end of the course, students should be able to:
1. Understand basic concepts related to autonomous intelligent systems and be able to model and design complex intelligent and autonomous systems.
2. Understand and be capable of using concepts of intelligent multi-agent systems such as communication, interaction, coordination, negotiation, and cooperation.
3. Understand and be capable of using reinforcement learning, including state-of-the-art algorithms and deep reinforcement learning mechanisms.

Learning outcomes and competences

Ability to specify and implement autonomous, complex, and adaptive intelligent systems.

Working method

Presencial

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

Knowledge of Python / Java programming and Artificial Intelligence.

Program

1. Intelligent Autonomous Systems: Intelligent Systems; Agents; Agent architectures.
2. Multi-Agent Systems. Agent-based simulation. Speech-act theory. Agent communication languages. Development platforms.
3. Multi-agent decision making. Game theory. Social choice theory. Mechanism design: protocols and auctions. Negotiation.
4. Learning in autonomous systems. Reinforcement Learning. Markov Decision Processes. Model-based and model-free methods. Monte Carlo. Temporal-difference learning: Q-learning, SARSA. Value function approximation. Policy gradient methods.

Mandatory literature

Michael Wooldridge; An introduction to multiagent systems. ISBN: 0-471-49691-X
Richard S. Sutton, Andrew G. Barto; Reinforcement learning: an introduction (2nd edition), The MIT Press, 2018. ISBN: 978-0-262-19398-6 (http://incompleteideas.net/book/the-book-2nd.html)

Complementary Bibliography

Stuart Jonathan Russell; Artificial intelligence. ISBN: 978-1-292-40113-3
Yoav Shoham, Kevin Leyton-Brown; Multiagent Systems – Algorithmic, Game-Theoretic, and Logical Foundations. ISBN: 978-0-521-89943-7 (http://www.masfoundations.org/)

Teaching methods and learning activities

Oral presentation of the themes of the course in theoretical classes, with interaction with students. Tool exploration and exercise solving in the practical classes. Practical classes will also be based on the supervision of assignments.

Software

SPADE
JADE

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	50,00
Frequência das aulas	48,00
Trabalho laboratorial	64,00
Total:	162,00

Eligibility for exams

Terms of frequency: Enrolled students must not exceed the allowed number of non-attendances to lab classes and achieve a minimum grade of 7.5 (out of 20) in each of the practical assignments.

Calculation formula of final grade

Final Grade = 0.35*TP1+0.15*TP2+0.5*E
TP1: practical assignment 1
TP2: practical assignment 2
E: exam

The minimum grade for the exam is 7.5 (out of 20).

Special assessment (TE, DA, ...)

Students who have been exempted from practical classes must agree with their teachers on how they will be accompanied in their practical work.

Evaluation at a special season corresponds to performing a practical assignment and the exam, each worth 50% in the final grade and keeping the minimum grades referred to in the normal assessment.

Classification improvement

The exam grade can be improved in the appeal period. The practical part cannot be improved.