Artificial Intelligence: A Practical Introduction

Artificial Intelligence

A Practical Introduction

Artificial intelligence is set to revolutionise the way that we work, live and interact.

In December 2017, Carnegie Mellon poker playing computer Libratus stunned the world by winning 1.7M in a 20-day tournament against four poker stars. This was the latest in a series of big wins for AI including DeepBlue beating Gary Kasparov at chess in 1997 and AlphaGo beating Lee Sedol at Go in 2016.

In a nutshell, the challenge for AI agents is to make decisions in an uncertain environment, exploring the potential consequences of their own choices using complex estimates of the world around.

This course is a study of the basic building blocks of decision-making agents, which are abstract entities living in an uncertain environment and are guided towards the realisation of given objectives. On top of this, the environment is usually inhabited by other agents, which may or may not strive to achieve similar objectives. The task is to take the best possible decision that can be taken given the (incomplete) information available.

These simple models are the basis of a number of important achievements in AI, and combine the use of logical, game-theoretic and algorithmic analysis.

Key Information

Level: Introductory to intermediate
Teaching: 60 hours
Expected independent study: 90 hours
Optional assessment: A 2 hour exam (50%) and coursework (50%)

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU) - please check with your home institution

Apply Now

This course can also be combined with our Exploring British Culture week - find out more.

Syllabus

The course will be an exploration of the basic methodologies for the design of artificial agents in complex environments. The course will first start with classical AI approaches where these agents are goal-oriented and take decisions in a potentially unknown environment.

Then it will move on to more sophisticated models allowing agents to have a representation of the other agents, their potential decisions and their goal, a representation about the representations of other agents, and so forth. This induces complex patterns of strategic reasoning, both in competitive and cooperative interactions, which need to be formally modelled and analysed.

These agent-based systems are built upon three important methodologies: Logic, because of the focus on reasoning, Game-Theory, because of the focus on strategies, and Algorithms, because of the focus on artificial agents.

You will learn the basics of how to program in python and apply this knowledge to studying, and building your own, learning algorithms.

As a coursework challenge, you will bring all of this knowledge together to build your own bot in python that implements strategies to compete against other bots in an auction game.

Topics to be covered include:

Agents: definitions, applications
Reasoning: logic and agents, knowledge representation, inference mechanisms
Decision-making: actions, time and risk
Learning: introduction to reinforcement learning
Introduction to multi-agent systems: definitions, strategies and knowledge, collective strategies, agent application areas.
Multi-agent reasoning: multi-agent epistemic logic, action logics, deliberation, BDI models.
Modelling opponents: uncertainty and expectations, multi-agent learning.
Competitive models: strategies and equilibria, opponent modelling.
Cooperative models: bargaining and negotiation, resource allocation, inter-agent relationships.
Open Issues: development methodology, programming languages, standards.
Beginner to intermediate python.
Learning algorithms in python: regret matching, q-learning, genetic algorithms, collective intelligence.
Writing software bots to compete in games with uncertain environments.

Aims and Learning Outcomes

Course Aims

The course will be an investigation of the most important developments of AI in multi-agent contexts, touching upon themes such as opponent modelling, games with imperfect information, resource allocation, collective decision-making and electronic commerce applications.

Learning Outcomes

Students will learn the basic methodologies for the design and the analysis of AI in complex systems with many interacting agents, ranging from competitive to cooperative interaction. The course take will be interdisciplinary, touching upon themes that are important for computer science, economics, and philosophy.

By the end of the course the students will learn how to program a strategic agent participating in an auction. During the allocated seminars time students will be receive support on the programming skills required for the task (Python), from scratch.

Teaching and Assessment

Course Structure

The format will consist of 4 hours of teaching on most weekdays. This is broken down to include three hours of lectures, followed by one hour of lab exercises, during the theory parts of the module. The programming days will be more hands-on, with a mixture of lectures and exercises throughout the day. Exercise sheets will be provided, as well as samples of previous exam papers.

Due to the intensive nature of the Warwick Summer School, students are expected to attend all timetabled teaching activity, to engage with the teaching material and to spend an average of 2-3 hours per day in self-guided study.

Students registered for Warwick Summer School must attain a minimum of 75% authorised attendance of the timetabled teaching activity. If students fall short of this threshold, they will not be awarded a certificate of completion and they will not be able to sit the exam/submit final assessment. 

Authorised attendance includes any sickness absence during the course of the programme that has been properly notified and recorded. An attendance register will be taken at the beginning of teaching session so students need to ensure they arrive on time in order to be marked as attended.

Course Assessment

A 2 hour exam (50%) and coursework (50%)

Students who choose to sit the final exam and submit the assessment will receive a transcript of results. The transcript of results will state the name of the student, the course studied, the exam mark and the grade. Transcripts are automatically sent to students via email by the middle of September.  You should keep this safe in case you need it for credit transfer approval or for future reference. If you would like us to email a copy of your transcript directly to someone at your University who deals with credit transfer please let us know.

The Summer School does not offer re-takes of examinations, whatever your result in the original examination. 

Sample Timetable

Subject to changes, this is an overview of the timetable for Warwick Summer School students.

	Monday	Tuesday	Wednesday	Thursday	Friday
Week 1	2 hour introductory lecture	4 hours of teaching 1 hour study group	4 hours of teaching	4 hours of teaching 1 hour study group	4 hours of teaching
Week 2	4 hours of teaching	4 hours of teaching 1 hour study group	No timetabled teaching activity	4 hours of teaching 1 hour study group	Sports activities
Week 3	4 hours of teaching	4 hours of teaching 1 hour study group	4 hours of teaching	2 hour final assessment preparation 1 hour study group	Final Assessment

In addition to the above schedule, students are expected to attend the Guest Lecture Series on Tuesday afternoons and dedicate approximately 88 hours to self-study throughout the programme. Social activities are also organised throughout the week, with optional trips running on Saturdays and the Wednesday in Week 2; for details, please visit our Social and Cultural pages.

Entry Requirements

There are no prerequisites to this course, however students should keep in mind that it is ultimately a technical course, where mathematical and programming knowledge will be provided. This course is open to students studying any discipline at University level. We welcome individuals from all backgrounds, including students who are currently studying another subject but who want to broaden their knowledge in another discipline.

Students must be aged 18 or over by the time the Summer School commences and have a good understanding of the English language.

Alumni

"After returning from the Summer School at Warwick, I noticed a significant improvement in my regular academic work. The routine I developed there helped me become more organized with my time. Although the subject matter (I took the Practical Introduction to AI course) was challenging and I continue to study it to this day, it was intellectually rewarding in numerous ways. This experience boosted my confidence significantly, especially when exploring new topics, methodologies, and mathematical models because I was no longer intimidated by subjects that at first glance seemed intractable. The approachable nature of the professors and assistant in the course encouraged me to ask questions without fear, regardless of how "dumb" I thought my questions might be. Their helpful and engaging responses ensured that I fully understood their answers, enhancing my overall learning experience."

Diego Macall (France)

Your Teachers

Lecturers

Teaching Assistants

Dr Paolo TurriniLink opens in a new window

Charlie PilgrimLink opens in a new window

Luisa Estrada

Claudia Viaro

Reading List

Core texts:

Shoham Y. and Leyton-Brown K., Multi-Agent Systems: Logical, Algorithmic and Game Theoretic foundations, Cambridge University Press, 2009.
Russell S and Norvig P, Artificial Intelligence: A Modern Approach, 3rd edition, Prentice-Hall, 2014.