Artificial Intelligence A Practical Introduction

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.

Course leader

Dr Paolo TurriniLink, Charlie Pilgrim

Target group

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 should also meet our standard entry requirements and must be aged 18 or over by the time the Summer School commences and have a good understanding of the English.

Course aim

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.