Artificial Intelligence: A Practical Introduction | Short course at the Summer School at Warwick University
Artificial Intelligence
A Practical Introduction
Artificial intelligence is changing the way we work, communicate and connect to others.
From superhuman-game playing achievements to proficient conversation skills, AI agents are now capable of making rational strategic decisions backed by accurate predictions in many situations that very hard for humans.
But what lies underneath the Agentic AI revolution and how can we build simple AI agents capable of accurate reasoning and good decisions?
Combining theoretical understanding and practical implementation of reinforcement learning and strategic reasoning, this course will take you on a journey into the basic building blocks of decision-making agents, which are computational entities living in an uncertain environment and guided towards the realisation of given objectives.
How do we construct such agents and how do we guarantee that they solve our problems?
Key Information
Level: Introductory to intermediate
Fees: Please see Fees page
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
This course can also be combined with our Exploring British Culture week or our Preparatory English Online Programme.
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.
Please note changes to the syllabus and teaching team may be made over the coming months before exact set of topics are finalised.