This module focuses on modern artificial intelligence (AI) techniques and their inspiration from biological systems. Examples include evolution, multicellular tissues, neural systems, the immune system and swarms, inspiring abstractions such as evolutionary or swarm-based optimization algorithms, neural computing, as well computational approaches to simulate real world systems, (e.g. cellular automata and agent-based models). Lecture material introduces a range of AI and related approaches in the context of their relevant biological inspiration and also their potential application to real word problems. A selection of optimisation and simulation techniques are explored in more depth using Python via active learning exercises. There is an emphasis on applying the scientific approach to practical work within this module.

• Group Project
• Formal Exam

• Blackboard
• Unconfirmed practical marks when available
• Exam Papers, past 2 years (where applicable)

This module aims to:

• provide a foundation in modern computational and AI techniques inspired by specific features of biological systems;
• provide experience of collaborative work that develops biologically-inspired solutions to practical problems;
• provide experience of using the scientific method and critical analysis skills to explore a particular question or hypothesis in the context a bioinspired method or algorithm

On completion of this module, students will be able to:

• describe essential features of biological inspired AI
• explain the key biological features and concepts that have inspired different approaches
• select the most appropriate bioinspired algorithm for a particular purpose
• evaluate the accuracy and efficiency of different bio-inspired optimisation approaches to solve a real world problem
• apply bio-inspired computing techniques to investigate a real world problem