Courses


This is a list of courses taught by members of the Adaptive Systems Research Group at the University of Hertfordshire

 

Artificial Life - Biology and Computation

Level: Postgraduate, MSc (required module for Specialist MSc in Artificial Intelligence)

Module Content:

The overall aim of this module is to provide an in-depth study of a range of advanced ideas, theory, and techniques used in the construction of artificial life systems. The module will be oriented towards (1) the modelling of real-life biological systems and (2) the application of ideas and principles from biology and evolution to computer science in the areas of optimisation, intelligent agents, and engineering, and feedback back to the biological sciences. There is a large practical element to the module with the students gaining experience in developing artificial life models.

Embodied Artificial Intelligence, Cognition and Robotics

Level: Undergraduate, Level 3

Module Content:

This course provides students with a sound introduction to embodied, behaviour-based artificial intelligence as a paradigm for the study of cognition in "whole agents" inhabiting physical environments, as opposed to the study of reasoning emphasised by "traditional" (symbolic) artificial intelligence. From a theoretical perspective, the course will cover a selection of models of adaptive behaviour in embodied artefacts (robotics and software) and their roots in disciplines such as animal behaviour (ethology), cybernetics, biology, and cognitive neuroscience. On a practical level, students will program, conduct, evaluate and critically analyse experiments with embodied agents using appropriate tools such as simple robots (LEGO Mindstorms) and agent simulators (e.g. Starlogo/Netlogo).

Natural Computation and Robotics (Natural Computation, Embodied AI and Behavioural Robotics)

Level: Undergraduate, Level 3

Module Content:

The course is a natural extension of the “Social Intelligence” module offered in the second year and leads to the BSc Honours Adaptive and Robotic Systems Specialist Award. The module is devoted to the study of computational systems that use ideas from natural systems in a broad sense of the word. It thus focuses on evolutionary, ecological, behavioural and physical dynamics by:

  • The exploration of rules that determine patterns found in a number of natural systems (such as the fractal structure of growing and developing tissues, self-organized group-formation, and chaos in population dynamics) by means of simulations (StarLogo);
  • The application of such fundamental rules and principles in the design of optimisation - and classifier algorithms (i.e. in neural networks) and embodied agents (robots);
  • The investigation of the effect of such basic rules in embodied agents (robots) acting in a physical environment;
  • The formalization of rules leading to patterns in natural systems (dynamical systems theory) in order to arrive at both a more generic and a deeper insight in their working.

Social Intelligence and Multi-Agent Systems (Social Intelligence in Animals, Software and Robots)

Level: Undergraduate, Level 2

Module Content:

This module provides students with an introduction to models of “social”- and “swarm-intelligence” and provides knowledge and tools for the application of such models to computational and robotic agents. Students will program, conduct, and critically evaluate experiments with artificial “social” agents.

The aims of this module are to enable students to:

  • Develop a critical understanding of theories and models of “social”- and “swarm-intelligence”, adaptations, self-organization and multi-agent systems;
  • Develop a basic understanding of the use and functioning of artificial ( “social”) agents;
  • Acquire basic skills in simulating and evaluating simple multi-agent systems.

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