Artificial Intelligence - CHA2555

Assessment 1: Quizzes / polls

Various research tools will be given to the students in order for them to test out the theory. Feedback and help in using the tools will help students to assess their progress and understanding.
 
 

Quizzes/polls

Assessment 2: Written practice exercise

Various research tools will be given to the students in order for them to test out the theory. Feedback and help in using the tools will help students to assess their progress and understanding.

Written Assignment

Assessment 1: Written Assignment

One report designed to assess the understanding and practical application of basic symbolic AI principles such as knowledge representation, search and reasoning.

Assessment 2: Written Assignment

One report designed to assess the understanding and practical application of sub-symbolic and uncertain AI principles such as Bayesian networks and machine learning.

<p>Can machines (in particular computers) be intelligent? And what does that mean precisely? These are the main questions that we try to answer in this module. We will explore how machines can achieve intelligent tasks in a variety of settings. In term one we consider settings with full observability and determinism, these are like laboratory conditions or puzzle games. In this setting, we will look at knowledge representation, problem solving, and planning. In term two the settings are relaxed, and we will study how to deal with the uncertainties that arise from this. In particular, we will see how to deal with opponents, with incomplete and/or uncertain information, and how intelligent agents can learn.</p>

School of Computing and Engineering

<p>https://library.hud.ac.uk/readinglists/CHA2555</p>

Computing and Information Systems

CHA2555 - Artificial Intelligence

Artificial Intelligence

Construct and reason with knowledge representations within a range of symbolic AI formalisms, such as rules, action schemas, classical logic. 

Explain concepts underlying symbolic AI such as knowledge representation and automated reasoning, and fundamental AI processes such as heuristic search methods.

Explain some of the main sub-symbolic approaches used to implement intelligent systems, such as neural networks or Bayesian networks.

Describe some of the central techniques in specific AI areas e.g., knowledge representation and/or machine learning.

Configure, apply and critically evaluate machine learning methods, and appropriate tools and techniques, for implementing intelligent systems in application areas.