Introduction to Model-Based Artificial Intelligence

Module aims

In this module you will have the opportunity to:

  • apply model-based algorithms to problems in artificial intelligence, such as planning and robotics
  • study the logical foundations of model-based approaches to artificial intelligence
  • explore methods for formalising and solving model-based problems efficiently

Learning outcomes

Upon successful completion of this module you will be able to:

  • describe the foundations of a variety of methods for knowledge-intensive applications in AI
  • design declarative solutions for knowledge-intensive applications in AI
  • define and implement state-based search problems
  • formulate and solve planning problems in both static and dynamic environments
  • apply logical reasoning and related algorithms to model-based AI problems
  • describe the satisfiability problem and implement a SAT solver for solving such problems efficiently
  • use appropriate algorithms to formulate problems in a SAT-solvable form   

Module syllabus

This module covers the following main themes and associated topics:

  • Introduction to and theoretical foundations of knowledge representation and reasoning in AI 
  • Resolution 
  • Abductive and inductive reasoning
  • Answer Set Programming
  • Sat-solving
  • State-space search algorithms
  • Introduction to planning and teleo-reactive programming for robotics   

Teaching methods

The module will be taught by a combination of traditional lectures, integrated with hands-on problem solving applied to unassessed, formative, exercises. Questions answering and class discussions will be an integral part of the approach. The material will be presented alongside numerous examples throughout, aimed at reinforcing understanding and bridging the gap between theory and practice. The problem-solving sessions will be partially supported by Graduate Teaching Assistants (GTAs).   


There will be two assessed courseworks, collectively counting for 15% of the marks for the module; and  a final written exam, which counts for the remaining 85% of the marks.

The courseworks will be accompanied by individual written feedback. Class-wide feedback will also be delivered, to allow you to learn from common mistakes and good practice.        

Reading list

Module leaders

Professor Francesca Toni
Professor Alessandra Russo