Automated reasoning is a branch of artificial intelligence aimed at providing machines with the ability to reason over a given knowledge base and to infer new pieces of information. The ability of reasoning over a set of data is pivotal in a wide range of applications, from consistency checking in a dataset, to logically derive new pieces of information from the application of rules of inference. In this module, students will be exposed to fundamental algorithms for reasoning and their application in domains such as planning—of paramount importance in business logistics—and constraint satisfaction problems, such as satisfiability problems in classical logic, one of the main success stories in artificial intelligence in the past decades. We will then discuss the frontier of research in automated reasoning, with the most up-to-date and efficient reasoning engines for fragment of first-order logic, argumentation theory, and automated reasoning with uncertainty.

1. Implement and evaluate automated reasoning approaches to solve a given task 
    

2. Explain the basic principles underlying common automated reasoning approaches 
    

3. Choose an appropriate automated reasoning approach to address the needs of a given application setting 
    

4. Reflect on the importance of data representation for the success of automated reasoning methods 
    

5. Critically appraise the ethical implications and societal risks associated with the deployment of automated reasoning methods 
    

6. Explain the nature, strengths and limitations of automated reasoning technique 

Modules will be delivered through mainly in-person sessions, online resources and reading materials. You will be guided through learning activities appropriate to your module, which may include: 

on-line resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes), 

on-line interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings) 

face to face small group sessions (e.g. help classes, feedback sessions) 

Implementing automated reasoning tools, taking advantage of existing libraries where appropriate 

Assessing the potential and limitations of automated reasoning methodologies 

Presenting a technical subject matter to an audience of non-specialists 

Critically thinking about which tools are appropriate in what contexts 

Formalising real-world problems in a rigorous way 

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations 

Students will be provided with reassessment opportunities in line with University regulations. 

Automated reasoning and artificial intelligence 

Search algorithms and complexity of algorithms 

Soundness and completeness of inference algorithms 

Satisfiability problems and constraint satisfaction problems 

Propositional inference algorithms 

First-order inference algorithms 

Computational argumentation and argumentative semantics 

Probabilistic reasoning and Bayesian networks 

Probabilistic models in automated reasoning 

Automated reasoning for machine learning