Logics for ai

By studying some of the most relevant applications of logic to AI systems, students will gain knowledge of their use and applications. The course includes activities for understanding the use of logics for knowledge representation and reasoning under uncertainty and with defeasible rules, verification, single and multi-agent information models, and for formal analyses of trustworthy and fair AI systems. Such notions and methods will be valuable in any activity requiring advanced reasoning and problem-solving abilities in the computational domain with particular reference to AI.

The course provides knowledge of formal methods and logics in the area of computing, information transmission, knowledge representation and verification, trustworthiness essential for the analysis and understanding of AI systems. The course provides basic knowledge and technical skills in the following topics:

- Logics of Program Correctness
- Process Algebra & Temporal Logics
- Multi-Agent Systems

- Reasoning under Uncertainty

- Trustworthy AI

Skills acquisition and ability to apply knowledge:

At the end of the course, students are expected to be able to:

- formally express reasoning about computational processes;
- formally describe informational structures and reason on them.

- formally model interacting systems under uncertainty and with defeasible rules

- formally define desirable properties of computational systems like fairness and trustworthiness.