Students should be able to:

• Comprehend that capturing the social aspects of human behaviour is essential in understanding how people think and how people react to each other, which is a fundamental step to developing reasoning algorithms that can operate effectively in social contexts.
• Demonstrate a good understanding of computational models of social reality. That is, how social contexts determine human behaviour through norms, practices, conventions, rituals and other rules of human social nature.
• Understand current methodologies to model social cognition, collaboration and teamwork.
• Understand /describe theoretical models for cooperation between agents.
• Understand the process of creating systems equipped with perception and social capabilities that allows them to adapt to different social contexts and learn from other agents in such environments.
• Understand how models of social reality generate emergent behaviour and the impact of such models in agent societies and social networks of multi-agent systems.

• Correctly identify different ways to sense the environment and understand how to use off-the-shelf solutions and how to make sense of the captured data.
• Explore the creation of a simple Social AI System, using a perception technology whose data feeds into a reasoning mechanism that outputs social (and intelligent) acts in a context of choice.
• Evaluate social reasoning and learning algorithms in the form of simulations or with a human.
• Analyse the solutions to a problem and critically think about the societal impact.

• Work effectively with others in an interdisciplinary and/or international team.
• Design and manage individual projects.
• Clearly and succinctly communicate their ideas to technical audiences.