Through the Foundations for Meaning and Understanding in Human-centric AI, the MUHAI project offers an in-depth and integrated overview of narratives and understanding in different disciplines and research fields.  The volume builds upon recent insights and findings from social and cognitive sciences, humanities and other fields for which narratives have been found to play a relevant role in human understanding and decision-making processes. This, to map the state-of-the-art of narrative-centric studies and to identify the most promising research streams for tomorrow’s AI.

When people hear the word "grammar", most of them still think about a set of syntactic rules to combine words (and their concepts) in a compositional fashion. Most NLP systems therefore consider grammar to be equal to syntactic parsing, so syntax is simply one of the components of a traditional pipeline that can be useful for downstream tasks. In the MUHAI project, we take a different approach inspired by cognitive-functional linguistics, in which grammar itself is meaningful: it expresses how people conceptualise reality, and which perspective they take on the events that they perceive in their daily lives.

Performing household activities such as cooking and cleaning have, until recently, been the exclusive provenance of human participants. However, the development of robotic agents that can perform different tasks of increasing complexity is slowly changing this state of affairs, creating new opportunities in the domain of household robotics. Most commonly, any robot activity starts with the robot receiving directions or commands for that specific activity. Today, this is mostly done via programming languages or pre-defined user interfaces, but this changes rapidly.

Over the last decade, conversational agents have slowly but surely become very common in our daily lives. Conversational agents - these are computer agents, robots or software which can understand and talk to a user in natural language - include assistants on our smartphones like Siri or Google Assistant, voice-controlled smart home devices like Google Home and Amazon Alexa, as well as online chatbots like the ones frequently used in customer service.

Babies, puppies, kittens may be bundles of joy but they are also agents of pure chaos. They knock things over, stick their fingers where they're not supposed to, and get a taste or sniff of anything they can, all just for fun of course. Or is it "just for fun"? In the moment, it is, but this playfulness of infants also allows them to build the intuitive models of the physical world which are needed to cope with that world "seriously".

One of the reasons why it is so difficult to develop human-centric AI systems is that such systems need to "understand" the world and human activities in a way that is compatible with how humans make sense of the world.

Inequality-related narratives can be created, circulated and employed at two distinct but intertwined debate levels. The first one is made by scholarly and scientific debates, which are mostly carried out by academic researchers and field experts, specialised in the measurement, analysis and modeling of specific types of inequalities, like inequalities of access to health and care services.

The activities of people as well as of artificial agents in reality, virtual reality or simulation can be recorded as data that discretize trajectories of body parts and the ensuing force events. While these data provide vast amounts of information they are, by themselves, meaningless.

We are constantly building a posteriori stories about how events happened, and about how you can make connections to come up with a coherent whole. To some extent, it is an extension of the Five W's - Who? What? When? Where? Why? -, the five questions that are considered the most basic in problem solving.

Within the framework of "Aqua Granda, a digital community memory" initiative, launched in November 2020 by the EU H2020 ODYCCEUS project and Science Gallery Venice, among the main outputs is a book, available in open access.

Part of the MUHAI objectives is developing tools to help humans understand media materials, such as tweets or  articles, on critical social issues, in particular socio-economic inequality.

AI (Artificial Intelligence) researchers try to understand the structures and processes that underlie intelligence and use that insight to build practical applications. Much has already been achieved. But much remains to be discovered.