Automated multi-level governance compliance checking

An institution typically comprises constitutive rules, which give shape and meaning to social interactions and regulative rules, which prescribe agent behaviour in the society. Regulative rules guide social interaction, in particular when they are coupled with reward and punishment regulations that are enforced for (non-)compliance. Institution examples include legislation and contracts. Formal institutional reasoning frameworks automate ascribing social meaning to agent interaction and determining whether those actions have social meanings that comprise (non-)compliant behaviour. Yet, institutions do not just govern societies. Rather, in what is called multi-level governance, institutional designs at lower governance levels (e.g., national legislation at the national level) are governed by higher level institutions (e.g., directives, human rights charters and supranational agreements). When an institution design is found to be non-compliant, punishments can be issued by annulling the legislation or imposing fines on the responsible designers (i.e., government). In order to enforce multi-level governance, higher governance levels (e.g., courts applying human rights) must check lower level institution designs (e.g., national legislation) for compliance; in order to avoid punishment, lower governance levels (e.g., national governments) must check their institution designs are compliant with higher-level institutions before enactment. However, checking non-compliance of institution designs in multi-level governance is non-trivial. In particular, because institutions in multi-level governance operate at different levels of abstraction. Lower level institutions govern with concrete regulations whilst higher level institutions typically comprise increasingly vague and abstract regulations. To address this issue, in this paper we propose a formal framework with a novel semantics that defines compliance between concrete lower level institutions and abstract higher level institutions. The formal framework is complemented by a sound and complete computational framework that automates compliance checking, which we apply to a real-world case study

Augmented Worlds: a proposal for modelling and engineering pervasive mixed reality smart environments

In recent years, the remarkable technological advancement has revolutionised the ICT panorama offering nowadays the opportunity to exploit several technological convergences to reduce the gulf existing between the physical and the digital matter, between the physical real world and every computational software system or application. Along with Pervasive Computing - entered in the mainstream with the concept of Internet of Things (IoT) - Mixed Reality (MR) is going to be an essential ingredient for the design and development of next future smart environments. In particular, in such environments is feasible to imagine that the computation will drive the augmentation of the physical space, and software will also be executed in a cyber-physical world, eventually populated with of (interactive) holograms. After an initial exploration of the state of the art about augmentation technologies both for humans and the environment, in this dissertation we present the vision of Augmented Worlds (AW), a conceptual and a practical proposal for modelling and engineering next future pervasive mixed reality smart environments as distribute, multi-user and cooperative complex software systems. On the one hand, a meta-model is formalised and opportunely discussed to offer to the literature a conceptual tool for modelling AWs. On the other hand, also a concrete infrastructure - called MiRAgE - is designed and developed to produce a platform for engineering and deploy such innovative smart environments. The work carried out in this dissertation fits into the scientific literature and research of Pervasive Computing and Mixed Reality fields. Furthermore, part of the contribution is related also to the area of Cognitive Agents and Multi-Agent Systems, due to the AWs orientation to be deeply connected to a layer involving autonomous agents able to observe and act proactively in the smart environment