Several solutions for specifying normative artefacts (norms, contracts, policies) in a computational processable way have been presented in the literature. Legal core ontologies have been proposed to systematize concepts and relationships relevant to normative reasoning. However, no solution amongst those has achieved general acceptance, and no common ground (representational, computational) has been identified enabling us to easily compare them. Yet, all these efforts share the same motivation of representing normative directives, therefore it is plausible that there may be a representational model encompassing all of them. This presentation will introduce DPCL, a domain-specific language (DSL) for specifying higher-level policies (including norms, contracts, etc.), centred on Hohfeld's framework of fundamental legal concepts. DPCL has to be seen primarily as a "template", i.e. as an informational model for architectural reference, rather than a fully-fledged formal language; it aims to make explicit the general requirements that should be expected in a language for norm specification. In this respect, it goes rather in the direction of legal core ontologies, but differently from those, our proposal aims to keep the character of a DSL, rather than a set of axioms in a logical framework: it is meant to be cross-compiled to underlying languages/tools adequate to the type of target application. We provide here an overview of some of the language features.

Artificial Intelligence (AI) started out with an ambition to reproduce the human mind, but, as the sheer scale of that ambition became manifest, it quickly retreated into either studying specialized intelligent behaviours, or proposing over-arching architectural concepts for interfacing specialized intelligent behaviour components, conceived of as agents in a kind of organization. This agent-based modeling paradigm, in turn, proves to have interesting applications in understanding, simulating, and predicting the behaviour of social and legal structures on an aggregate level. For these reasons, this chapter examines a number of relevant cross-cutting concerns, conceptualizations, modeling problems and design challenges in large-scale distributed Artificial Intelligence, as well as in institutional systems, and identifies potential grounds for novel advances.