Normative design using inductive learning

TL;DR

This paper introduces an inductive learning-based iterative methodology for designing and revising normative frameworks, represented as logic programs, guided by use cases to ensure desired system behaviors.

Contribution

It presents a novel semi-automatic process for theory revision of normative frameworks using answer set programming guided by user-defined use cases.

Findings

Enables synthesis and revision of normative rules based on use case constraints.

Integrates a new methodology for theory revision within ASP.

Supports iterative refinement of normative frameworks through user-guided learning.

Abstract

In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductive logic programming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. The learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture possible scenarios, and (ii) a state that describes the desired outcome. The learning process then proposes additional rules, or changes to current rules, to satisfy the constraints expressed in the use cases. Thus, the contribution of this paper is a process for the elaboration and revision of a normative framework by means of a semi-automatic and iterative process driven from specifications of (un)desirable behaviour. The process integrates a novel and general methodology for theory revision based on ASP.