Normative design using inductive learning

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.Comment: Theory and Practice of Logic Programming, 27th Int'l. Conference on Logic Programming (ICLP'11) Special Issue, volume 11, issue 4-5, 201

The Complexity of Data-Driven Norm Synthesis and Revision

Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, designing norms to achieve a particular system objective can be difficult, particularly when there is no direct link between the language in which the system objective is stated and the language in which the norms can be expressed. In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete

Mining International Political Norms from the GDELT Database

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society. This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events extracted from news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.Comment: 16 pages, 2 figures, pre-print for International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 202

Do agents dream of abiding by the rules?:Learning norms via behavioral exploration and sparse human supervision

In recent years, several normative systems have been presented in the literature. Relying on formal methods, these systems support the encoding of legal rules into machine-readable formats, enabling, e.g. to check whether a certain workflow satisfies or agents abide by these rules. However, not all rules can be easily expressed (see for instance the unclear boundaries between tax planning and tax avoidance). The paper introduces a framework for norm identification and norm induction that automates the formalization of norms about non-compliant behavior by exploring the behavioral space via simulation, and integrating inputs from humans via active learning. The proposed problem formulation sets also a bridge between AI &amp; law and more general branches of AI concerned by the adaptation of artificial agents to human directives.</p

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

Inductive logic programming using bounded hypothesis space

Inductive Logic Programming (ILP) systems apply inductive learning to an inductive learning task by deriving a hypothesis which explains the given examples. Applying ILP systems to real applications poses many challenges as they require large search space, noise is present in the learning task, and in domains such as software engineering hypotheses are required to satisfy domain specific syntactic constraints. ILP systems use language biases to define the hypothesis space, and learning can be seen as a search within the defined hypothesis space. Past systems apply search heuristics to traverse across a large hypothesis space. This is unsuitable for systems implemented using Answer Set Programming (ASP), for which scalability is a constraint as the hypothesis space will need to be grounded by the ASP solver prior to solving the learning task, making them unable to solve large learning tasks. This work explores how to learn using bounded hypothesis spaces and iterative refinement. Hypotheses that explain all examples are learnt by refining smaller partial hypotheses. This improves the scalability of ASP based systems as the learning task is split into multiple smaller manageable refinement tasks. The thesis presents how syntactic integrity constraints on the hypothesis space can be used to strengthen hypothesis selection criteria, removing hypotheses with undesirable structure. The notion of constraint-driven bias is introduced, where hypotheses are required to be acceptable with respect to the given meta-level integrity constraints. Building upon the ILP system ASPAL, the system RASPAL which learns through iterative hypothesis refinement is implemented. RASPAL's algorithm is proven, under certain assumptions, to be complete and consistent. Both systems have been applied to a case study in learning user's behaviours from data collected from their mobile usage. This demonstrates their capability for learning with noise, and the difference in their efficiency. Constraint-driven bias has been implemented for both systems, and applied to a task in specification revision, and in learning stratified programs.Open Acces