RuleCNL: A Controlled Natural Language for Business Rule Specifications

Business rules represent the primary means by which companies define their business, perform their actions in order to reach their objectives. Thus, they need to be expressed unambiguously to avoid inconsistencies between business stakeholders and formally in order to be machine-processed. A promising solution is the use of a controlled natural language (CNL) which is a good mediator between natural and formal languages. This paper presents RuleCNL, which is a CNL for defining business rules. Its core feature is the alignment of the business rule definition with the business vocabulary which ensures traceability and consistency with the business domain. The RuleCNL tool provides editors that assist end-users in the writing process and automatic mappings into the Semantics of Business Vocabulary and Business Rules (SBVR) standard. SBVR is grounded in first order logic and includes constructs called semantic formulations that structure the meaning of rules.Comment: 12 pages, 7 figures, Fourth Workshop on Controlled Natural Language (CNL 2014) Proceeding

The protection of the consumers’ interests in the car distribution

The present study gives an overview primary of the private law rules of the miscellaneous regulations of the consumer protection on the example of car distribution, presenting its civil law and competition law regulations.

The Nozick Game

In this article I introduce a simple classroom exercise intended to help students better understand Robert Nozick’s famous Wilt Chamberlain thought experiment. I outline the setup and rules of the Basic Version of the Game and explain its primary pedagogical benefits. I then offer several more sophisticated versions of the Game which can help to illustrate the difference between Nozick’s libertarianism and luck egalitarianism

On Redundancy Elimination Tolerant Scheduling Rules

In (Ferrucci, Pacini and Sessa, 1995) an extended form of resolution, called Reduced SLD resolution (RSLD), is introduced. In essence, an RSLD derivation is an SLD derivation such that redundancy elimination from resolvents is performed after each rewriting step. It is intuitive that redundancy elimination may have positive effects on derivation process. However, undesiderable effects are also possible. In particular, as shown in this paper, program termination as well as completeness of loop checking mechanisms via a given selection rule may be lost. The study of such effects has led us to an analysis of selection rule basic concepts, so that we have found convenient to move the attention from rules of atom selection to rules of atom scheduling. A priority mechanism for atom scheduling is built, where a priority is assigned to each atom in a resolvent, and primary importance is given to the event of arrival of new atoms from the body of the applied clause at rewriting time. This new computational model proves able to address the study of redundancy elimination effects, giving at the same time interesting insights into general properties of selection rules. As a matter of fact, a class of scheduling rules, namely the specialisation independent ones, is defined in the paper by using not trivial semantic arguments. As a quite surprising result, specialisation independent scheduling rules turn out to coincide with a class of rules which have an immediate structural characterisation (named stack-queue rules). Then we prove that such scheduling rules are tolerant to redundancy elimination, in the sense that neither program termination nor completeness of equality loop check is lost passing from SLD to RSLD.Comment: 53 pages, to appear on TPL

Construction of Neural Network Classification Expert Systems Using Switching Theory Algorithms

A new family of neural network architectures is presented. This family of architectures solves the problem of constructing and training minimal neural network classification expert systems by using switching theory. The primary insight that leads to the use of switching theory is that the problem of minimizing the number of rules and the number of IF statements (antecedents) per rule in a neural network expert system can be recast into the problem of minimizing the number of digital gates and the number of connections between digital gates in a Very Large Scale Integrated (VLSI) circuit. The rules that the neural network generates to perform a task are readily extractable from the network's weights and topology. Analysis and simulations on the Mushroom database illustrate the system's performance

Fiscal Responsibility Framework: International Experience and Implications for Hungary

In an effort to correct worrisome trends in discretionary fiscal policy (deficit bias, procyclicality, and structural distortions), an increasing number of countries introduced a rules-based fiscal responsibility framework (FRF), characterized by fiscal policy rules, procedural rules, transparency standards, and a surveillance and enforcement mechanism. Preliminary evidence suggests that compliance with a well-designed FRF contributes to building policy credibility, to reducing risk premia, to boosting economic growth, and to lowering output volatility. Faced with large and persistent fiscal imbalances and a sharp buildup of public indebtedness, Hungary would benefit from exploring the adoption a FRF along the following lines. The FRF should encompass the entire public sector, fully accounting for contingent liabilities, and including prudent fiscal projections. Second, it is necessary to strengthen procedural rules, including implementation of the pay-go approach to budget legislation and preparat on of a rolling three-year budget program, setting annual limits on the nominal level of primary expenditures. Third, phasing in of a primary surplus rule, calibrated to the path of desired debt reduction, should be seriously considered. Fourth, a current balance rule should be adopted for local self-governments. Finally, compliance with the FRF would need to be monitored by an independent authority.public finances, macroeconomics.