Marc van Zee

Formalizing Enterprise Architecture Decision Models Using Integrity Constraints

The aim of this paper is to introduce and validate a logic-based framework that serves as the underlying model for a recently introduced formalism for capturing enterprise architecture design decisions by Plataniotis et al. Our working hypothesis is that capturing of design knowledge in terms of a logic-based framework will enable consistency checks of the underlying rationales and advanced impact/what-if analysis when confronted with changes. We formalize a set of integrity constraints, which allow guidance of decision capturing during model creation and provide means to perform consistency checks. We apply our formal framework to a practical case study from the insurance sector.

Rationalization of goal models in GRL using formal argumentation

We apply an existing formal framework for practical reasoning with arguments and evidence to the Goal-oriented Requirements Language (GRL), which is part of the User Requirements Notation (URN). This formal framework serves as a rationalization for elements in a GRL model: using attack relations between arguments we can automatically compute the acceptability status of elements in a GRL model, based on the acceptability status of their underlying arguments and the evidence. We integrate the formal framework into the GRL metamodel and we set out a research to further develop this framework.

Formalizing and Modeling Enterprise Architecture (EA) Principles with Goal-oriented Requirements Language (GRL)

Enterprise Architecture (EA) principles are normally written in natural language which makes them informal, hard to evaluate and complicates tracing them to the actual goals of the organization. In this paper, we present a set of requirements for improving the clarity of definitions and develop a framework to formalize EA principles with a semi-formal language, namely the Goal-oriented Requirements Language (GRL). We introduce an extension of the language with the required constructs and establish modeling rules and constraints. This allows us to automatically reason about the soundness, completeness and consistency of a set of EA principles. We demonstrate our methodology with a case study from a governmental organization. Moreover, we extend an Eclipse-based tool.

RationalGRL: A Framework for Rationalizing Goal Models Using Argument Diagrams

Goal modeling languages, such as i* and the Goal-oriented Requirements Language (GRL), capture and analyze high-level goals and their relationships with lower level goals and tasks. However, in such models, the rationalization behind these goals and tasks and the selection of alternatives are usually left implicit. To better integrate goal models and their rationalization, we develop the RationalGRL framework, in which argument diagrams can be mapped to goal models. Moreover, we integrate the result of the evaluation of arguments and their counterarguments with GRL initial satisfaction values. We develop an interface between the argument web tools OVA and TOAST and the Eclipse-based tool for GRL called jUCMNav. We demonstrate our methodology with a case study from the Schiphol Group.

On the Semantic Feature Structure of Modeling Concepts: An Empirical Study

Is an actor typically considered a human being? What about an autonomous entity? We investigate the typical feature structure of common modeling concepts in order to create an empirically grounded description of the semantic feature structure that people implicitly use while reasoning about, and with such concepts. Apart from the insights into modeling concept structure that this work presents, consequences for the quality of models and use of modeling languages are discussed. We finally discuss in more detail how the process of modeling, especially when it involves multiple people with different backgrounds, modeling different aspects (i.e., enterprise modeling), stands to benefit from more insights into how the individual modelers see the basic modeling concepts shared between them.

Belief caching in 2APL

The BDI-oriented multi-agent programming language 2APL allows the implementation of an agents beliefs in terms of logical facts and rules. An agents beliefs represent information about its surrounding environment including other agents. Repeated querying of the beliefs by the 2APL interpreter causes unnecessary overhead resulting in poor run-time performance of the interpreter. We propose an extension to 2APL to reduce the number of such queries by using belief caching. We show that our proposal implements belief caching and extends an existing caching proposal. Moreover, we provide formal proofs establishing that our extension does not affect the execution behavior of 2APL. Benchmarking results indicate that belief caching leads to significant improvements.

Rational Enterprise Architecture

We are interested in formal foundations for enterprise decision support. In this perspective, enterprise architecture is characterised by highly uncertain plans in a changing environment, and translates strategic goals into an IT strategy. Typically there are a large number of stakeholders with conflicting views, communicating plans of action, and explaining decisions instead of making them. An enterprise architecture considers qualitative before quantitative data, has stronger business focus than other disciplines, and politics, emotions, and soft skills play a bigger role than in other areas. We view a plan abstractly as a sequence of commitments in time, and each commitment in the plan may come with a number of underlying assumptions. If these underlying assumptions change, then parts of the plan may require revision, which in turn may invalidate other parts of the plan, and so on. Therefore, assumptions have an inherently non-monotonic character: they are assumed to be true, unless it becomes clear they are false. This is related to the resource-boundedness of enterprise architecture: an enterprise architect cannot always know all of the assumptions, especially for long term plans.

Formalising Enterprise Architecture Decision Models

In this we introduce and validate a logic-based framework that serves as the underlying model for Chap. 23 The resulting logic-based framework aims to address some of the challenges identified in Chap. 15

Bridging Social Network Analysis and Judgment Aggregation

Judgment aggregation investigates the problem of how to aggregate several individuals’ judgments on some logically connected propositions into a consistent collective judgment. The majority of work in judgment aggregation is devoted to studying impossibility results, but the relationship between the (social) dependencies that may exist between voters and the outcome of the voting process is traditionally not studied. In this paper, we use techniques from social network analysis to characterize the relations between the individuals participating in a judgment aggregation problem by analysing the similarity between their judgments in terms of social networks. We obtain a correspondence between a voting rule in judgment aggregation and a centrality measure from social network analysis and we motivate our claims by an empirical analysis. We also show how large social networks can be simplified by grouping individuals with the same voting behavior.