A Heuristic for Problem Formalization in Agent Based Simulation Studies

Abstract

Abstract-Agent Based Modeling and Simulation (ABMS)is considered an effective approach for conducting simulation studies in many fields. In order to develop high quality simulation models, methodological approaches are demanded. In such direction we are moving by proposing a heuristic for the formalization of agent based simulation problems. The proposed heuristic is based on some guidelines developed for identifying the main elements of the problem domain description by analysing verbs and their common taxonomy in grammar.

Simulation Goals and Metrics Identification

“…For sure this approach depends on the presence of a good and well done Problem Ontology Diagram but, the GQM approach is a valid support that, once applied, also allows to reason on the problem domain and on the POD and potentially to refine it. The same we make with the Problem Formalization [8], both are iterative and incremental approaches that lead to the goal simulation model and at the same time allow to refine the POD towards a status where, reasonably, nothing has been omitted.…”

“…We already discussed how to perform problem formalization and the proposed results are shown in [8] [13]. The result of the Problem Formalization activity is a work product, the Problem Ontology diagram, containing all the elements representing the domain under study (see section I).…”

“…The second situation, instead, happens when the goal's object is a process or simply it has not been identified during Problem Formalization, in this latter case a refinement activity of the POD is necessary and should be performed. 1 The reader may have a look at [8] for a detailed example.…”

“…In so doing, we are building our approach according to the phases of classical simulation studies [1][2] but providing specialized guidelines for using agent based simulation techniques. So far, we developed the initial activities of Simulation Problem Analysis phase, namely Problem Formalization [8]. Problem formalization aims at formalizing simulation problems by adopting an ontological representation of the simulation problem domain.…”

Cognitive Communication Scheme for Unmanned Aerial Vehicle Operation

“…Apart of the above fundamental distributed network ar- The examples of such architectures are a two-level hierarchical architecture in [24], three-level hierarchical architecture in [25], randomly generated hierarchical architecture in [26]. In this paper, we assume that the CD component is capable of autonomous operation without relying on its neighbours, thus non-communication architecture is able to reduce system complexity.…”