Name
Abstract | ||
|---|---|---|
This paper introduces a new methodology based on the use of Pathfinder networks (PFNETs) for the debugging of multi-agent systems (MASs). This methodology is specifically designed to develop a forensic analysis (i.e. a debugging process performed on previously recorded data of the MAS run) of MASs showing complex tissues of relationships between agents (i.e. a high complexity in their social level). Like previous works in the field of forensic analysis of MASs, our approach is performed by considering displays of the system activity which aim to be understandable by human beings. These displays allow us to understand the social behavior of the system, discover emergent behaviors, and debug possible undesirable behaviors. However, it is well known that the visualization of information in a humanly comprehensible way becomes a complex task when large amounts of information have to be represented, as is the case of the social behavior of large-scale MASs. Our methodology tackles this problem through the use of PFNETs, which are considered to reduce the data complexity in order to obtain simple representations that show only the most important global interactions in the system. In addition, the proposed methodology is customizable thanks to the use of two thresholds allowing the user to define the desired specificity level in the display. The proposal is illustrated with a detailed case study considering a complex customer-seller MAS. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.ins.2009.11.007 | Inf. Sci. |
Keywords | Field | DocType |
proposed methodology,new methodology,multi-agent systems debugging forensic analysis pathfinder networks complex systems,multi-agent system,complex software system,pathfinder network,large-scale mass,complex task,social level,social behavior,complex tissue,complex customer-seller,forensic analysis,software systems,debugging,complex systems,complex system,multi agent system,multi agent systems,emergent behavior | Complex system,Pathfinder,Visualization,Computer science,As is,Software system,Multi-agent system,Artificial intelligence,Machine learning,Debugging,Data complexity | Journal |
Volume | Issue | ISSN |
180 | 5 | 0020-0255 |
Citations | PageRank | References |
18 | 0.94 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emilio Serrano | 1 | 121 | 12.53 |
| Arnaud Quirin | 2 | 168 | 13.68 |
| Juan Botia | 3 | 70 | 3.73 |
| Oscar Cordón | 4 | 1572 | 100.75 |