@INPROCEEDINGS { Rsds2264,
author = {Skowron, Andrzej and Suraj, Zbigniew}, 
title = {Discovery of Communicating Agent Systems from Experimental Data: A Rough Set Approach}, 
booktitle = {Proceedings of the 13th Biennial European Conference on Artificial Intelligence (ECAI 98)}, 
conference = {Workshop on Synthesis of Intelligent Agent Systems from Experimental Data}, 
pages = {76--90}, 
publisher = {University of Brighton}, 
address = {Brighton, UK}, 
year = 1998, 
editor = {Komorowski, Jan and Skowron, Andrzej and Duntsch, Ivo}, 
abstract = {We discuss a fully integrated approach to discovery of concurrent models from experimental data, presented in our papers [27], [28], [29], [30], [32], [33], [34], 35], [36], [21], separately. The concurrent models are specified by data tables in the form of information systems [16]. They are interpreted as communicating agent systems and represented by Petri nets. At first we present a new synthesis algorithm which constructs a marked Petri net from dependencies between the local states of agents of an information system. The proposed synthesis algorithm is easier to understand and simpler to implement than presented in [27]. Some new relationships of the knowledge represented by information systems and its Petri net models are also considered. Constructed nets make it possible to evaluate the behavior of communicating agent systems specified by information systems, and to trace computations in rules derived from information systems. In order to discover a communicating agent system from data table we formulate an algorithm which produces, from rules extracted from data table, a decomposition of data table into functional agents linked by communications. Finally, we describe a methodology for re-engineering of the synthesized communicating agentsystem when the specification for the system is being changed, e.g., by adding new requirements. The methodology is based on rough set theory.It can be treated as an adaptive strategy in re-engineering of communicating agent systems in the following sense: it allows to produce a plan of reconstruction of a given system by specifying which parts can remain unchanged and which must be changed to satisfy new requirements. It is also possible to estimate the cost of the reconstruction. Our ideas are illustrated by some simple examples. The results seem to have some significance for methods of information systems decomposition as well as can be applied in the system technology domain to adaptive reorganization of existing information systems [15], [6], [7], [9], [20].}, 
keywords = {communicating agent systems, system decomposition, rough sets, Petri nets, }, 
}