ARTOR System
Organizations, as any complex and inherently
distributed entities, are characterized by their internal and external
interactions. Generally, and as a result of the continuous interactive process,
the involved organizations become more efficient. This performance increase,
achieved through resources optimization, can be seen as the outcome of a know-
how acquired from previous interactions. In broad terms, our work can be
classified as the study and modeling of the behavior of organizations. Currently
, we are concerned with a specific inter-organization relation: the selection
process that leads to the establishment of contracts between organizations. This
selection process can be characterized as an iterative loop composed of an
evaluation phase followed by a negotiation phase. During the selection activity,
conflicts may occur imposing further negotiation as a mean for conflict
resolution. According to the diverse selection methodologies that can be adopted
, different learning opportunities can also be detected. The computational
system under development, which supports the above mentioned interaction
processes, is called ARTOR (ARTificial ORganizations)
, and is based on the Distributed Artificial Intelligence - Multi-Agent Systems
(DAI-MAS) and Symbolic Learning (SL) paradigms. Each component, or agent, is
provided with the needed observation, planning, coordination, execution,
communication and learning capabilities to perform its social role.
Marcos got his PhD at the University of Porto, Portugal in 1999 with a thesis on this subject (Artificial Organisations). He is now in Brasil at PUC/Curitiba.
Please contact:
Marcos A. H. Shmeil email: [email protected]
Eugenio Oliveira email: [email protected]
smail: NIAD&R, DEEC, FEUP, Rua dos Bragas, 4099 PORTO CODEX, PORTUGAL
Tel: +351 2 2041849
Fax: +351 2 319280