Belief revision is a critical issue in real world DAI applications.
A Multi-Agent System not only has to cope with the intrinsic incompleteness
and the constant change of the available knowledge (as in the case of its stand
alone counterparts), but also has to deal with possible conflicts between the
agents’ perspectives. Each semi-autonomous agent, designed as a combination
of a problem solver – assumption based truth maintenance system (ATMS),
was enriched with improved capabilities: a distributed context management facility
allowing the user to dynamically focus on the more pertinent contexts,
and a distributed belief revision algorithm with two levels of consistency. This
work contributions include: (i) a concise representation of the shared external
facts; (ii) a simple and innovative methodology to achieve distributed context
management; and (iii) a reduced inter-agent data exchange format. The different
levels of consistency adopted were based on the relevance of the data under
consideration: higher relevance data (detected inconsistencies) was granted
global consistency while less relevant data (system facts) was assigned local
consistency. These abilities are fully supported by the ATMS standard functionalities
