![](bg4.png)
• Calculate time for task execution
3. Respond (decline, bid)
• A bid consists of the calculated time for the
execution of the task (welding)
• In case of available capability to execute the
task, reserve for the duration of negotiation
(defined by time-out of protocol) required
available capacity resources.
4. If the bid is accepted, then perform the task
(welding)
5. The welded car-body will be transported via
from the manager pre-reserved transport agent
to the destination agent (painting agent).
The critical step in the proposed protocol is step 2 of
contractor perspective: comparison of two
ontologies in order to determine whether they are
similar and whether the task can be executed from
the possible contractor. Several approaches for the
automated comparison of ontologies are summarized
by Gal, Modica & Jal 2003. One of the approaches
that can be used in the selected scenario is the
composition matching algorithm, which uses
linguistic matching. Another approach could be
comparison of schema and data format used in CAD
files.
5 PROPERTY OF PROPOSED
DESIGN AND CONCLUSIONS
The proposed design of production line factory with
multiagents enables treatment of failure by starting
on lowest hierarchy level – on production line agent
where the failure occurs. The agent, who detects
failure on its machine(s), initiates the process of
finding appropriate alternative agent with similar
capabilities and available resources. The
announcements are propagated step by step
hierarchically upwards, ensuring the “treat failure
local first” strategy, which ensures shortest transport
paths. This strategy is also selective and reduces
overflow of all Blackboards with announcements.
The hierarchy of production line to a factory can be
extended to composition of several factories to a
concern with different locations or even further to
consortium consisting of several concerns. The
information spread can be reduced further, if a
blackboard system on each level is aware of all
agents reading or writing on it. This can be done by
announcements made by each blackboard “who is
here with which abstract capability”, starting with
the lower level and proceeding with higher level and
so building routing tables. This enables to distribute
announcements more selectively (even to agents),
based on abstract capabilities (as topic), but requires
routing capability of blackboards. The task ontology
based evaluation of agent capabilities presumes no
common ontology and so only those agents which
are able to understand required task ontology (done
by checking similarity between ontologies) and able
to execute required task, answer with time
calculation for task execution. The calculation of
task execution in the bid on task announcement,
together with time for transport execution form
performance criteria for optimisation of the overall
performance and enables so distributed resource
scheduling. The usage of multiagent systems in
industrial manufacturing enables the factory to adapt
autonomous and fast to unknown problem, while
keeping production process running.
The proposed approach, based on ontological
matching and contract net protocol, could be used in
SLA-negotiation of BREIN project, if ontological
matching becomes a challenging issue in project.
The BREIN project aims at realizing an intelligent,
on multiagents, web-semantic and web-services
based infrastructure, capable to setup and manage
goal-driven dynamic virtual organization of service
provider/consumer, while optimizing usage of
resources.
ACKNOWLEDGEMENTS
This work has been supported by the BREIN project
(http://www.gridsforbusiness.eu) and has been partly
funded by the European Commission’s IST activity
of the 6th Framework Programme under contract
number 034556. This paper expresses the opinions
of the author and not necessarily those of the
European Commission. The European Commission
is not liable for any use that may be made of the
information contained in this paper.
REFERENCES
Weiss, G., 1999 Multiagent Systems, a modern approach
to distributed artificial intelligentce. The MIT press.
Huhns, M., N., Stephens, L., M., 1999. Multiagent
Systems and Societies of Agents.
Gal, A., Modica, G., Jamil, H., 2003. Improving Web
Search with Automatic Ontology Matching.
Wooldridge, M., 1999. Intelligent Agents, in Multiagent
Systems.
The BREIN Project, Website http://www.eu-brein.com/
The Wikipedia Website www.wikipedia.com
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