![](bg4.png)
continuously perceive the environment, and make
real-time decisions on the process. Agent-based
software entity is able to sense and interpret the
situations and determine appropriate actions upon
the situations based on predefined rules or
knowledge. Moreover, AOC supports prediction of
future state of the environment for purpose of
proactive actions. Different from passive response to
current events, proactive behavior has an orientation
to the future, anticipating problems and taking
affirmative steps to deal positively with them rather
than reacting after a situation has already occurred.
It refers to the exhibition of goal-oriented behaviors
by taking initiatives (Wang et al., 2006, 2002).
AOC for Resource Coordination. A business
process can be dynamically established by
connecting or composing Web services provided by
different organizations over the Internet. However, it
is a complex problem to search appropriated services
from a large number of resources as well as schedule
and coordinate them under various constraints. The
complexity arises from the unpredictability of
solutions from service providers (e.g. availability,
capacity, and price), and constraints on services in a
process (e.g. temporal relationship, time and cost
constraint). To deal with the problem, AOC can be
used for coordination based on distributed decision
making. In process integration, decision and
coordination among services can be modelled as a
distributed constraint satisfaction problem, in which
solutions and constraints are distributed into a set of
services and to be solved by a group of agents
(brokers) on behalf of service requesters and
providers. Finding a global solution to the integrated
process requires that all agents find the solutions that
satisfy not only their own constraints but also inter-
agent constraints (Wang et al., 2008). In this
situation, the special type of agent works as an
intermediary between service requester and service
provider, coordinating on behalf of two parties
regarding service requirements, qualities, costs, and
other constraints.
5 CONCLUSIONS
This work has investigated the rationale for
developing solutions to complex process
management by using agent-oriented technologies.
The main problems and key solutions for complex
process management are examined. Meanwhile, the
mechanisms of Agent-Oriented Computing, together
with its relationship with BPM systems development
are clarified. Based on this, we present a clear
picture on why and how Agent-Oriented Computing
technology can be applied to developing process
management systems, especially in complex
situations. This work will benefit professionals,
researchers, and practitioners by guidelines and
methods for designing and developing technical
solutions to complex business process management.
ACKNOWLEDGEMENTS
This research is supported by a UGC CERG
research grant (No. RGC/HKU7169/07E) from the
Hong Kong SAR Government, and a Seed Funding
for Basic Research (200611159216) from The
University of Hong Kong.
REFERENCES
Jennings, N.R., Faratin, P., Norman, T.J., O'Brien, P., &
Odgers, B., 2000. Autonomous Agents for Business
Process Management. International Journal of
Applied Artificial Intelligence, 14 (2), 145-189.
Kumar, K., & Narasipuram, M.M., 2006. Defining
Requirements for Business Process Flexibility.
Seventh Workshop on Business Process Modeling,
Development, and Support, CAiSE 2006
Mintzberg, H., The Structuring of Organizations, New
Jersey, Prentice Hall, 1979
Mowshowitz, A., 1997. Virtual organization.
Communications of the ACM, 40 (9), 30-37.
Simon, H.A., 1981. The sciences of the Artificial, MIT
Press, Cambridge, Massachusetts, London, England.
van der Aalst, W.M.P., & van Hee, K.M., 2002. Workflow
Management: Models, Methods, and Systems, MIT
press, Cambridge, MA.
Wang, M., & Wang, H., 2002. Intelligent Agents
Supported Flexible Workflow Monitoring System.
Proceedings of the14th International Conference on
Advanced Information Systems Engineering (CAiSE),
Toronto, Lecture Notes in Computer Science,
Springer-Verlag, 2348, 787-791.
Wang, M., Liu, J., Wang, H., Cheung, W., & Xie, X.,
2008. On-demand E-Supply Chain Integration: A
Multi-Agent Constraint-Based Approach. Expert
Systems with Applications, 34(4), 2683-2692.
Wang, M., & Wang, H., From Process Logic to Business
Logic - A Cognitive Approach to Business Process
Management. Information & Management, 43(2), 179-
193.
Wooldridge, M., 2002. An introduction to multiagent
systems, England, J. Wiley.
ICEIS 2008 - International Conference on Enterprise Information Systems
206