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2 MOTIVATION
A typical planner has been seen as a producer of ac-
tion sequences that requires the following inputs: (i)
an initial state of the world; (ii) a description of a
goal state; and (iii) a set of possible actions, which
can be used in the generated plan to lead the agent
from the initial state to the goal state. As the need
for more processing power (for increasingly complex
problems) arose and the inadequacy of centralised ap-
proaches became evident, researchers turned to Dis-
tributed Problem Solving. However, taking advantage
of the decentralised control of such distributed envi-
ronments requires that coordination mechanisms exist
that are able to avoid conflicts that arise from the con-
current interactions of agents, which otherwise would
result in a turmoil.
The coordination process can be undertaken at dif-
ferent times and situations, depending on what is suit-
able for the specific domain to which it applies. In
spite of the variety of approaches, domains and con-
texts, we have concluded that they have the same
kind of limitations. Whether they rely on some sort
of centralised component or on a pre-defined struc-
ture/knowledge that rules the activity of all entities
in the environment, these approaches show signs of
compromised scalability and robustness.
Most task refinement and task allocation ap-
proaches rely on centralised components, such as
blackboards (Wellman, 1993), tables of capabilities
(Fung and Chen, 2005) or broker like auctioneers
(Wellman et al., 2001). An obvious limitation of these
approaches in very large networks is its non scalabil-
ity. Other approaches (de Weerdt et al., 2007) propose
the use of social structures to govern the task alloca-
tion process, but again these have to be provided a
priori by some human user.
The same limitations apply to planning coordina-
tion. Whether coordination is performed before, dur-
ing or after planning, most approaches rely on some
pre defined structure or some centralised component
to govern the activity of the agents in the environ-
ment. Social laws (Shoham and Tennenholtz, 1992)
and other organizational based approaches (Abdallah
and Lesser, 2004) (Gaston and Desjardins, 2005) are
examples of systems where sets of rules or organiza-
tional structures are imposed to the agents societies.
Alternative approaches (Cox et al., 2003) use fully
connected networks to allow agents to know which
and when agents should be contacted. However, this
kind of network topology is very difficult to manage
in large dynamic networks where high churn rates (the
rate at which agents enter or leave a network) make
the approach non scalable. Centralisation is often the
choice for most domains, whether it is by allowing
agents to coordinate themselves through an advertis-
ing blackboard (de Weerdt and Van Der Krogt, 2002),
or by using specialised central agents to perform post-
planning coordination (Cox and Durfee, 2005).
3 TECHNICAL APPROACH
By using a matchmaking process that analyses each
agents operators and establishes links between in-
puts/outputs and pre-conditions/effects of the opera-
tions that can be done by other agents (see details in
(Lopes and Botelho, 2008)), we dynamically build a
powerful Semantic Overlay Network. This abstract
layer subsumes the discovery process of the coordi-
nation environment by helping agents find each other
based on their semantic relationships. However, for
an agent to create a plan to solve a specific problem,
it still needs a planning algorithm that is capable of
dealing with partial knowledge of the domain opera-
tors and deriving the potential contributions that can
be done by the agent to the referred problem. Our
work focuses on finding the appropriate planning al-
gorithm for this distributed environment.
We have studied several algorithms and decided
to use the well-known Graphplan planner (Blum and
Furst, 1997) because it is one of the best planning
algorithms and has been used as the basis for other
even more efficient planning algorithms. We have
created different versions of the planner so we could
test its application to different distributed problems.
In section 3.1, we present our distributed version
of the Graphplan algorithm. Section 3.2 describes
an alternative version of the distributed Graphplan
algorithm that previously builds an operators-graph
through means-ends analysis.
3.1 Distributed Graphplan
In a centralised version of the Graphplan algorithm
an agent has full knowledge of the available opera-
tors. However, in a distributed setting, each agent may
only have knowledge of its own operators and of those
of selected neighbours or, in the case of the Seman-
tic Overlay Network, of agents that are semantically-
linked to it. So, we modified the algorithm to allow
partial contributions to the creation of the planning-
graph. The process is carried out as follows:
• The agent receiving a request (which includes a
description of the initial state and the goal state)
will analyse each level and determine which of
the operators (that it knows) can contribute to the
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