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184 Selected Studies on Software and Information Systems
The aim of web service composition is to solve problems like the creation of the meteorological
forecast maps described above. We have no service to achieve our goal, but we can
compose it from sub-services. There are two ways in web service composition. In first, the
composition is created manually, i.e. somebody creates it. The second way is the automatic
composition of services. Here, the composition method automatically creates a composed
service based on a defined goal. These approaches use AI (Artificial Intelligence) planning
techniques [68].
The approaches based on AI planning convert the web service composition problem to
AI planning problem [67]. Then several existing approaches can be used to solve it. Using
these approaches we can create a plan which determines the action sequence leading from
the initial state to the goal state. The creation of the plan does not solve the whole problem.
The found sequence does not need to bring the desired results. We are successful only if we
get the required output data by execution of this sequence. This means that also the data flow
between the actions must be correct, i.e. the successive services are compatible on I/O data
level and input data for each service has defined source. To summarize, we are looking for
a composite service which execution in the initial state leads to the goal state and desired data.
The composers work with different formalisms to encode and solve the planning problem
[58]. Here belong the pure backward chaining, any of the search space algorithms or the
most trivial shortest path search. Real semantic web service composers usually combine and
enhance these algorithms. Their aim is to reduce the search space to find a plan in acceptable
time. In the following we take a brief overview of the approaches formalizing the web service
composition problem or are used to find its solution.
State-Space Based Planning
The web service composition problem based on state-space planning is described by a fivetuple
〈S, s 0 ,G,A,Γ〉, where S is a set of the possible states of the world, s 0 is the initial state,
G denotes the goal state which we want to reach, A is the set of actions the planner can use
to change one state to other, and Γ is the set of translation relations Γ ⊆ S × A × S defining
the preconditions and effects for the execution of each action, it maps one state into another.
The goal of the planning is to find such a sequence of actions which when are applied
to the initial state, will lead to the goal state. Formally, we look for a sequence a 0 ,...,a n ,
where a i ∈ A, ∀i =0,...,nwhich generates a state trajectory s 0 ,...,s n+1 , where s i ∈ S, ∀i =
0,...,n+1and s n+1 = G. In each step, there must exist γ i ∈ Γ,γ i =(s i ,a i ,s i+1 ), ∀i =
0,...,n.
The transition relations can by related to a cost function c(a, s) > 0. This function
measures the cost of execution the action a in state s. The aim is to find the plan with the
lowest cumulative cost computed from the costs of each state transition of the plan.
State-space based search can use any of the space search algorithms. The aim of these
algorithms is to reduce the search space which has 2 p states, where p is the number of
propositions. To find a state transition path a simple shortest path algorithm can by used
solving the problem in O(m log m) time, where m =2 p [21]. Considering a problem where we
have 20 propositions, the search space consists from 2 20 = 1 048 576 states. In reality, we often
have more hundreds of propositions. This seriously increases the number of states. Hence,
the simple algorithms are not usable. A lot of known algorithms try to reduce the search