If PLAN I,G) returns state-action table SA, then Σ SA is a consistent solution executor for the respective composition problem.
By directly applying the planning algorithm to planning domain Σ CF and planning goal ρ, we obtain the plan that is a consistent solution executor for a composition problem of Ψ and ρ.
The composition engine accepts as input a context C represented by context properties p 1,p 2,…,p m, a set of fragments ({F}^ =left {f_{1}^, f_{2}^,ldots,f_{n}^right }) annotated over C (together C and F + form a contextaware system Ψ=〈F +,C〉) and composition requirements ρ expressed as a context formula over C. The output is an executor Σ E that is a consistent solution executor for Ψ and ρ.
Once a planning problem is obtained, a consistent solution executor is derived by the algorithm for strong planning in asynchronous domain presented in [18].
Moreover, if such plan is not found, then a consistent solution executor for a given composition problem does not exist.
This mechanism is proposed to provide a consistent solution to those operators aiming at prioritizing voice traffic users over data traffic users in indoor femtocell environments, as the impact to the end user is more frustrating in voice calls than in data traffic.
The underlying idea is to form a hypothesis of the active and inactive transmission modes and check whether a consistent solution can be found or not.
Based on fundamental flow equations describing mass balance, energy conservation and momentum, a consistent solution is derived for natural ventilation by thermal buoyancy in a room with two openings and with uniform temperature.
The routine for searching a consistent solution executor is presented in Fig. 10.
At present, there is no methodology that provides alone a consistent solution to the problem of estimating mass eruption velocities, or ash plume heights.
