Simulation and Integration Sample Clauses

Simulation and Integration. ‌ In this section, we express the main traits of the robotics scenario into the ASCENS jargon introduced in WP4 over the course of the project. Subsequently, we sketch the general structure a solution for this scenario must possess.
Sample 1
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Simulation and Integration. ‌ 4.3.2 highlights how tools and methods from technical WPs can be used to facilitate the development of the scenario simulation. 4.3.1 Optimization of individual vs. global goals‌ Besides optimizing local resources for example by finding the best trips and journeys for each vehicle [MMH12], the e-Mobility case study aims at solving global problems, involving large ensembles of different vehicles. Such large problems tend to be complex to solve and often a globally optimal solution may be impossible to find. For this reason specific strategies are needed to solve them. We propose a technique based on the coordination of declarative and procedural knowledge, as discussed in [Mon13]. It consists of decomposing the global optimization problem in many local problems which can be separately solved by a Soft Constraint Logic Programming (SCLP) implemen- tation and which are coordinated by suitable procedural strategies acting at run time on the declarative optimization environment to guarantee an acceptable global solution. Here the use of SCLP is con- venient for two reasons: (1) it allows one to naturally model and solve local optimization problems (see for example [MMH12]); (2) a fact/clause-based declarative implementation is more flexible and easier to modify than an ordinary imperative module structure. Let us consider for example the parking optimization problem [ASC11] consisting of finding the best parking lot for each vehicle of an ensemble. The best parking lot is chosen by considering: the distance from the current location of the vehicle to the parking lot, the distance from the parking lot to the appointment location and the cost of the parking lot. The application of the coordination technique described above to this problem leads to several local optimization problems, one for each vehicle of the ensemble, consisting in determining the best parking lot for it. All these local problems are solved separately by using a SCLP implementation. The orchestrator implementing the coordination strategy then receives the results of all the local op- timization solutions and verifies if the local solutions all together form an admissible global solution, i.e., if local optimal choices can be satisfied by the parking lots. If this is not the case, the declarative knowledge is changed by increasing the cost of the parking lots which received too many requests and the local optimizations are again computed. The procedure, possibly conveniently modified, is repe...
Sample 1

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