Asignación y simulación de paternidad /maternidad

The estimated traveler model was implemented for acquiring both user needs and expectation, and providing a valuable service to users by exploiting the learned context.

An Adaptive Rank algorithm was developed aimed at proving tailored list of ranked travel solutions to each user.

The user model has been used to model each travel solution T S (i.e. each travel solution in output from a generic journey planner can be converted into features and action of the presented model), the collection of different travel solutions T Sk (k the number of travel solutions) for each query will form the

list to be ranked for tailoring the users’ experience. The collection of T Sk

modeled over the travel model has the following form:

T Sk(f1(ai), f2(aj), ..., f11(ay) (2.50)

The acquired user knowledge in terms of needs and preference, is therefore used to evaluate each travel solution by assigning a value.

As previously described for each action in each individual Q-Matrix (build by following the user model) we associate a weight, and then modeling each travel solution (as described in equation 2.50) we obtain a weight for each action in each feature.

Algorithm 3 Adaptive Rank Algorithm Initialization

Given

K=Number of solutions w=w-th Q-Matrix

i, j, y=active actions in each solution s=1,...,K

Compute

Model the set of solutions T SK into traveler model

T SK(f1(ai), f2(aj), ..., f11(ay)

Take the weights for each travel characteristic from the Q-Matrix as Ws(T SK) =

Qw(fj(ai))

Compute the weight for each TS following the equation T SK =PWs

W = Ws

Wsorted= sort(W)

The list of ranked travel solutions, according to the user needs, is therefore obtained by ordering the travel solutions by means of weights and considering a descending order, as described by the Algorithm 3.

Now, consider a generic user A who begins its first interaction with the system shown in figure 2.12. The learning algorithm such as Single user adaptive learning, designed for acquiring knowledge from the human-machine interaction, doesn’t know the user needs, since the user is at the first interaction with the system.

This means that, in case user A requests a generic trip, the system provides an initial random list of ranked travel solutions (according to the whole set of alternatives) waiting for a user first choice in order to acquire the first user A needs.

Figure 2.13 shows what the system has provided to the user. The set of travel solutions with a tailored random order is shown to the user (referring to top left part of the figure 2.13 ). In this respect, in the set of travel solutions depicted in figure 2.13, the user A selects the third solution, for instance. The selected solution contains paths with FOOT and CAR SHARING. The selection from user allows the system to learn the initial user needs.

Figure 2.14 shows the same solutions, according to the same request, with the difference that the solutions are ordered taking into account the user latest

2.4 RL Control approach applied to transportation system 63

Fig. 2.13 First set of random ranked travel solutions

choice. The adaptive rank algorithm has learned the initial preferences and in this second interaction (see figure 2.14) a more precise order is performed. In order to figure out the performances in terms of effectiveness of the tailored rank of solutions in Figure 2.15 a new set of solutions is shown and provided by the system to the user A. The set of solutions are returned to the user, still according to the acquired needs and preferences, in fact in the first position in 2.15 is still presented the CAR SHARING solution; the other solutions are ranked taking into account the 11 features already presented.

In case the user A selects the third solution in the list of 2.15, (METRO BUS) the system acquires again the user preferences and needs. A result of the last choice is presented in Figure 2.16 where the system returns the tailored list of ranked solutions according to the user A choices.

The effects of the human-machine interaction have result also in case the query is outside the urban area and covers long range distance as depicted in Figure 2.17 where the solutions are ranked according to the user A complete choices. The first solution in 2.17 from OSLO to ROME includes CAR SHARING for traveling in the Rome city as already selected by user A in its choices. In particular, the first solution within the whole set of solutions returned by the system contains: (1) CAR as first transport mean to reach the airport, (2) AIRPLANE to reach the Rome airport, (3) TRAIN to link the Rome airport with the Rome city center and (4) CAR SHARING path to reach destination. Furthermore, the fact that the system has learned the user A preferences is highlighted by the fifth ordered solution in the list. The fifth solution contains paths not desired by the user A taking into account the previous user A selections.

The results shown from figures 2.13 to 2.17 try to demonstrate how the system is able to learn preferences from the human-machine interaction and project the acquired knowledge for controlling and improving the users’ experience while interacting with the application by ordering in a personalized way the solutions for user A.

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Fig. 2.15 Third set of ranked travel solutions

Fig. 2.17 Fifth set of ranked travel solutions