

![0_image_0.png](0_image_0.png)

During the interaction with this user, CTS learned the valences of each trace (Figure 3. Scenario1 and 2) with its nodes and all emotional valences being saved at the end of each execution. 

The average length of the stored sequences was of 26 events. 

One partial trace saved when CTS gave a hint (scenario 2) to User 3 was <(13, c11), (14, c14), (15, c15), (16, c18), (17, c19 e4 {0.8}>. In this trace, the positive valence 0.8 for emotion e4 
(compassion) was recorded because the learner answered to the Evaluation question correctly after receiving the hint. Thus during CTS's interaction with User 3, when User 3 gave the correct answer to CTS, the EM gave more positive valences to the scenario which might have been chosen in the future by EPL 
mechanism. In another partial trace saved by CTS, when User 3, having received some help, answered incorrectly to a question 
(scenario 1) <(16, c11), (17, c14), (18, c16), (19, c17), (20, c20 e2 {-0.4})>, CTS associated the negative valence -0.4 to emotion e2 (medium fear). After five executions, the memory consolidation phase extracted ten frequent event sequences, with a minimum support (minsup) higher than 0.25. This way, had the CTS have had to face the same problem in the future, it might have chosen between scenario 1 and 2. 

The episodic learning process evaluates all consolidated patterns and tries to detect the best pattern for each situation having ended by self-satisfaction (OCC model) in CTS. CTS chooses the pattern < (0, c11), (1, c18), (3, c18), (4, c19 e4 {0.8})>, 
because it contains the most positive emotional valence, has the highest frequency, and many of its events match with the current events executed in CTS. In our model, CTS will thus choose the path which gives it greatest self-satisfaction (scenario 2). When the emotional valence is not as positive as was the case in User 3, CTS might choose scenario 1 rather than scenario 2. Moreover, because the set of patterns is regenerated after each CTS cognitive cycle, some new patterns are created and others disappear. This may cause some changes in CTS's behavior. 

This suggests that CTS's architecture now adapts better to any 

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situation and dynamically learn how to associate an episode to one or more scenarios in the BN. It also suggests that it now learns how to choose the path that is the most probable of bringing self-satisfaction. 