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Figure 10. Planning system evolution. The efficiency of the system and the learning rate are both

Figure 10 Planning system evolution. The efficiency of the system and the learning rate are both

Related Figures (15)

Figure 1. Shopping Mall scenario. or shoppers.
Figure 1. Shopping Mall scenario. or shoppers.
Figure 2. Guiding agent, Shop agents and User agents. agents.
Figure 2. Guiding agent, Shop agents and User agents. agents.
Figure 3. Gaia Incidents Manager role model for the shopping mall problem. particular attributes: responsibilities, permission, activities and protocols [21].
Figure 3. Gaia Incidents Manager role model for the shopping mall problem. particular attributes: responsibilities, permission, activities and protocols [21].
Figure 4. Gaia agent model for the shopping mall problem. After applying Gaia, the result is a high-level abstraction design. At this point the Gaia design is transformed so that AUML techniques can be applied. The AUML design provides class diagrams for each agent, collaboration or sequence diagrams for each interaction, state and activity diagrams to
Figure 4. Gaia agent model for the shopping mall problem. After applying Gaia, the result is a high-level abstraction design. At this point the Gaia design is transformed so that AUML techniques can be applied. The AUML design provides class diagrams for each agent, collaboration or sequence diagrams for each interaction, state and activity diagrams to
Figure 5. Guiding agent class diagram. to the user profile and available resources at one particular moment
Figure 5. Guiding agent class diagram. to the user profile and available resources at one particular moment
Table 5. Route definition for a guidance suggestion When a client purchases a product, the following information is stored: date, money spent, product and shop details. the client, arrival time, the time spent in the shop, the products consumed by the client, and the
Table 5. Route definition for a guidance suggestion When a client purchases a product, the following information is stored: date, money spent, product and shop details. the client, arrival time, the time spent in the shop, the products consumed by the client, and the
Figure 6. Weighted SOM neural network. the output neuron j, and finally y; represents the output value obtained from the neuron j. As can be seen in Figure 6, in the first phase the input value of the client profile is standardized (2). not. S;represents the input for the neuron i, p, represents the weight which links the input neuron i and
Figure 6. Weighted SOM neural network. the output neuron j, and finally y; represents the output value obtained from the neuron j. As can be seen in Figure 6, in the first phase the input value of the client profile is standardized (2). not. S;represents the input for the neuron i, p, represents the weight which links the input neuron i and
input values according to their importance in the classification (6). When the values have been calculated, predictions can be made. The predictions will indicate the described in the following equations. The objective of this weight is to weigh the distances of client’s preference degree for a product. A high value in the resulting prediction indicates a high probability of a client’s interest in a product. The prediction of the output value is calculated a
input values according to their importance in the classification (6). When the values have been calculated, predictions can be made. The predictions will indicate the described in the following equations. The objective of this weight is to weigh the distances of client’s preference degree for a product. A high value in the resulting prediction indicates a high probability of a client’s interest in a product. The prediction of the output value is calculated a
Therefore the neighbourhood function will be: closing time of the neuron k. fj: its value is calculated using the Floyd algorithm [30].
Therefore the neighbourhood function will be: closing time of the neuron k. fj: its value is calculated using the Floyd algorithm [30].
Figure 7. Screenshot representing how a client indicates his preferences (a) and the plan suggested to
Figure 7. Screenshot representing how a client indicates his preferences (a) and the plan suggested to
Figure 8. Screenshot representing the steps for a plan (a) and the results obtained for the plan (b) and preferences, while Figure 8b illustrates the result obtained from the suggested plan.
Figure 8. Screenshot representing the steps for a plan (a) and the results obtained for the plan (b) and preferences, while Figure 8b illustrates the result obtained from the suggested plan.
Figure 9, Evolution of the number of users registered in the system. Users are classified according to of the users: 16-30, 30-45 and more than 45 years old.
Figure 9, Evolution of the number of users registered in the system. Users are classified according to of the users: 16-30, 30-45 and more than 45 years old.
Figure 11. Percentage of the number of shops visited in relation to the number of shops initially plans that were successfully completed. The results obtained can be observed in Figure 11.
Figure 11. Percentage of the number of shops visited in relation to the number of shops initially plans that were successfully completed. The results obtained can be observed in Figure 11.
Related topics:
EngineeringComputer ScienceAmbient IntelligenceMulti Agent SystemUser profileIT EvaluationIntelligent EnvironmentCBR SystemsPsychology and Cognitive Sciences
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