Name
Abstract | ||
|---|---|---|
This paper proposes a learning model for taking-decision problems using intelligent agents technologies combined with instance-based machine learning techniques. Our learning model is applied to a real case to support the daily decisions of a poultry farmer. The agent of the system is used to generate action policies, in order to control a set of factors in the daily activities, such as food-meat conversion, amount of food to be consumed, time to rest, weight gain, comfort temperature, water and energy to be consumed, etc. The perception of the agent is ensured by a set of sensors scattered by the physical structure of the poultry. The principal role of the agent is to perform a set of actions in a way to consider aspects such as productivity and profitability without compromising bird welfare. Experimental results have shown that, for the decision-taking process in poultry farming, our model is sound, advantageous and can substantially improve the agent actions in comparison with equivalent decision when taken by a human specialist. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.5220/0005373604950503 | ICEIS (3-1) |
Field | DocType | Citations |
Intelligent agent,Activities of daily living,Poultry farmer,Simulation,Computer science,Knowledge management,Profitability index,Welfare,Poultry farming,Perception,Physical structure,Environmental economics | Conference | 1 |
PageRank | References | Authors |
0.43 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richardson Ribeiro | 1 | 43 | 11.12 |
| Marcelo Teixeira | 2 | 19 | 8.07 |
| André L. Wirth | 3 | 1 | 0.43 |
| André Pinz Borges | 4 | 14 | 7.67 |
| Fabrício Enembreck | 5 | 274 | 38.42 |