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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.

A Learning Model for Intelligent Agents Applied to Poultry Farming