PLANNING IN MULTI AGENT SYSTEMS BASED ON REINFORCEMENT LEARNING
In this paper, a method for feature selection by reinforcement learning is considered. Reinforcement learning is the problem faced by an agent that learns behavior through trial and error interactions with a dynamic environment. The work described here is a multi agent system in which the agents which try to coordinate to reach common goals, would be learned by considering the ability of the features to separate the sample data in order to discriminate them as well as possible. This discrimination is under Linear Discriminate Analysis; in addition the correlation coefficient does as the weight, perspective to maximize the minimum distance of categories.
Dear authors,
Can you please check the title of your paper ("Feature selection using") again?
It seems incomplete and also different from the title given in the pdf file.
Thank you.
D Pham.
Dear Ms Bastanfard and Prof. Katebi,
Would you please chenge the format of the paper according IPROMS 2007 conference template and re-upload it again to be able to published in the proceeding.
Many thanks,
Co-chair
Afshin Ghanbarzadeh
Hi Bastanfard,
First of all, thanks for you paper. I would like to ask some questions:
a. Why you using LDA? LDA, in my humble knowledge is used so far work on feature extraction not selection. Correct me if I'm wrong.
b. In your paper you did mentions that statistical is the most common method for feature selection. May I know why is that?Why it so different to other approaches?
Regards,