Bruno Norberto da Silva

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Any Web user is a potential knowledge contributor, but it remains a challenge to make them devote their time contributing to some purpose. In order to align individual with social interests, we selected the CAPTCHA Web resource protection application to embed knowledge elicitation within the users' main task of accessing a Web resource. Consequently, unlike(More)
We study the problem of knowledge reuse by a reinforcement learning agent. We are interested in how an agent can exploit policies that were learned in the past to learn a new task more efficiently in the present. Our approach is to elicit spatial hints from an expert suggesting the world states in which each existing policy should be more relevant to the(More)
We study the problem of learning from disagreeing demonstrators. We present a model that suggests how it might be possible to design an incentive-compatible mechanism that combines demonstrations from human agents who disagree on the evaluation of the demonstrated task. Apart from comonotonicity of preferences over atomic outcomes, we make no assumptions(More)
We introduce a new approach towards a more accessible Web by means of more accessible knowledge acquisition mechanisms. Our strategy is to detect the Web designer's needs for knowledge that can be collected from minorities of Web users, and subsequently to design mechanisms that allow the proper elicitation of such knowledge from Web users. We discuss how(More)
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