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Reinforcement Learning of User Preferences for a Ubiquitous Personal Assistant

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Abstract

New technologies bring a multiplicity of new possibilities for users to work with computers. Not only are spaces more and more equipped with stationary computers or notebooks, but more and more users carry mobile devices with them (smart-phones, personal digital assistants, etc.). Ubiquitous computing aims at creating smart environments where devices are dynamically linked in order to provide new services to users and new human-machine interaction possibilities. The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it (Weiser, 1991). This network of devices must perceive the context in order to understand and anticipate the user's needs. Devices should be able to execute actions that help the user to fulfill his goal or that simply accommodate him. Actions depend on the user's context and, in particular, on the situation within the context. The objective of this work is to construct automatically a context model by applying reinforcement learning techniques. Rewards are given by the user when expressing his degree of satisfaction towards actions proposed by the system. A default context model is used from the beginning in order to have a consistent initial behavior. This model is then adapted to each particular user in a way that maximizes the user's satisfaction towards the system's actions.
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Dates and versions

hal-00753245 , version 1 (18-11-2012)

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Sofia Zaidenberg, Patrick Reignier. Reinforcement Learning of User Preferences for a Ubiquitous Personal Assistant. Abdelhamid Mellouk. Advances in Reinforcement Learning, Intech, pp.59-80, 2011, 978-953-307-369-9. ⟨10.5772/13723⟩. ⟨hal-00753245⟩
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