Optima
Optimal Personalized Interface by Man-Imitating Agents (Optima)
01-03-05 21:16
Adapting Virtual Guides: Using cognitive user profiling to enhance data driven information exchange.
The project addresses the problem of selecting which information from an extremely rich database to relay to an interested but relatively passive user. In many situations, the amount of information that can be given to a receiving party exceeds practical constraints such as the time available for the information exchange, motivational limitation on the receiving party, or the maximum complexity that the receiving party is willing to process. A typical example of this situation is a professional, educated museum guide touring an interested party of art novices through a museum. The guide has access to much more knowledge that the party can handle, so she has to limit the information given during the tour. Assuming an experienced guide, she probably has a default tour through the museum, discussing the highlights of the museum and some anecdotes to keep the party interested. However, unlike in a classroom setting, there is no formal curriculum that she has to follow. Based on the perceived interests of the party, she can adapt her tour, selecting from her extensive knowledge of the works exhibited in the museum, to better align the relayed information to the interests of the party.
In this project we will apply principles from the ACT-R architecture of cognition to implement an dynamic, adaptive virtual guide for the collection of the Rijksmuseum. In related I2RP-projects, databases containing semantic knowledge about the collection and methods to interface this knowledge have been developed. The aim of this project is to design and implement a virtual guide that is able to adapt itself to the user. In order to realize this goal, both guide and user models have to be developed. One of the challenges that will be faced is how to update the guide model, as the incoming information for shaping the model will be relatively sparse. An import aspect is how to use the learning capabilities available in ACT-R to have the virtual guide adapt and develop itself over time in such a way that a returning user can take the tour again without being presented with exactly the same information.
Another issue that this project might focus on, is the form in which the information is given to the user. For example, when describing visual art, presenting large amounts of visually presented text goes against the parallel listen-and-look advantage of a real-world guided tour. At the same time, having to wait until a spoken text is finished decreases the
self-directed advantages of virtual guiding.
The project addresses the problem of selecting which information from an extremely rich database to relay to an interested but relatively passive user. In many situations, the amount of information that can be given to a receiving party exceeds practical constraints such as the time available for the information exchange, motivational limitation on the receiving party, or the maximum complexity that the receiving party is willing to process. A typical example of this situation is a professional, educated museum guide touring an interested party of art novices through a museum. The guide has access to much more knowledge that the party can handle, so she has to limit the information given during the tour. Assuming an experienced guide, she probably has a default tour through the museum, discussing the highlights of the museum and some anecdotes to keep the party interested. However, unlike in a classroom setting, there is no formal curriculum that she has to follow. Based on the perceived interests of the party, she can adapt her tour, selecting from her extensive knowledge of the works exhibited in the museum, to better align the relayed information to the interests of the party.
In this project we will apply principles from the ACT-R architecture of cognition to implement an dynamic, adaptive virtual guide for the collection of the Rijksmuseum. In related I2RP-projects, databases containing semantic knowledge about the collection and methods to interface this knowledge have been developed. The aim of this project is to design and implement a virtual guide that is able to adapt itself to the user. In order to realize this goal, both guide and user models have to be developed. One of the challenges that will be faced is how to update the guide model, as the incoming information for shaping the model will be relatively sparse. An import aspect is how to use the learning capabilities available in ACT-R to have the virtual guide adapt and develop itself over time in such a way that a returning user can take the tour again without being presented with exactly the same information.
Another issue that this project might focus on, is the form in which the information is given to the user. For example, when describing visual art, presenting large amounts of visually presented text goes against the parallel listen-and-look advantage of a real-world guided tour. At the same time, having to wait until a spoken text is finished decreases the
self-directed advantages of virtual guiding.