
## 5.4.1 Mobile Systems And Robotics

Ubiquitous Knowledge Discovery (UKD) deals with the distributed and mobile systems, operating in a complex, dynamic and unstable environment. The word 'ubiquitous' means distributed at a time. Navigation systems, vehicle monitoring, household management systems, music mining are examples of UKD. DARPA navigation challenge was presented in [146]. A winning entry in 2005 used online learning for road image classification into drivable and not drivable. They used an adaptive Mixture of Gaussians, for gradual adaptation they were adjusting the internal Gaussian and rapid adaptation by replacement of the Gaussians with the new ones. The needed speed of adaptation would depend on the road conditions.

Adaptivity to changing environment has been addressed in robotics [124], for instance in designing a player for robot soccer [94].

## 5.4.2 Intelligent Systems

'Smart' home systems [126] or intelligent household appliances [34] also need to be adaptive to changing environment and user needs.

## 5.4.3 Virtual Reality

Finally, virtual reality needs mechanisms to take concept drift into account. In computer game design [28] adversary actions of the players (cheating) might be one of the drift sources. In flight simulation the strategies and skills differ across different users [63]. In Table 3 we summarize the discussed applications with concept drift.

## 6 Terminology

Concept drift is relatively new research field and the terminology is not yet fixed. Moreover, the problem of shifting data is discovered and handled in very broad domain area. With the loads of data more and more attention is drawn to the differences between training and testing data distributions. We provide alternative terminology in Table 4.

## 7 Concluding Remarks

We provided an overview of the available concept drift responsive techniques and real learning tasks, where concept drift problem is relevant.

The problem of concept drift is very broad. There has been plenty of general research and attempts to understand the phenomena. Generalization is not possible without assumptions about the nature