Human-Machine Alignment for Context Recognition in the Wild

The premise for AI systems like personal assistants to provide guidance and suggestions to an end-user is to understand, at any moment in time, the personal context that the user is in. The context – where the user is, what she is doing and with whom – allows the machine to represent the world in user’s terms. The context must be inferred from a stream of sensor readings generated by smart wearables such as smartphones and smartwatches, and the labels are acquired from the user directly. To perform robust context prediction in this real-world scenario, the machine must handle the egocentric nature of the context, adapt to the changing world and user, and maintain a bidirectional interaction with the user to ensure the user-machine alignment of world representations. To this end, the machine must learn incrementally on the input stream of sensor readings and user supervision. In this work, we: (i) introduce interactive classification in the wild and present knowledge drift (KD), a special form of concept drift, occurring due to world and user changes; (ii) develop simple and robust ML methods to tackle these scenarios; (iii) showcase the advantages of each of these methods in empirical evaluations on controlled synthetic and real-world data sets; (iv) design a flexible and modular architecture that combines the methods above to support context recognition in the wild; (v) present an evaluation with real users in a concrete social science use case.

Grantor dc:publisher

Università degli studi di Trento

Year dc:date

2024

Author dc:creator

• Bontempelli, Andrea

Contributors dc:contributor

• Giunchiglia, Fausto
• Passerini, Andrea

• interactive machine learning, incremental learning, data streams human-in-the-loop, concept drift, context recognition

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Creative commons
• license uri:http://creativecommons.org/licenses/by-sa/4.0/

Language dc:language

eng

Identifier

http://dx.doi.org/10.15168/11572_407669
10.15168/11572_407669

OAI identifier oai:identifier

oai:iris.unitn.it:11572/407669