AmIciTy |

Lightweight Middleware for IoT

positions for 2 master or diploma students

objectives

The goal of this project is to create an agent-based middleware that works on a variety of devices, from Raspberry Pi nodes to Android smartphones to full-size workstations.

The project implies building a uniform infrastructure for all types of devices, allowing agents to easily work with any of them.

This projects will use the vast experience of the tATAmI platform for software agents.

At least one scientific paper will be produced during this research.

references

[1] Olaru, Andrei. 'tATAmI-2--A Flexible Framework for Modular Agents.' 2015 20th International Conference on Control Systems and Computer Science. IEEE, 2015.

Intelligent Assistant Agent

positions for 2 master or diploma students

objectives

The goal of this project is to create a conversational agent that can assist the user in various tasks related to the user's digital life. The user will communicate with the agent in natural language and the agent will use a visual representation (context graphs and patterns) to retain its knowledge; this knowledge can be shown to the user on demand, in a graphical form or in a textual form.

Using the agent, the user will be able to control the device and will be able to configure the assistant such that it will give reminders to the user or perform automatic actions when certain pre-described situations are detected.

While the project is, in the first phase, directed towards a PC implementation, an Android implementation is also envisioned.

A successful project will also produce a scientific paper.

Intelligent Assistant Agent

positions for 2 master or diploma students

objectives

The goal of this project is to create a conversational agent that can assist the user in various tasks related to the user's digital life. The user will communicate with the agent in natural language and the agent will use a visual representation (context graphs and patterns) to retain its knowledge; this knowledge can be shown to the user on demand, in a graphical form or in a textual form.

Using the agent, the user will be able to control the device and will be able to configure the assistant such that it will give reminders to the user or perform automatic actions when certain pre-described situations are detected.

While the project is, in the first phase, directed towards a PC implementation, an Android implementation is also envisioned.

A successful project will also produce a scientific paper.

Activity Detection on Android Devices

position for 1 diploma student

objectives

While machine learning works great when computing resources abound, a privacy-protecting distributed implementation of context-aware, intelligent behavior requires learning on resource-constrained devices.

The goal of this research is to develop means to learn from the user's activity on smart devices only with the help of local resources.

The expected result is to be able to predict the activities of the user, using sensors on the device and also information available from local apps, in order to better assist the user in those activities and anticipate potential unwanted situations.

references

[1] Krishnan, Narayanan C., and Diane J. Cook. 'Activity recognition on streaming sensor data.' Pervasive and mobile computing 10 (2014): 138-154.
[2] Cook, Diane J. 'Learning setting-generalized activity models for smart spaces.' IEEE intelligent systems 2010.99 (2010): 1.
[3] Olaru, Andrei, and Adina Magda Florea. 'A Platform for Matching Context in Real Time.' International Conference on Hybrid Artificial Intelligence Systems. Springer International Publishing, 2015.