Memory Aware Conversational AI to aid virtual Team-Meetings

In this micro-project, we propose investigating human recollection of team meetings and how conversational AI could use this information to create better team cohesion in virtual settings.

Specifically, we would like to investigate how a person's emotion, personality, relationship to fellow teammates, goal and position in the meeting influences how they remember the meeting. We want to use this information to create memory aware conversational AI that could leverage such data to increase team cohesion in future meetings.

To achieve this goal, we plan first to record a multi-modal data-set of team meetings in a virtual-setting. Second, administrate questionnaires to participants in different time intervals succeeding a session. Third, annotate the corpus. Fourth, carry out an initial corpus analysis to inform the design of memory-aware conversational AI.

This micro-project will contribute to a longer-term effort in building a computational memory model for human-agent interaction.

Output

A corpus of repeated virtual team meetings (6 sessions spaced, 1 week each)

manual annotations (people’s recollection of the team meeting etc.)

automatic annotations (e.g. eye-gaze, affect, body posture etc.)

A paper describing the corpus and insights gained on the design of memory-aware agents from initial analysis

Project Partners:

• TU Delft, Catholijn Jonker
• Eötvös Loránd University (ELTE), Andras Lorincz

Primary Contact: Catharine Oertel, TU Delft

Main results of micro project:

1) A corpus of repeated virtual team meetings (4 sessions spaced, 4 days apart each).
2) Manual annotations (people's recollection of the team meeting etc.)
3) Automatic annotations (e.g. eye-gaze, affect, body posture etc.)
4)A preliminary paper describing the corpus and insights gained on the design of memory-aware agents from initial analysis

Contribution to the objectives of HumaneAI-net WPs

In this micro-project, we propose investigating human recollection of team meetings and how conversational AI could use this information to create better team cohesion in virtual settings.
Specifically, we would like to investigate how a person's emotion, personality, relationship to fellow teammates, goal and position in the meeting influences how they remember the meeting. We want to use this information to create memory aware conversational AI that could leverage such data to increase team cohesion in future meetings.
To achieve this goal, we plan first to record a multi-modal data-set of team meetings in a virtual-setting. Second, administrate questionnaires to participants in different time intervals succeeding a session. Third, annotate the corpus. Fourth, carry out an initial corpus analysis to inform the design of memory-aware conversational AI.
This micro-project will contribute to a longer-term effort in building a computational memory model for human-agent interaction.

Tangible outputs

• Dataset: MEMO – Catharine Oertel
• Publication: MEMO dataset paper – Catharine Oertel
• Program/code: Memo feature extraction code – Andras Lorincx