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A Machine Learning Approach to Dialogue Act Classification in Human-Robot Conversations: Evaluation of dialogue act classification with the robot Furhat and an analysis of the market for social robots used for education
KTH, School of Computer Science and Communication (CSC). KTH, School of Industrial Engineering and Management (ITM).
KTH, School of Computer Science and Communication (CSC). KTH, School of Industrial Engineering and Management (ITM).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Maskininlärning för klassificering av talhandlingar i människa-robot-konversationer (Swedish)
Abstract [en]

The interest in social robots has grown dramatically in the last decade. Several studies have investigated the potential markets for such robots and how to enhance their human-like abilities. Both of these subjects have been investigated in this thesis using the company Furhat Robotics, and their robot Furhat, as a case study.

This paper explores how machine learning could be used to classify dialogue acts in human-robot conversations, which could help Furhat interact in a more human-like way. Dialogue acts are acts of natural speech, such as questions or statements. Several variables and their impact on the classification of dialogue acts were tested. The results showed that a combination of some of these variables could classify 73 % of all the dialogue acts correctly.

Furthermore, this paper analyzes the market for social robots which are used for education, where human-like abilities are preferable. A literature study and an interview were conducted. The market was then analyzed using a SWOT-matrix and Porter’s Five Forces. Although the study showed that the mentioned market could be a suitable target for Furhat Robotics, there are several threats and obstacles that should be taken into account before entering the market.

Abstract [sv]

Intresset för sociala robotar har ökat drastiskt under det senaste årtiondet. Ett flertal studier har undersökt hur man kan förbättra robotars mänskliga färdigheter. Vidare har studier undersökt potentiella marknader för sådana robotar. Båda dessa aspekter har studerats i denna rapport med företaget Furhat Robotics, och deras robot Furhat, som en fallstudie.

Mer specifikt undersöker denna rapport hur maskininlärning kan användas för att klassificera talhandlingar i människa-robot- konversationer, vilket skulle kunna hjälpa Furhat att interagera på ett mer mänskligt sätt. Talhandlingar är indelningar av naturligt språk i olika handlingar, såsom frågor och påståenden. Flertalet variabler och deras inverkan på klassificeringen av talhandlingar testades i studien. Resultatet visade att en kombination av några av dessa variabler kunde klassificera 73 % av alla talhandlingar korrekt.

Vidare analyserar denna rapport marknaden för sociala robotar inom utbildning, där mänskliga färdigheter är att föredra. En litteraturstudie och en intervju gjordes. Marknaden analyserades sedan med hjälp av en SWOT-matris och Porters femkraftsmodell. Fastän studien visade att den ovannämnda marknaden skulle kunna vara lämplig för Furhat Robotics finns ett flertal hot och hinder som företaget måste ta hänsyn till innan de tar sig in på marknaden.

Place, publisher, year, edition, pages
Keyword [en]
dialogue act, dialogue act classification, furhat, furhat robotics, classification, j48, machine learning, weka, robotics, education robots, swot, market segmentation
Keyword [sv]
talhandlingar, furhat, furhat robotics, klassificering, j48, maskininlärning, weka, robotik, utbildningsrobotar, swot, marknadssegmentering
National Category
Computer Science
URN: urn:nbn:se:kth:diva-175705OAI: diva2:861979
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Available from: 2015-11-18 Created: 2015-10-19 Last updated: 2015-11-18Bibliographically approved

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