SlideShare a Scribd company logo
Empowering Conversational Agents with Situated Natural Language
Communication Skills by Exploiting Deep Reinforcement Learning
Techniques
Alessandro Suglia
Heriot-Watt University, Edinburgh Centre for
Robotics, Edinburgh, Scotland, UK
D o c t o ra l C o n s o r t i u m A I * I A 2 0 1 7 , B a r i
Supervisor: Prof. Oliver Lemon
Director of the Interaction Lab,
Heriot-Watt University
Dynamic Temporal Contextualized
The answer provided
by one of the
interlocutor affects
the state of the
dialogue
What is dialogue?
The interpretation of
each utterance
depends incredibly on
different environment
conditions
Different topics are
discussed in a given
conversation without
a specific order
[1]: Rieser, V., and O. Lemon. Reinforcement learning for adaptive dialogue systems: a data-driven methodology
for dialogue management and natural language generation. Springer Science & Business Media, 2011.
Goal-directed learning from interaction with an
environment by learning to map situations to actions
by exploiting Deep Neural Networks models
Deep Reinforcement Learning
Deep Reinforcement Learning
Successfully applied to different games such as Go [2] or Poker [3]
Incrementally abstract representations learned from training data
Learning to behave by interacting with the environment
Learning in an incremental and online fashion
Inability to generalise due to the domain specific design
Handsome quantity of data required to effectively learn a policy
[2]: Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I.,
Panneershelvam, V., Lanctot, M., et al.: Mastering the game of Go with deep neural networks and tree search. Nature
529(7587) (2016) 484–489
[3]: Moravčík, Matej, et al. "Deepstack: Expert-level artificial intelligence in no-limit poker." arXiv preprint arXiv:
1701.01724 (2017).
RQ1 : Is it possible to train a conversational agent to interact
within real world contexts consisting of embodied agents and
situated objects?
RQ2: Is it possible to train a conversational agent to generate
accurate contextualized responses for the user?
RQ3: Is the system able to adapt seemly to different domains by
exploiting what it has previously learned?
Natural Language
interaction
Multi-modal
interaction
Agent
embodiment
Thanks!
You can find me at the poster session
@ale_suglia
as247@hw.ac.uk
Any questions?
References
[1]: Rieser, V., and O. Lemon. Reinforcement learning for adaptive dialogue systems: a data-driven
methodology for dialogue management and natural language generation. Springer Science &
Business Media, 2011.
[2]: Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J.,
Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of Go with deep neural
networks and tree search. Nature 529(7587) (2016) 484–489
[3]: Moravčík, Matej, et al. "Deepstack: Expert-level artificial intelligence in no-limit poker." arXiv
preprint arXiv:1701.01724 (2017).

More Related Content

Similar to Empowering Conversational Agents with Situated Natural Language Communication Skills by Exploiting Deep Reinforcement Learning Techniques

Engineering active learning: LEGO robots & 3D virtual worlds
Engineering active learning: LEGO robots & 3D virtual worldsEngineering active learning: LEGO robots & 3D virtual worlds
Engineering active learning: LEGO robots & 3D virtual worlds
Michael Vallance
 
No.257 miaomiao ding-erc poster
No.257  miaomiao ding-erc posterNo.257  miaomiao ding-erc poster
No.257 miaomiao ding-erc poster
苗苗 丁
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
punedevscom
 
11.the theoretician and the practitioner represent a language community or it...
11.the theoretician and the practitioner represent a language community or it...11.the theoretician and the practitioner represent a language community or it...
11.the theoretician and the practitioner represent a language community or it...
Alexander Decker
 
Language and Intelligence
Language and IntelligenceLanguage and Intelligence
Language and Intelligence
butest
 
Rise of AI through DL
Rise of AI through DLRise of AI through DL
Rise of AI through DL
Rehan Guha
 
Do We Click? - Laurent Silbert - H+ Summit @ Harvard
Do We Click? - Laurent Silbert - H+ Summit @ HarvardDo We Click? - Laurent Silbert - H+ Summit @ Harvard
Do We Click? - Laurent Silbert - H+ Summit @ Harvard
Humanity Plus
 
Timo Honkela: Multimodally Grounded Translation by Humans and Machines
Timo Honkela: Multimodally Grounded Translation by Humans and MachinesTimo Honkela: Multimodally Grounded Translation by Humans and Machines
Timo Honkela: Multimodally Grounded Translation by Humans and Machines
Timo Honkela
 
An Overview Of Natural Language Processing
An Overview Of Natural Language ProcessingAn Overview Of Natural Language Processing
An Overview Of Natural Language Processing
Scott Faria
 
SLALS526_MUVEs
SLALS526_MUVEsSLALS526_MUVEs
SLALS526_MUVEs
Edith Paillat
 
how humans learn in general and how they learn a foreign language
how humans learn in general and how they learn a foreign languagehow humans learn in general and how they learn a foreign language
how humans learn in general and how they learn a foreign language
Estefy Feijoo
 
David Marsh: Benefits of bilingualism
David Marsh: Benefits of bilingualismDavid Marsh: Benefits of bilingualism
David Marsh: Benefits of bilingualism
Pilar Torres
 
Russian english workshop world-call2013
Russian english workshop world-call2013Russian english workshop world-call2013
Russian english workshop world-call2013
Konstantin Shestakov
 
Teoria suunnittelu a_otvt
Teoria suunnittelu a_otvtTeoria suunnittelu a_otvt
Teoria suunnittelu a_otvt
Jari Laru
 
A Comprehensive Study On Natural Language Processing And Natural Language Int...
A Comprehensive Study On Natural Language Processing And Natural Language Int...A Comprehensive Study On Natural Language Processing And Natural Language Int...
A Comprehensive Study On Natural Language Processing And Natural Language Int...
Scott Bou
 
A Survey on Word Sense Disambiguation
A Survey on Word Sense DisambiguationA Survey on Word Sense Disambiguation
A Survey on Word Sense Disambiguation
IOSR Journals
 
Eurocall 2011, Nottingham
Eurocall 2011, NottinghamEurocall 2011, Nottingham
Eurocall 2011, Nottingham
elinatapio
 
CLaSIC 2016 presentation
CLaSIC 2016 presentationCLaSIC 2016 presentation
CLaSIC 2016 presentation
Takeshi Sato
 
Using ontology for natural language processing
Using ontology for natural language processingUsing ontology for natural language processing
Using ontology for natural language processing
cracaoanu constantin sergiu
 
LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...
LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...
LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...
paperpublications3
 

Similar to Empowering Conversational Agents with Situated Natural Language Communication Skills by Exploiting Deep Reinforcement Learning Techniques (20)

Engineering active learning: LEGO robots & 3D virtual worlds
Engineering active learning: LEGO robots & 3D virtual worldsEngineering active learning: LEGO robots & 3D virtual worlds
Engineering active learning: LEGO robots & 3D virtual worlds
 
No.257 miaomiao ding-erc poster
No.257  miaomiao ding-erc posterNo.257  miaomiao ding-erc poster
No.257 miaomiao ding-erc poster
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
11.the theoretician and the practitioner represent a language community or it...
11.the theoretician and the practitioner represent a language community or it...11.the theoretician and the practitioner represent a language community or it...
11.the theoretician and the practitioner represent a language community or it...
 
Language and Intelligence
Language and IntelligenceLanguage and Intelligence
Language and Intelligence
 
Rise of AI through DL
Rise of AI through DLRise of AI through DL
Rise of AI through DL
 
Do We Click? - Laurent Silbert - H+ Summit @ Harvard
Do We Click? - Laurent Silbert - H+ Summit @ HarvardDo We Click? - Laurent Silbert - H+ Summit @ Harvard
Do We Click? - Laurent Silbert - H+ Summit @ Harvard
 
Timo Honkela: Multimodally Grounded Translation by Humans and Machines
Timo Honkela: Multimodally Grounded Translation by Humans and MachinesTimo Honkela: Multimodally Grounded Translation by Humans and Machines
Timo Honkela: Multimodally Grounded Translation by Humans and Machines
 
An Overview Of Natural Language Processing
An Overview Of Natural Language ProcessingAn Overview Of Natural Language Processing
An Overview Of Natural Language Processing
 
SLALS526_MUVEs
SLALS526_MUVEsSLALS526_MUVEs
SLALS526_MUVEs
 
how humans learn in general and how they learn a foreign language
how humans learn in general and how they learn a foreign languagehow humans learn in general and how they learn a foreign language
how humans learn in general and how they learn a foreign language
 
David Marsh: Benefits of bilingualism
David Marsh: Benefits of bilingualismDavid Marsh: Benefits of bilingualism
David Marsh: Benefits of bilingualism
 
Russian english workshop world-call2013
Russian english workshop world-call2013Russian english workshop world-call2013
Russian english workshop world-call2013
 
Teoria suunnittelu a_otvt
Teoria suunnittelu a_otvtTeoria suunnittelu a_otvt
Teoria suunnittelu a_otvt
 
A Comprehensive Study On Natural Language Processing And Natural Language Int...
A Comprehensive Study On Natural Language Processing And Natural Language Int...A Comprehensive Study On Natural Language Processing And Natural Language Int...
A Comprehensive Study On Natural Language Processing And Natural Language Int...
 
A Survey on Word Sense Disambiguation
A Survey on Word Sense DisambiguationA Survey on Word Sense Disambiguation
A Survey on Word Sense Disambiguation
 
Eurocall 2011, Nottingham
Eurocall 2011, NottinghamEurocall 2011, Nottingham
Eurocall 2011, Nottingham
 
CLaSIC 2016 presentation
CLaSIC 2016 presentationCLaSIC 2016 presentation
CLaSIC 2016 presentation
 
Using ontology for natural language processing
Using ontology for natural language processingUsing ontology for natural language processing
Using ontology for natural language processing
 
LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...
LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...
LANGUAGE ASSIMILATION THROUGH TYPE-2 FUZZY GRAMMARS: AN APPLICATION IN COMPUT...
 

Recently uploaded

Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Marlon Dumas
 
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
osoyvvf
 
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative ClassifiersML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
MastanaihnaiduYasam
 
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
ElizabethGarrettChri
 
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
SaffaIbrahim1
 
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
eoxhsaa
 
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCAModule 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
yuvarajkumar334
 
Sample Devops SRE Product Companies .pdf
Sample Devops SRE  Product Companies .pdfSample Devops SRE  Product Companies .pdf
Sample Devops SRE Product Companies .pdf
Vineet
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
jitskeb
 
Econ3060_Screen Time and Success_ final_GroupProject.pdf
Econ3060_Screen Time and Success_ final_GroupProject.pdfEcon3060_Screen Time and Success_ final_GroupProject.pdf
Econ3060_Screen Time and Success_ final_GroupProject.pdf
blueshagoo1
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Timothy Spann
 
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
agdhot
 
一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理
keesa2
 
Data Scientist Machine Learning Profiles .pdf
Data Scientist Machine Learning  Profiles .pdfData Scientist Machine Learning  Profiles .pdf
Data Scientist Machine Learning Profiles .pdf
Vineet
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
Timothy Spann
 
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptxREUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
KiriakiENikolaidou
 
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
slg6lamcq
 
社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .
NABLAS株式会社
 
Cell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docxCell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docx
vasanthatpuram
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
bmucuha
 

Recently uploaded (20)

Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
 
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
 
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative ClassifiersML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
 
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
 
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
 
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
 
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCAModule 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
 
Sample Devops SRE Product Companies .pdf
Sample Devops SRE  Product Companies .pdfSample Devops SRE  Product Companies .pdf
Sample Devops SRE Product Companies .pdf
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
 
Econ3060_Screen Time and Success_ final_GroupProject.pdf
Econ3060_Screen Time and Success_ final_GroupProject.pdfEcon3060_Screen Time and Success_ final_GroupProject.pdf
Econ3060_Screen Time and Success_ final_GroupProject.pdf
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
 
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
 
一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理
 
Data Scientist Machine Learning Profiles .pdf
Data Scientist Machine Learning  Profiles .pdfData Scientist Machine Learning  Profiles .pdf
Data Scientist Machine Learning Profiles .pdf
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
 
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptxREUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
 
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
 
社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .
 
Cell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docxCell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docx
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
 

Empowering Conversational Agents with Situated Natural Language Communication Skills by Exploiting Deep Reinforcement Learning Techniques

  • 1. Empowering Conversational Agents with Situated Natural Language Communication Skills by Exploiting Deep Reinforcement Learning Techniques Alessandro Suglia Heriot-Watt University, Edinburgh Centre for Robotics, Edinburgh, Scotland, UK D o c t o ra l C o n s o r t i u m A I * I A 2 0 1 7 , B a r i Supervisor: Prof. Oliver Lemon Director of the Interaction Lab, Heriot-Watt University
  • 2. Dynamic Temporal Contextualized The answer provided by one of the interlocutor affects the state of the dialogue What is dialogue? The interpretation of each utterance depends incredibly on different environment conditions Different topics are discussed in a given conversation without a specific order [1]: Rieser, V., and O. Lemon. Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation. Springer Science & Business Media, 2011.
  • 3. Goal-directed learning from interaction with an environment by learning to map situations to actions by exploiting Deep Neural Networks models Deep Reinforcement Learning
  • 4. Deep Reinforcement Learning Successfully applied to different games such as Go [2] or Poker [3] Incrementally abstract representations learned from training data Learning to behave by interacting with the environment Learning in an incremental and online fashion Inability to generalise due to the domain specific design Handsome quantity of data required to effectively learn a policy [2]: Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587) (2016) 484–489 [3]: Moravčík, Matej, et al. "Deepstack: Expert-level artificial intelligence in no-limit poker." arXiv preprint arXiv: 1701.01724 (2017).
  • 5. RQ1 : Is it possible to train a conversational agent to interact within real world contexts consisting of embodied agents and situated objects?
  • 6. RQ2: Is it possible to train a conversational agent to generate accurate contextualized responses for the user?
  • 7. RQ3: Is the system able to adapt seemly to different domains by exploiting what it has previously learned?
  • 9. Thanks! You can find me at the poster session @ale_suglia as247@hw.ac.uk Any questions?
  • 10. References [1]: Rieser, V., and O. Lemon. Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation. Springer Science & Business Media, 2011. [2]: Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587) (2016) 484–489 [3]: Moravčík, Matej, et al. "Deepstack: Expert-level artificial intelligence in no-limit poker." arXiv preprint arXiv:1701.01724 (2017).