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

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Since the early days of Artificial Intelligence, designing an agent able to interact with humans was considered a remarkable goal, aiming to replicate human cognitive capabilities in computer. In this work, we outline the main topics of a prospective PhD project whose aim is to exploit Deep Reinforcement Learning techniques to learn to interact with users in Natural Language conversations.

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

  1. 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. 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. 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. 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. 5. RQ1 : Is it possible to train a conversational agent to interact within real world contexts consisting of embodied agents and situated objects?
  6. 6. RQ2: Is it possible to train a conversational agent to generate accurate contextualized responses for the user?
  7. 7. RQ3: Is the system able to adapt seemly to different domains by exploiting what it has previously learned?
  8. 8. Natural Language interaction Multi-modal interaction Agent embodiment
  9. 9. Thanks! You can find me at the poster session @ale_suglia as247@hw.ac.uk Any questions?
  10. 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).

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