AAAI 2018 Workshop

Title: Interactions with Social Robots - issues and challenges in combining knowledge and dialogue capabilities for digital companions

Speaker :  Kristiina Jokinen

Abstract: In recent years, many different robotic companions have appeared to enable potentially useful applications for healthcare assistance, home and assisted living services, language learning, gaming, etc. The role of the robot is a peer-like companion which can provide useful information and communicate with the human users in natural language. Such agents appear as social robots which act and interact in the physical world, and their appearance, behavior, and interaction capabilities affect the users’ views and acceptance of the applications. However, many practical challenges remain to be solved before robot companions become a reality. In this talk I will focus on dialogue modelling that enables interaction between users and social robots. In particular, I will discuss dialogue design that takes into account structured data on human activities so as to expand the system's reasoning and interaction capabilities with the help of goal-directed ontologies. I will also discuss technology for building human-centered interactive systems and present our work to create a framework for automated social agents that can assist human care-takers in their collaborative activities in service industries such as nursing, caregiving, and education. I will also explore the general challenges concerning possibilities to improve and reconstruct operations in service industries based on explicit knowledge and social robots.

Bio: Kristiina Jokinen is Senior Researcher at AI Reserach Center at AIST Tokyo Waterfront. Before joining AIRC, she was Professor and Project Manager at University of Helsinki and at University of Tartu. She received her PhD from UMIST, Manchester, and was awarded the JSPS Fellowship to research at the AIST, Japan. She was Invited Researcher at the ATR Research Labs in Kyoto, and Visiting Professor at Doshisha University in Kyoto in 2009-2010. She was the Nokia Foundation Fellow in Stanford in 2006, and she is Life Member of Clare Hall College at University of Cambridge. Her research focuses on spoken dialogue systems, corpus analysis, and cooperative and multimodal human-robot communication. She has widely published on these topics, including three books. Together with G.Wilcock she developed the WikiTalk open-domain dialogue application for social robots. She has had a leading role in multiple national and international cooperation projects. She has served as General Chair for the SIGDial 2017 and ICMI 2013, Area Chair for Interspeech 2017 and COLING 2014, she organised the northernmost dialogue conference IWSDS in 2016 in Lapland and edited the Springer book "Dialogues with Social Robots" (LNEE 427)

 

Title: Statistical Machine Learning for Dialog Management; its history and future promise.

Speaker :  Paul Crook

Abstract: Partially contradicting the workshop's premise, statistical machine learning (ML) methods for dialogue management have a long history, similar to that of other areas of natural language processing. The talk starts with a  review of the statistical approaches that have been applied in the literature, especially to goal-orientated dialogues such as browsing and task-completion. Techniques such as modelling dialogues as sequential decision-making processes, e.g. MDPs and POMDPs. Then suggest reasons why such approaches have struggled to cross over into industry.

The second part of the talk  considers the new golden age that has opening-up for dialogue management. The recent influx of Deep Learning approaches that remove the burden of input featurization and dialogue-state design. Sequence-to-Sequence and Information Retrieval methods that make it easy to stand-up shallow chatbots that despite their lack of understanding are able to converse in convincingly natural language. Search Engines that are attempting to incorporate elements of dialogue state-tracking and deliver actions, e.g. deep links to Apps, to users. The confluence of these approaches offers the potential for exciting new ways of building and training dialogue systems, repacking statistical dialogue management in forms that non-experts can use, and thus deliver on some of the promises of Conversational AI.

Bio: Paul Crook is currently a Senior Scientist at Microsoft where over the past five years he has contributed to the design and implementation of the conversational engine of Cortana; introducing the concepts of Information-State (IS) dialogue management and statistical ranking of dialogue hypotheses. Prior to Microsoft, Paul was a Research Fellow in the Interaction Lab at Heriot-Watt University and Research Associate at the University of Edinburgh. His research focuses on statistical machine learning methods for dialogue management and user simulation, especially Reinforcement Learning and more recently Deep Learning.