AAAI 2018 Workshop

On Reasoning and Learning for Human-Machine Dialogues (DEEP-DIAL18)
February 1-8, 2018, New Orleans, LA, USA

Natural conversation is a hallmark of intelligent systems. Unsurprisingly, dialog systems have been a key sub-area of AI for decades. Their most recent form, chatbots, which can engage people in natural conversation and are easy to build in software, have been in the news a lot lately. There are many platforms to create dialogs quickly for any domain, based on simple rules. Further, there is a mad rush by companies to release chatbots to show their AI capabilities and gain market valuation. However, beyond basic demonstration, there is little experience in how they can be designed and used for real-world applications that need decision making under constraints (e.g., sequential decision making). The workshop will thus be timely in helping chatbots realize their full potential.
Furthermore, there is an upcoming interest and need for innovation in Human-Technology-Interaction, as addressed in the context of Companion Technology. Here, the aim is to implement technical systems that smartly adapt their functionality to their users’ individual needs and requirements and are even able to solve problems in close co-operation with human users. To this end, they need to enter into a dialog and convincingly explain their suggestions and decision-making behavior.
From research side, statistical and machine learning methods are well entrenched for language understanding and entity detection. However, the wider problem of dialog management is unaddressed with mainstream tools supporting rudimentary rule-based processing. There is an urgent need to highlight the crucial role of reasoning methods such as constraints satisfaction, planning and scheduling, and learning working together with them, that can play to build an end-to-end conversation system that evolves over time. From the practical side, conversation systems need to be designed for working with people in a manner that they can explain their reasoning, convince humans about making choices among alternatives, and stand up to ethical standards demanded in real life settings.
Topics of interest, include, but are not restricted to:
Dialog Systems
  • Early experiences with implemented dialog systems
  • Evaluation of dialog systems, metrics
  • Open domain dialog and chat systems
  • Task-oriented dialogs
  • Style, voice and personality in spoken dialogue and written text
  • Novel Methods for NL Generation for dialogs
  • Domain model acquisition, especially from unstructured text
  • Plan recognition in natural conversation
  • Planning and reasoning in the context of dialog systems
  • Learning to reason
  • Learning for dialog management
  • End2end models for conversation
  • Explaining dialog policy
Practical Considerations
  • Ethical issues with reasoning in dialog systems
  • Corpora, Tools and Methodology for Dialogue System
The intended audience includes students, academic researchers and practitioners with an industrial background from the AI sub-areas of dialog systems, learning, reasoning, planning, HCI, ethics and knowledge representation.
Workshop Agenda
Talk 1:
Title: Continuous Improvement of Intelligent Assistants Through Interaction with Live Customers
SpeakerErik T. Mueller
Abstract: On March 10, 2017, Capital One launched Eno, the first natural language SMS chatbot from a U.S. bank. Eno allows customers to track account balances, see recent transactions, and make credit card payments by texting in natural language. In this talk, I discuss the artificial intelligence capabilities we're building into Eno and how we continuously improve them by leveraging interactions with live customers.
Bio: Erik Mueller is Director of Intelligent Assistants at Capital One. Before joining Capital One, Erik was a member of the original Watson Team at IBM for five years, where he co-developed the Watson Jeopardy! system, Watson for Healthcare, and WatsonPaths. He won the AAAI Feigenbaum Prize with the IBM team. He has ten patents on artificial intelligence and is the author of four books on AI, including Commonsense Reasoning, published by Morgan Kaufmann/Elsevier. He holds a Ph.D. and M.S. in computer science from UCLA and an S.B. in computer science and engineering from MIT.
Talk 2:
Title: Combining Functional Programming, Probabilistic Reasoning, and Machine Learning in an Event-Driven AI Agent Framework
SpeakerMatthew (Matt) Davis
Abstract: IBM Research has developed a framework and platform for leveraging probabilistic reasoning and machine learning models in a functional programming framework that allows modularized skills composition from microservices and learning for AI Agents. Our framework leverages both established and novel algorithms for information retrieval, classification, NLP, planning, and event generation and sequencing. We will discuss use cases, implementation, and results from real-world deployments.
Bio: Matthew (Matt) Davis is a Researcher and Manager at IBM Research - Cambridge, USA focusing on customization and generalization of AI Systems.
Important Dates
Manuscripts due:                        November 9, 2017
Notification of acceptance:    November 19, 2017
Camera-ready manuscript:    November 21, 2017
Workshop:                                      February 2 or 3, 2018
Submission Guidelines 
Papers must be formatted in AAAI two-column, camera-ready style (AAAI style files are at:
Regular research papers, which present a significant contribution, may be no longer than 7 pages, where page 7 must contain only references, and no other text whatsoever.
Short papers, which describe a position on the topic of the workshop or a demonstration/tool, may be no longer than 4 pages, references included.