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Dietmar Janetzko National College of Ireland Dialogue-Based Authoring of Units of Learning

Explore dialogue-based authoring, AIML, IMS-LD, and Dialogue Management for developing educational content. Discuss challenges and solutions in error management, user modeling, and more.

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Dietmar Janetzko National College of Ireland Dialogue-Based Authoring of Units of Learning

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  1. Dietmar JanetzkoNational College of IrelandDialogue-Based Authoring of Units of Learning

  2. Outline • Dialogue-based Authoring • AIML • IMS-LD • Dialogue Management based on the Information State Update Approach • Discussion & Future Work

  3. Motivation for Dialogue-Based Authoring • The sucess of XML has increased the demand for editors and authoring tools supporting the development of special purpose applications. • While graphical editors/authoring tools often are useful, they do not support all tasks possibly related to editing or authoring. • Cases in point are anwering questions or reconstruction of knowledge that may not be fully aware to the person interviewed. • In these situations, dialogue-based authoring may be a good alternative or supplement to graphical editors

  4. Dialogue-Based Authoring IMS-LD Specification IMS-LD Learning Design IMS-LD Learning Design Elicitation Dialogue System Educational Expert E-Learning System* Learner * In EML LD lingo: “An EML LD aware player”

  5. Dialogue-Based Authoring

  6. Features of Dialogue-based Authoring • Clear thematic Scope defined by an XML specification • Complex Domain • Possibly involvement of multiple resources • Mixed Initiative dialogue, but mainly system driven • Strategies for clarification requests needed Knowledge elicited should not be flawed due to misunderstandings of either the human user or the system • Timing and grounding • Clarification requests • Error correction subdialogues

  7. Challenges of Dialogue-based Authoring • Strategies for Error Management Knowledge elicited should not be flawed due to misunder- standings of either the human user or the system • Error avoidance (timing and grounding, concept explanation and clarification requests) • Error detection (rephrasing, open modeling) • Error Handling and Repair (error feedback by the system, error correction subdialogues) • User Modeling • Dialogue Management

  8. AIML (Artificial Intelligence Markup Language • simple XML based pattern matching language • aka standard for Chat-Bots • includes a number of advanced features (e.g., recursion, interfaces to OpenCyC) • developed by R. Wallace won several time the Loebner prize (Turing Test Competion)

  9. Example of an AIML Category • <category> • <pattern>WHAT IS ALU • </pattern> • <template>The Association of Lisp Users • organizes the International Lisp Conference. • </template> • </category>

  10. AIML-Based Dialogue Systems

  11. <?xml version="1.0.1" encoding= "ISO-8859-1"?> <aiml version="1.0"> <category> <pattern>HELLO</pattern> <template> Well hello there! </template></category> </aiml> Program B (Java) Program D (Java) PyAIML Y (Python) Program N /AIMLpad (C++) Program V (Perl) C-Alice / Hippie / Program C (C++)AIMLbot (.NET) http://www.Pandorabots.com Some AIML Tags Animation/Talking Heads:Oddcast (Flash), Mediasemantics (Flash), MS Agent SR/TTS Engines: ACE, ALICETalker Interface to other Knowledge Bases: CYN (OpenCyc) <aiml> … </aiml> <´pattern> … </pattern> <template> … </template> <topic> … <topic> <javascript> … </javascript> AIML-Based Dialogue Systems AIML-Based Dialogue System = AIML-Script + AIML-Engine + X

  12. AIML–Engines

  13. IMS Global Learning, Inc.http://www.imsglobal.org • IMS is a non profit organisation that includes more than 50 contributing members and affiliates • IMS develops and promotes the adoption of open technical specifications for interoperable learning technology. • Several IMS specifications have become worldwide de facto standards for delivering learning products and services. • IMS specifications and related publications are made available to the public at no charge.

  14. IMS LD • Overall Goal: Development of a pedagogical framework that supports pedagogical diversity and interoperability of e-learning materials on any e-learning platform. • Developed by the IMS on the basis of the Educational Modeling Language (EML) of the Open University of the Netherlands • IMS LD describes a “Unit of Learning” (i.e. a module, course, lesson) which is designed to meet specified learning objectives. • IMS LD is integrated into other specifications set up by IMS (IMS Content Packaging Specification, IMS Question and Test Interoperability)

  15. Unit of Learning = IMS Content Package + IMS Learning Design Component Elements of the Learning-Design Element Structure of a Unit of Learning Structure of an IMS Content Package + The element may occur one or more times ? The element is optional

  16. Turning IMS LD into a Dialogue Script • On the face of it, turning IMS LD into a dialogue script appears to be possibly by following a simple template filling approach (e.g., known from dialogue system for flight bookings) • However, special attention needs to be devoted to • reconstructing knowledge possibly known only vaguely, • transparency, • error management, • user modeling. • For this reason, dialogue management is of special importance in this work.

  17. Dialogue Management • Based on a blackboard architecture • Usage of agents • follows both a form-filling and a Information State Update (ISU) approach • Cue-based instead of plan-based

  18. Information State Update (ISU) Approach to Dialogue Management (I) • The information state update approach (ISU) has been developed in the TRINDI and SIRIdUS Projects. • The notion of information state (IS) characterises the state of each participants knowledge as the conversation proceeds. • Information state is often referred to by similar names like „conversational score“ or „discourse context.“ • Its notion of information state elaborates (and simplifies) the dialogue game board approach (Ginzburg, 1996). Moreover, other concepts of the dialogue game board approach have been adopted by the ISU approach, e.g., QUD (Question Under Discussion).

  19. Information State Update (ISU) Approach to Dialogue Management (II) An information state theory of dialogue modeling consists of • Description of the informational components • Formal representation of the above components • A set of dialogue moves that trigger the update of an information state • Set of updating rules, interpretation of user utterances results in an update of the information state • Update strategy (Larsson & Traum, 2000)

  20. Information State Update Approach to Dialogue Management (III) Clearly, the question what is actually included in the information state (IS) is a key issue in dialogue management following an ISU approach. There are different representation schemas of information states (Poesio et al. 1999), e.g. • The Cooper-Larson model of IS • The Poesio-Traum Model of IS. The Cooper Larson model of IS seems to be the representation schema that is used that is used more often in Dialogue management that follows the ISU approach.

  21. Information State Update Approach to Dialogue Management/Cooper-Larsson Model BEL : Set ( Prop ) PRIVATE : AGENDA : Stack ( Action ) BEL : Se t( Prop ) SHARED : QUOD : Stack ( Question ) • The Cooper-Larsson Model is basically a typed record of features • Each participant in the conversation is described by a record • Feature values may be lists, stacks or other records • Division between Private and Shared information • Beliefs represented by a set of propositions, • Private beliefs: propositions and more general goals • Shared beliefs: what has been established in the conversation • Actions: stack of actions which the agent is to perform, Agenda items (actions) should in general be performed in the next move. • QUD are questions that should be addressed more or less in the next turn.

  22. Blackboard Architecture • A Blackboard architecture is used to integrate the ISU approach and LMS-LD • The shared IS is provided by the blackboard • The various informational components are described in terms of the elements of IMS LD • Dialogue Scripting is modularized by introducing agents for each of the 6 top-level component elements of the learning-design element of IMS-LD (i.e., TITLE, LEARNING-OBJECTIVES, PREREQUISITES, METHOD etc.) • Agents are • greedy -> Agents want to contribute to the dialogue • sticky -> Once an agent has taken the control, it has a higher chance than other agents to contribute to the next move • responsive -> Even though agent A has taken over, agent B might lead the dialogue if it fits better to a new move uttered by the user.

  23. Information State Update Approach to Dialogue Management/Cooper-Larsson Model resource1 resource2 resourcem Agent1 Agent2 Agent3 ... User • Total Information State / Blackboard • QUD • Global Goals (Jobs to be done) • User Model

  24. Discussion and Future Work • Using TrindiKit instead of AIML • better support of agents (OAA framework) • more expressive language(s) • better chances to achieve domain independece • disavantages of TrindiKit when compared to AIML? • A general approach of turning XML schemas like IMS LD into a dialogue system? • Question Mining à la amazon.com: Users who asked question a also asked question b

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