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School of Computing FACULTY OF ENGINEERING

School of Computing FACULTY OF ENGINEERING. Augmenting the Knowledge Capture Process with Dialogue Agents Vania Dimitrova Intelligence Augmentation Forum @ Leeds 14 June 2010 V.G.Dimitrova@leeds.ac.uk. Outline. Context - Knowledge elicitation challenges

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School of Computing FACULTY OF ENGINEERING

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  1. School of Computing FACULTY OF ENGINEERING Augmenting the Knowledge Capture Process with Dialogue AgentsVania DimitrovaIntelligence Augmentation Forum @ Leeds14 June 2010V.G.Dimitrova@leeds.ac.uk

  2. Outline Context - Knowledge elicitation challenges Dialogue agents - Examples - Key components - Example architectures Discussion

  3. Becerra-Fernandez, et al., Knowledge Management, Prentice Hall, 2004 / Additional material, Dekai Wu, 2007 Often used interchangeably Context: Terminology Knowledge elicitation (elicit knowledge from humans) Knowledge acquisition (broader sources – humans, documents) Becerra-Fernandez, et al., Knowledge Management, Prentice Hall, 2004 Additional material, Dekai Wu, 2007.

  4. Knowledge Elicitation Challenges • Most knowledge is in the heads of experts • Experts have vast amounts of knowledge • Experts have a lot of tacit knowledge • Tacit knowledge is hard (impossible) to describe • Experts are very busy and valuable people • Each expert doesn't know everything • People see the world from different and changing perspectives • There is often no consensus what is wrong and what is right Find a tractable, effective, and efficientway to articulate some part of a person’s conceptualisation and align to conceptualisations by other people. Adapted from http://www.epistemics.co.uk/Notes/63-0-0.htm

  5. Dialogic Aproach • Exploit dialogue agents to facilitate the articulation and alignment of people’s conceptualisations Scenario 1: Dialogue agent to help elicit a human’s knowledge

  6. Dialogic Aproach • Exploit dialogue agents to facilitate the articulation and alignment of people’s conceptualisations Scenario 2: Dialogue agent to help align different conceptualisations

  7. Why Dialogue? Dialogue is crucial when creating, merging and aligning ontologies - Communication stage present in most methodologies for creating ontologies • Dialogue commonly used in ontology engineering studies Dialogue is critical in multi-agent systems for sharing meaning - Do agents know the same concept, do different concepts actually have same meaning (Williams, 2004) - Agents that do not share the same ontology negotiate meaning (Bailin & Truszkowski, 2002) Williams, A., Learning to Share Meaning in a Multi-Agent System, Autonomous Agents and Multi-Agent Systems, Vol 8(2), 2004 Bailin, S. & Truszkowski, W., Ontology Negotiation: How Agents Can Really Get to Know Each Other. WRAC 2002: 320-334.

  8. Dialogue Agents Intelligent agents which can engage in a dialogue with a user Types of dialogue: • Task-based (help users complete tasks, e.g. buy a ticket, book a room) • Tutoring (support learning – explanation, meta-cognition, motivation) • Diagnostic (diagnose user’s state, e.g. medical diagnosis) • Information seeking (provide answers to user’s questions) • Negotiation (decision making agents) • Interactive user modelling (extract a user model)

  9. Dialogue Agents: Examples See demos: • Roomline: task-based dialogue (booking a room) • AUTOTUTOR: tutoring dialogue (learning basic computer skills) • Gnututor: tutoring dialogue (learning basic concepts) • RIA: information seeking (finding properties) Earlier work @ Leeds: • STyLE-OLM: user modelling (diagnosing user’s conceptual knowledge, conceptual graphs) • OWL-OLM (SWALE): user modelling (diagnosing user’s conceptual knowledge, OWL) STyLE-OLM reference: Dimitrova, V., Interactive Open Learner Modelling, International Journal of AI in Education, IJAIED, 2003 OWL-OLM reference: Aroyo, L., Denaux, R., Dimitrova, V., Pye, M., Interactive Ontology-Based User Knowledge Acquisition: A Case Study. ESWC 2006: 560-574

  10. Learning technical terminology

  11. Main Components Dialogue Management Focus maintenance (local & global) Interpretation of user utterance Management of dialogue commitments Decide what to say next User Utterance Dialogue moves (intention & proposition) Communicative acts Computer Utterance Dialogue moves (intention & proposition) Communicative acts

  12. U p d a t I n g t h e U s e r M o d e l C o m m u n i c a t i o n M e d i u m Dialogue Games Model System and User’s Reasoners Commitment Rules Belief Stores User Model Beliefs Misunder-standings Miscon-ceptions Game Rules Tactics and Strategies Domain Ontology STyLE-OLM

  13. Example Dialogue Games in STyLE-OLM

  14. Eliciting a User Model from the Belief Stores in STyLE-OLM User's Commitment Store System's Commitment Store System’s Reasoners User’s Reasoners DomainOntology Resultant UM C O N F L I C T S Finding Agreements and Conflicts A G R E E M E N T S Updating the User Model

  15. Dialogue Acts Dialogue Acts Ontology Lexicon Participants Social state Dialogue history Conversation model Layered Information States (Traum et al., 2006) • Layer consists of • Information State components (state of interaction) • Dialogue Acts (Packages of changes to information state) Input Utterance Recognition Rules Update Rules Info State Components Selection Rules Output Utterance (verbal and nonverbal) Realization Rules Dialogue Manager David Traum, Interactive Dialogue for Simulation with Virtual Characters,http://graphics.usc.edu/~suyay/class/Slides/CS597-10-23-06.ppt

  16. Modular Acrhitecture (Zinn et al., 2002) Claus Zinn, Johanna D. Moore, Mark G. Core, A 3-tier Planning Acrhitecture for Managing Tutoring Dialogue, Proceedings of ITS2002, Springer, LNCS.

  17. 3-tear response generation (Zinn et al., 2002) Claus Zinn, Johanna D. Moore, Mark G. Core, A 3-tier Planning Acrhitecture for Managing Tutoring Dialogue, Proceedings of ITS2002, Springer, LNCS.

  18. Summary: Dialogic Approach Dialogic Approach: Potential - Efficient - Independent from the knowledge representation formalism - Depth versus breath Dialogic Approach: Challenges - Computationally expensive (fidelity vs tractability) - Managing confusion (uncertainty) - Multiple participants (perspectives)

  19. Dialogue and Knowledge Capture • Scenario 1: • - Dialogue to assist ontology engineering • Dialogue to capture user experience • Dialogue to capture user context • Scenario 2: • - Dialogue to initiate clarification • Dialogue to point at similarities and differences • Argumentation strategies

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