School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Augmenting the Knowledge Capture Process with Dialogue Agents Vania Dimitrova Intelligence Augmentation Leeds 14 June 2010

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

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

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 efficient way to articulate some part of a persons conceptualisation and align to conceptualisations by other people. Adapted from

Dialogic Aproach Exploit dialogue agents to facilitate the articulation and alignment of peoples conceptualisations Scenario 1: Dialogue agent to help elicit a humans knowledge

Dialogic Aproach Exploit dialogue agents to facilitate the articulation and alignment of peoples conceptualisations Scenario 2: Dialogue agent to help align different conceptualisations

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:

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 users state, e.g. medical diagnosis) Information seeking (provide answers to users questions) Negotiation (decision making agents) Interactive user modelling (extract a user model)

Dialogue Agents: Examples See demos: Roomline: task-based dialogue (booking a room)Roomline AUTOTUTOR: tutoring dialogue (learning basic computer skills)AUTOTUTOR Gnututor: tutoring dialogue (learning basic concepts)Gnututor RIA: information seeking (finding properties)RIA Earlier Leeds: STyLE-OLM: user modelling (diagnosing users conceptual knowledge, conceptual graphs) OWL-OLM (SWALE): user modelling (diagnosing users 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:

Learning technical terminology

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

CommunicationMediumCommunicationMedium Dialogue Games Model UpdatIngtheUserModelUpdatIngtheUserModel STyLE-OLM Commitment Rules Game Rules Tactics and Strategies Belief Stores System and Users Reasoners User Model Beliefs Misunder- standings Miscon- ceptions Domain Ontology

Example Dialogue Games in STyLE-OLM

Eliciting a User Model from the Belief Stores in STyLE-OLM User's Commitment Store System's Commitment Store Finding Agreements and Conflicts CONFLICTSCONFLICTS A G R E E M E N T S Updating the User Model Users Reasoners Resultant UM Domain Ontology Systems Reasoners

Layered Information States (Traum et al., 2006) Realization Rules Dialogue Acts Input Utterance Recognition Rules Update Rules Output Utterance (verbal and nonverbal) Selection Rules Info State Components Dialogue Manager Dialogue Acts David Traum, Interactive Dialogue for Simulation with Virtual Characters, Layer consists of Information State components (state of interaction) Dialogue Acts (Packages of changes to information state) Ontology Lexicon Participants Social state Dialogue history Conversation model

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.

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.

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)

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