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Transcript of School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Augmenting the...
School of somethingFACULTY OF OTHER
School of ComputingFACULTY OF ENGINEERING
Augmenting the Knowledge Capture Process with Dialogue Agents
Vania Dimitrova
Intelligence Augmentation Forum @ Leeds14 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, 2004Additional 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 person’s conceptualisation
and align to conceptualisations by other people.
Adapted from http://www.epistemics.co.uk/Notes/63-0-0.htm
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
Dialogic Aproach
Exploit dialogue agents to facilitate the articulation and alignment of people’s 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), 2004Bailin, S. & Truszkowski, W., Ontology Negotiation: How Agents Can Really Get to Know Each Other. WRAC 2002: 320-334.
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)
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, 2003OWL-OLM reference: Aroyo, L., Denaux, R., Dimitrova, V., Pye, M., Interactive Ontology-Based User Knowledge Acquisition: A Case Study. ESWC 2006: 560-574
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
Communication
Medium
Dialogue Games ModelUpdatIng
the
User
Model
STyLE-OLM
Commitment
Rules
Game
Rules
Tactics and
Strategies
Belief
Stores
Systemand
User’sReasoners
UserModelBeliefs
Misunder-standings
Miscon-ceptions
DomainOntology
Example Dialogue Games in STyLE-OLM
Eliciting a User Model from the Belief Stores in STyLE-OLM
User'sCommitment
Store
System'sCommitment
Store
Finding Agreements and Conflicts
CONFLICTS
A G R E E M E N T S
Updating the User Model
User’s Reasoners
Resultant UM
DomainOntology
System’s Reasoners
Layered Information States (Traum et al., 2006)
Realization Rules
DialogueActs
InputUtterance
Recognition Rules
Update Rules
Output Utterance(verbal and nonverbal)
Selection Rules
Info StateComponents
Dialogue Manager
DialogueActs
David Traum, Interactive Dialogue for Simulation with Virtual Characters,http://graphics.usc.edu/~suyay/class/Slides/CS597-10-23-06.ppt
Layer consists of• Information State components (state of interaction)• Dialogue Acts (Packages of changes to information state)
OntologyLexicon
ParticipantsSocial state
Dialogue historyConversation 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