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

V.G.Dimitrova@leeds.ac.uk

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