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Investigate synergies between instructional systems and acquisition tools from the USC Information Sciences perspective. Focus on developing knowledge bases, detecting gaps/errors, and guiding users through complex changes. Explore how new knowledge fits using background theories. A comprehensive overview of representative knowledge acquisition tools and their functions.
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Interactive Knowledge Acquisition Tools:A Tutoring Perspective Yolanda Gil Jihie Kim USC/Information Sciences Institute August 9, 2002
Motivation: Investigate Synergies between Instructional Systems and Acquisition Tools SOFTWARE USER ? Instructional System teaches Good Tutoring Principles Acquisition Tool Good Learning Principles teaches ?
Our Previous Work in Knowledge Acquisition:The EXPECT Project at USC/ISI: • EXPECT architecture for knowledge-based systems exploits highly declarative representations • [Swartout & Gil, KAW-95], [Gil & Melz, AAAI-96] [Blythe et al, IUI-01] • http://www.isi.edu/expect • Research focus: interactive knowledge acquisition (KA) tools that help end users to develop knowledge bases • Deriving models of knowledge interdependencies to detect knowledge gaps and errors [Kim & Gil, AAAI-99] [Kim & Gil, IUI-2000] [Kim & Gil, AAAI-2000] • KA dialogue scripts to guide users by following up on effects of complex changes [Gil & Tallis, AAAI-97] [Tallis & Gil, AAAI-99] [Tallis, IJHCS-2001] • Exploiting background theories to understand how new knowledge fits [Blythe, IJCAI-2001] [Blythe, AAAI-02]
EXPECT: A User-Centered Framework for Developing KBSs EXPECT Ontologies and Method libraries Knowledge Base Method instantiator Domain ontologies Domain ontologies and factual knowledge CYC/Sensus Upper Problem solving methods Evaluations and Critiques Plans (PLANET) Domain dependent KBS Resources (OZONE) Evaluation PSMs KA tools Interdependency Model (IM) KBS compiler EMeD PSMTool Dialogue plans (KA Scripts) NL Editor Knowledge-Based System Instrumentation
Brief Overview of Representative KA Tools (I) • CHIMAERA[McGuinness et al 2000] • Acquisition of concepts, relations, instances • Diagnoses faulty definitions • EXPECT[Blythe et al 2001] • Acquisition of problem solving knowledge • Exploits dialogue scripts, interdependency models, bg k • INSTRUCTO-SOAR[Huffman & Laird 1995] • Acquisition of task models in Soar • Situated NL instruction is mapped to PSCM [Newell et al. 1991] • KSSn[Gaines & Shaw 1993] • Acquisition of concepts, rules, data • Based on personal construct psychology [Kelly 1955] • PROTOS[Bareiss et al 1990] • Acquisition and classification of new cases • Learning indexes to categories
Brief Overview of Representative KA Tools (II) • SALT[Marcus & McDermott 1989] • Acquisition of constraints and fixes for configuration design • Exploits Problem Solving Method/ Task (Role-limiting approach) • SEEK2[Ginsberg et al. 1985] • Acquisition of rules • Uses verification and validation techniques • SHAKEN[Clark et al. 2001] • Acquisition of process models • User interaction based on concept maps [Novak 1977] • TAQL[Yost 1993] • Acquisition of SOAR rules • Editor for high level language for PSCM [Newell et al. 1991] • TEIREISIAS[Davis 1979] • Acquires and classifies new cases • Learning indexes to categories
Open Challenges in KA • Users remain largely responsible for the acquisition process • Decide where, what, when, how, why to enter knowledge • System checks errors, may have some short-term acquisition goals • Ideally, KA tools should have student-like skills: • Formulate and pursue learning goals • Keep track of lessons and progress • Assess how much they are learning and how useful k is • If teacher is not so great, still capable of learning
Instructional Systems and Acquisition Tools: What Are the Synergies? SOFTWARE USER ? Instructional System Supplement Student’s limitations teaches Good Tutoring Principles Acquisition Tool Supplement Teacher’s limitations Good Learning Principles teaches ?
Tutoring and Learning Principles Relevant to KA [Kim & Gil, ITS 02] (I)
Tutoring and Learning Principles Relevant to KA [Kim & Gil, ITS 02] (II)
Five Main Functions of KA Tools KNOWLEDGE ACQUISITION BACKEND ASSIMILATE INSTRUCTION TRIGGER GOALS USER INTERFACE PROPOSE STRATEGIES PRIORITIZE GOALS & STRATEGIES PRESENTATION DESIGN Knowledge Base
Guidance Exploited by KA Tools Guidance from Knowledge Base KNOWLEDGE ACQUISITION BACKEND Problem Solving & Task Knowledge Domain Knowledge ASSIMILATE INSTRUCTION General Background Knowledge TRIGGER GOALS USER INTERFACE Example Cases PROPOSE STRATEGIES Guidance from Meta Knowledge Knowledge Repres. Model PRIORITIZE GOALS & STRATEGIES Diagnosis & Debugging Principles PRESENTATION DESIGN Tutoring & Learning Principles
Acqu. goals Acqu. strats Guess Generators Priority Schemes Interaction Guidelines Tutoring and Learning Principles in KA Tools: Basic Conceptual Framework KNOWLEDGE ACQUISITION BACKEND USER INTERFACE ASSIMILATE INSTRUCTION Operational Principles Knowledge Editor TRIGGER GOALS General Tutoring & Learning Principles • Dialogue • Goals & Strats • State • Suggestions • History PROPOSE STRATEGIES PRIORITIZE GOALS & STRATEGIES PRESENTATION DESIGN Knowledge Base
Tutoring and Learning Principles Implicit in KA tools Tutoring/Learning principle Assimilate Instruction Trigger Goals Design Presentation Propose Strategies Prioritize Goals & Strats Introduce topics & goals EXPECT, SEEK2 Use topics of the lesson as a guide SALT SEEK2 EXPECT SALT Subsumption to existing cog. structure PROTOS PROTOS, SALT TEIREISIAS Immediate feedback PROTOS INSTRUCTO-SOAR TEIREISIAS EXPECT Generate educated guesses TEIREISIAS EXPECT Keep on track Indicate lack of understanding INSTRUCTO-SOAR INSTRUCTO-SOAR Detect and fix “buggy” K TAQL EXPECT,CHIMERA Learn deep models Learn domain language Keep track of answers SEEK2 Prioritize learning tasks EXPECT Limit the nesting of lessons Summarize what is learned Assess learned knowledge KSSn
Tutoring and Learning Principles in KA Tools • Observation: Some learning and tutoring principles are used in some aspects of the dialogue by some tools Opportunity: Incorporate principles more thoroughly in all aspects of the dialogue • Observation: These principles are implicit in the tool’s code and thus are limited Opportunity: Exploit declarative representations of learning state, goals, and strategies
KB SLICK (Skills for Learning and Interactively Capture Knowledge) USER INTERFACE KNOWLEDGE ACQUISITION BACKEND SLICK Dialogue Manager Proactive Dialogue Window Active Acquisition Goals & Strategies Awareness Annotations Tutoring & Learning Principles KB State Dial. History
Conclusions • Analysis of existing KA tools shows they use tutoring/learning principles • Sparsely • Implicitly • Current capabilities of KA tools can be improved by: • Representing tutoring/learning principles declaratively • Organizing the dialogue around lesson topics • Keeping track of how knowledge improves through dialogue • Exposing what knowledge has been assimilated and what areas need improvement or testing • Assessing their competence and confidence on question answering