Cox – 8 July 2011

MICHAEL T. COXUMIACS, UNIVERSITY OF MARYLAND, COLLEGE PARK

Toward an Integrated Metacognitive Architecture

http://xkcd.com/

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Why a Metacognitive Architecture?

Why Cognitive Architectures? To better understand the mechanisms of reasoning across

tasks To account for human data To study high-level cognition by specifying the underlying

infrastructureMetacognition because it is especially human and

gets at the nature of what it means to be intelligentIntegrated because many different aspects exist

And much of it is confused And none have put it all together And this is the only way to get at human-level AI

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INTRODUCTIONOUTLINE

COGNI TI VE AND METACOGNI TI VE ARCHI TECTURES

REPRESENTATI ONSTHE SELF-REGULATED LEARNI NG TASK

CONCLUSI ON

Outline

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INTRODUC TIONOUTLI NE

COGNITIVE AND METACOGNITIVE ARCHITECTURES

REPRESENTATI ONSTHE SELF-REGULATED LEARNI NG TASK

CONCLUSI ON

Cognitive and Metacognitive Architectures

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Action and Perception Cycle

Doing Reasoning

from Russell & Norvig, 2002

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ObjectLevel Meta-Level

GroundLevel

Doing Reasoning Metareasoning

ActionSelection

Meta-level Control

PerceptionIntrospectiveMonitoring

Simple Model of Metareasoning

ObjectLevel Meta-Level

GroundLevel

Doing Reasoning Metareasoning

ActionSelection

Meta-level Control

PerceptionIntrospectiveMonitoring

from Cox & Raja (2011)

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The Meta-Cognitive Loop (MCL)

indications failures responses

expectations correctionsMCL

host system

abst

ract

conc

rete

Meta-level ControlIntrospective Monitoring

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from Anderson et al., (2008)

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Meta-AQUA Metacognitive Architecture

TaleSpin

MultistrategyStory

Understanding

MultistrategyLearning

CBR PlannerG

Story Representation

FK

BK

Learning Algorithm Toolbox

Trace

Learning Plans

Execute Learning

caselibrary

XPLibrary

scriptlibrary

is-ahierarchy

Performance SubsystemProblem Generation

Learning Subsystem

Story Input

MemoryLearning

Algorithms

Learning Goals

XPs

∆BK

Introspective Monitoring

Meta-levelControl

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from Cox & Ram (1999)

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INTRO: The INitial inTROspective Agent

Ground Level

Object Level

Object Level

Object and Meta-Level

from Cox (2007)

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Cognitive Model

Domain

Intend

Act (& Speak)

Plan

Evaluate

Perceive (& Listen)

Interpret

Goals

from Norman (1986)

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MemoryWorld Model

Plans

Semantic Memory

Episodic Memory

Visual Memory

goal change goal inputresolve

anomalygoal

subgoal

ProblemSolving

Explanation

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Metacognitive Model

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7Mental Domain

goal change goal input

Intend

Control

Plan

Evaluate

Monitor

Interpret

Meta Goals

Meta-LevelControl

Introspective Monitoring

subgoal

MemoryReasoning Trace

Strategies

Episodic Memory

Metaknowledge

Self Model

resolve anomaly

goal

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An Integrated Metacognitive Architecture

Domain

MemoryWorld Model

Plans

Semantic Memory

Episodic Memory

Visual Memory

ProblemSolving

Explanation

goal change goal inputresolve

anomalygoal

Intend

Act (& Speak)

Plan

Evaluate

Perceive (& Listen)

Interpret

Goalssubgoal

4Mental Domain

Intend

Control

Plan

Evaluate

Monitor

Interpret

Meta Goals

Meta-LevelControl

Introspective Monitoring

subgoal

MemoryReasoning Trace

Strategies

Episodic Memory

Metaknowledge

Self Model

resolve anomaly

goal

Goal Managementgoal change goal input

Cognition

Metacognition

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INTRODUC TIONOUTLI NE

COGNIT I VE AND METAC OGNIT I VE ARC HI TECTURESREPRESENTATIONS

THE SELF-REGULATED LEARNI NG TASKCONCLUSI ON

Representations

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Representations For Mental Traces

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Truth Values on Graph Nodes

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Description

A E G I M

Absent Memory

inFK outFK inFK outBK outBK

Absent Index inFK outFK inFK outBK inBK

Absent Question

inFK outFK outFK x x

Absent Feedback

outFK outFK x x x

X=don’t care

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Partial Ontology for Mental Terms

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Self-Models

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How to represent episodic memory? Case-based reasoning Soar’s episodic memory

How to represent model of self? Physical attributes Mental attributes

Dispositions Attitudes Emotions Intellectual abilities

Social attributes

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INTRODUC TIONOUTLI NE

COGNIT I VE AND METAC OGNIT I VE ARC HI TECTURESREPRESENTATI ONS

THE SELF-REGULATED LEARNING TASKCONCLUSI ON

The Self-Regulated Learning Task

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Task: Self-Regulated Learning (SRL)

SRL focuses on deliberate learningSRL scope is wide and task is difficultSRL has extant data (e.g., Azevedo)The problem of studying for a test

Must master the domain Must understand one’s self

One’s own knowledge One’s own reasoning ability

Must understand the teacher’s priorities

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How to Study for a Test

Reason about the domain (e.g., chemistry)Reason about one’s knowledge of the domainReason about skills in the domain (e.g., lab skills)Reason about reasoning (problem-solving) in the

domainReason about personal strengths and weaknesses

in domain (I struggled with Chem I, so need to work harder; I study best in quiet environments)

Reason about teacher and what is likely to be on test

Reason about resources (e.g., time left to study)

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Task Decomposition I

ContextReading assignment,

take notesAttend lecture, take

notesPerform homeworkStudy for testTake test

Study for testReview notesReview readingsReview old testsPractice problems

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Task Decomposition II

To review readingsMust have indicated

key parts when first read

Integrate notes from lecture

Identify parts needing elaboration

Do elaborationIterate until confident

or no time remaining

Lecture

NotesBasic backgroundKey textKey textPartially understoodPartially understood

Figure Caption

Figure

Homework

Readings

Teacher ModelSelf Model

Time left &not

prepared?

yesno Halt

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Desiderata

System that has self-identity Knows its own strengths and weaknesses Knows what it does not know Knows what it wants for the future Has a memory for what it has done in the past Has a sense of its current physical presence in space

and time (e.g., knows what is graspable) Is self-confident and acts deliberately Can empathize with others Can explain itself to others Generates its own goals (is an independent actor) *Wonders about what happens when it gets turned off

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Self-Description

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INTRODUC TIONOUTLI NE

COGNIT I VE AND METAC OGNIT I VE ARC HI TECTURESREPRESENTATI ONS

THE SELF-REGULATED LEARNI NG TASKCONCLUSION

Conclusion

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Conclusion

A number of different architectures exist that bear on metacognition

None have integrated the many aspects of cognition and metacognition

To do so would capture something uniquely human and at the heart of what it means to be intelligent

This presentation represents a small start