Toward an Integrated
Metacognitive Architecture
MICHAEL T. COX
UMIACS, UNIVERSITY OF MARYLAND, COLLEGE PARK
http://xkcd.com/
Cox – 8 July 2011
Why a Metacognitive Architecture?
2
 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
infrastructure
 Metacognition because it is especially human and
gets at the nature of what it means to be intelligent
 Integrated 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
Cox – 8 July 2011
Outline
3
INTRODUCTION
OUTLINE
COGNITIVE AND METACOGNITIVE ARCHITECTURES
REPRESENTATIONS
THE SELF-REGULATED LEARNING TASK
CONCLUSION
Cox – 8 July 2011
Cognitive and Metacognitive
Architectures
4
INTRODUCTION
OUTLINE
COGNITIVE AND METACOGNITIVE ARCHITECTURES
REPRESENTATIONS
THE SELF-REGULATED LEARNING TASK
CONCLUSION
Cox – 8 July 2011
Action and Perception Cycle
5
Doing
Reasoning
from Russell & Norvig, 2002
Cox – 8 July 2011
Simple Model of Metareasoning
6
Action
Selection
Ground
Level
Meta-level
Control
Object
Level
Perception
Doing
Meta-Level
Introspective
Monitoring
Reasoning
Metareasoning
from Cox & Raja (2011)
Cox – 8 July 2011
The Meta-Cognitive Loop (MCL)
concrete
abstract
7
indications
failures
responses
expectations
MCL
corrections
Introspective Monitoring
Meta-level Control
host system
from Anderson et al., (2008)
Cox – 8 July 2011
Meta-AQUA Metacognitive Architecture
8
Problem
Generation
Tale
Spin
Story
Input
Learning Subsystem
Multistrategy
Learning
Performance Subsystem
Multistrategy
G
Story
Understanding
CBR
Trace
Introspective
Monitoring
Memory
FK
case
library
XP
Library
Learning
Algorithms
Learning
Algorithm
Toolbox
Story
Representation
Planner
Learning
Goals
XPs
Learning
Plans
script
library
is-a
hierarchy
BK
Cox – 8 July 2011
∆BK
Execute Learning
Meta-level
Control
from Cox & Ram (1999)
INTRO: The INitial inTROspective Agent
9
Ground Level
Object Level
Object Level
Object and
Meta-Level
from Cox (2007)
Cox – 8 July 2011
Cognitive Model
10
goal change
goal input
subgoal
resolve
anomaly
goal
Goals
Intend
Problem
Solving
Memory
Evaluate
Explanation
World Model
Plan
Plans
Interpret
Semantic Memory
Episodic Memory
Act
Visual Memory
(& Speak)
Perceive
(& Listen)
Domain
from Norman (1986)
Cox – 8 July 2011
Metacognitive Model
11
goal change
goal input
subgoal
Meta Goals
Intend
Memory
resolve
anomaly
goal
Evaluate
Reasoning Trace
Plan
Strategies
Interpret
Episodic Memory
Meta-Level
Control
Introspective
Monitoring
Metaknowledge
Self Model
Monitor
Control
Mental Domain
7
Cox – 8 July 2011
An Integrated
Metacognitive
Architecture
Goal Management
goal change
goal input
subgoal
resolve
anomaly
goal
Meta Goals
Intend
Evaluate
Memory
Reasoning Trace
Strategies
Plan
Metacognition
Interpret
Episodic Memory
Meta-Level
Control
Introspective
Monitoring
Metaknowledge
Self Model
Monitor
Control
Mental Domain
4
goal change
goal input
subgoal
resolve
anomaly
goal
Goals
Intend
Problem
Solving
Memory
Evaluate
Explanation
World Model
Plan
Plans
Interpret
Semantic Memory
Episodic Memory
Cognition
Act
Visual Memory
(& Speak)
(& Listen)
Domain
Cox – 8 July 2011
12
Perceive
Representations
13
INTRODUCTION
OUTLINE
COGNITIVE AND METACOGNITIVE ARCHITECTURES
REPRESENTATIONS
THE SELF-REGULATED LEARNING TASK
CONCLUSION
Cox – 8 July 2011
Representations For Mental Traces
14
Cox – 8 July 2011
Truth Values on Graph Nodes
15
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
Cox – 8 July 2011
Partial Ontology for Mental Terms
16
Cox – 8 July 2011
Self-Models
17
 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
Cox – 8 July 2011
The Self-Regulated
Learning Task
18
INTRODUCTION
OUTLINE
COGNITIVE AND METACOGNITIVE ARCHITECTURES
REPRESENTATIONS
THE SELF-REGULATED LEARNING TASK
CONCLUSION
Cox – 8 July 2011
Task: Self-Regulated Learning (SRL)
19
 SRL focuses on deliberate learning
 SRL scope is wide and task is difficult
 SRL 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
Cox – 8 July 2011
How to Study for a Test
20
 Reason about the domain (e.g., chemistry)
 Reason about one’s knowledge of the domain
 Reason about skills in the domain (e.g., lab skills)
 Reason about reasoning (problem-solving) in the
domain
 Reason 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)
Cox – 8 July 2011
Task Decomposition I
21
Context
 Reading assignment,




take notes
Attend lecture, take
notes
Perform homework
Study for test
Take test
Cox – 8 July 2011
Study for test
 Review notes
 Review readings
 Review old tests
 Practice problems
Task Decomposition II
22
To review readings
Readings
 Must have indicated key




parts when first read
Integrate notes from
lecture
Identify parts needing
elaboration
Do elaboration
Iterate until confident or
no time remaining
Cox – 8 July 2011
Lecture
Notes
Basic background
Key text
Key text
Partially understood
Partially understood
Homework
Figure
Figure
Caption
Self Model
yes
Teacher Model
Time left &
not prepared?
no
Halt
Desiderata
23
 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
Cox – 8 July 2011
Self-Description
24
Cox – 8 July 2011
Conclusion
25
INTRODUCTION
OUTLINE
COGNITIVE AND METACOGNITIVE ARCHITECTURES
REPRESENTATIONS
THE SELF-REGULATED LEARNING TASK
CONCLUSION
Cox – 8 July 2011
Conclusion
26
 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
Cox – 8 July 2011