統合認知アーキテクチャの魅力と困難

ACT-Rを利用した研究事例
森田 純哉
北陸先端科学技術大学院大学

共同研究者: 三輪和久、前東、寺井仁、小島一晃、Frank Ritter、橋本敬、金野武アウトライン• 統合認知アーキテクチャとは何か？
• ACT-Rのレビュー• 研究事例• 人工物とのインタラクション• 他者とのインタラクション• ACT-Rの魅力と困難• 認知の統合理論＝統合認知アーキテクチャ• 心を知覚と運動を含めた1つのシステムとみなす

Newell, A. (1994). Unified Theories of Cognition,
Harvard University Press; Reprint edition

Features↓  vs.  Architectures→(download this table)

4D/RCS

ACT-R

ART

BECCA

biSoar

CERA-CRANIUM

ChrestClarionCogPrime

CoJACK

Disciple

Epic

FORR

GLAIR

GMU-BICAHTM

LeabraLIDA

NARS

Nexting

Pogamut

Polyscheme

Recommendation Architecture

REM

Soar

Ymir

Contributors to this table: →Curator: Alexei Samsonovich
Last update: 2010-09-20
Iconic link to diagramsJames Albus

Andrea Stocco andChristian Lebiere

Stephen Grossberg

Brandon Rohrer, updated 8/3/2011

B. Chandrasekaran and
U. Kurup

Raul Arrabales

Fernand Gobet and Peter Lane

Ron Sun

Ben Goertzel

Frank Ritter & Rick Evertsz

George Tecuci

Shane Mueller, Andrea Stocco

Susan L. Esptein

Stuart C. Shapiro

Alexei Samsonovich

Jeff Hawkins

David C. Noelle

Stan Franklin

Pei Wang

Akshay Vashist, Shoshana Loeb

Cyril Brom

Nick Cassimatis

L. Andrew Coward

Ashok K. Goel, J. William Murdock, andSpencer Rugaber

Andrea Stocco / John Laird

Kristinn R. Thórisson

other architectures toconsider including:IcarusSAL
Tosca

Basic Overview
Knowledge and experiences are
represented using

images, maps, objects, events,
state, attributes, relationships,
situations, episodes, frames

Main components

Behavior Generation, World
Chunk-based declarative
Many model brain regions, notably laminar
Modeling, Value Judgment,
memory; buffers; procedural cortical and thalamic circuits
Sensory Processing, Knowledge
knowledge encoded as
Database. These are organized in a productions
hierarchical real-time control
system (RCS) architecturePrimary original reference(s)Chunks and productions,

Visual 3D boundary and surface
representations; auditory streams; spatial,
object, and verbal working memories; list
chunks; drive representations forreinforcement learning; orienting system;
expectation filter; spectral timing networks

Anderson, J.R., & Lebiere, C. Grossberg, S. (1987). Competitive learning:(1998). The Atomic
From interactive activation to adaptive
Components of Thought.
resonance. Cognitive Science 11: 23-63.
Mahwah: Lawrence Erlbaum
Associates.

Other key references

Most recent representative reference(s)http://cns.bu.edu/~steve

Albus, J. S., and Barbera, A. J.
Anderson, 2007(2005) "RCS: A cognitive
architecture for intelligent multiagent systems," Annual Reviews inControl, Vol. 29, Issue 1 87-99

Implementation

http://www.isd.mel.nist.gov/project Lisp, TCL/Tks/rcslib/
C++, Windows Real-time, VXworks,
Neutral Messaging Language(NML),
Mobility Open Architecture
Simulation and Tools (MOAST)
http://sourceforge.net/projects/mo
ast/
Urban Search and Rescue
Simulation (USARSim)
http://sourceforge.net/projects/usa
rsim/

Funding program, project andenvironment in which the architecture
was applied(added by Jim Albus)

Please see the long list:
http://members.cox.net/bica2009/
cogarch/4DRCS.pdf

Nonlinear neural networks (feedback,multiple spatial and temporal scales)

Perceptual experiences are represented features and combinationsof features. Knowledge about the world is represented astransitions between experiences.

The representational
Single percepts, complex
framework of Soar plus
percepts, and mission
diagrams  –  the  percepts. Single anddiagrammatic part cancomplex behaviors.
also be combined withany symbolic generalarchitecture, such as ACTR

chunks and productions

chunks, rules, and NNs

BECCA is a solution to the general reinforcement learning problem. DRS, for diagram
Physical layer global
It consists of two parts, an unsupervised feature creator and a
representation; perceptual workspace, Missionmodel-based incremental learner. Both are incremental and on-line, and action routines to get specific layer global
designed for a physically embodied agent operating in an
information from andworkspace, core layer
unstructured environment.
create/modify diagrams.
contextualization,
attention, sensory
prediction, statusassessment, goal
management.

Attention, sensory memory, short-term
memory, long-term memory

procedural knowledge
The primary knowledge store is the AtomSpace, a neural- Beliefsets for long term memory,and declarative
symbolic  “weighted  labeled  hypergraph”  with  multiple   plans, intentions, events, goals
knowledge (each in both cognitive processes acting on it (in a manner carefully
implicit and explicit
designed  to  manifest  cross-­‐process  “cognitive  forms --- rules and NNs) synergy”),  and  other  specialized  knowledge  stores  indexed by it. The cognitive processes are numerous but
include: an uncertain inference engine (PLN,
Probabilistic Logic Networks), a probabilistic
evolutionary program learning engine (MOSES,
developed initially by Moshe Looks), an attention
allocation algorithm (ECAN, Economic Attention
Networks, which is somewhat neural net like), concept
formation and blending heuristics, etc. Work is underway  to  incorporate  a  variant  of  Itamar  Arel’s  DeSTIN  system as a perception and action layer. Motivation andemotion  are  handled  via  a  variant  of  Joscha  Bach’s  MicroPsi framework called CogPsi.

B Rohrer, "An implemented architecture for feature creation andUnmesh Kurup & B.
Arrabales, R. Ledezma, A.
general reinforcement learning," Workshop on Self-Programming
Chandrasekaran,and Sanchis, A. "A
in AGI Systems, Fourth International Conference on Artificial"Modeling Memories of
Cognitive Approach toGeneral Intelligence, Mountain View, CA, Aug 3-6, 2011. (1MB pdf) Large-scale Space Using a Multimodal Attention".
http://www.sandia.gov/rohrer/doc/Rohrer11ImplementedArchitectur Bimodal Cognitive
Journal of PhysicaleFeature.pdf
Architecture."
Agents. Volume 3. Issue 1.
Proceedings of thePages 53-64. January
International Conference 2009.
on Cognitive Modeling
July 27-29, 2007, Ann
Arbor, MI. (CD-ROM, 6
pages.)

Feigenbaum, E. A., & Simon, H. A. (1962). A
theory of the serial position effect. British
Journal of Psychology, 53, 307-320.

Sun, R. (2004). The
CLARION cognitive
architecture: Extending
cognitive modeling toGobet, F. & Lane, P. (2005). The CHREST
social simulation. In: Ronarchitecture of cognition: Listening to
Sun (Ed.), Cognition andempirical data. In D. Davis (Ed.), Visions ofMulti-Agent Interaction.
mind: Architectures for cognition and affect. Cambridge University(pp. 204-224). Hershey, PA: Information
Press: New York.
Science Publishing.

Kieras, D. & Meyer, D.E. (1997).
Susan L. Epstein. 1994. For the Right Reasons: The FORR
An overview of the EPIC
Architecture for Learning in a Skill Domain. Cognitive
architecture for cognition andScience, 18(3): 479-511.
performance with application tohuman-computer interaction.
Human-Computer Interaction.,
12, 391-438. ; Meyer, D.E., &Kieras, D.E. (1997). A
computational theory of
executive cognitive processes andmultiple task performance: Part I.
Basic mechanisms. Psychological
Review 63: 81-97.

www.sandia.gov/rohrer

Gobet, F. & Lane, P. (2005). The CHREST
architecture of cognition: Listening toempirical data. In D. Davis (Ed.), Visions ofmind: Architectures for cognition and affect.
(pp. 204-224). Hershey, PA: Information
Science Publishing.

Kieras, D. EPIC Architecture
Epstein, S. L. and S. Petrovic. 2010 Learning Expertise
Principles of Operation.
with Bounded Rationality and Self-awareness. In
ftp://www.eecs.umich.edu/people/ Autonomous Search. Y. Hamadi, E. M., F. Saubion, editors.
kieras/EPICtutorial/EPICPrinOp.pd Springer.
fB. Chandrasekaran.
Multimodal Cognitive
Architecture:
Making Perception MoreCentral to Intelligent
Behavior. Proceedings of
the AAAI National
Conference on Artificial
Intelligence, 2006, pp.
1508-1512.

www.sandia.gov/rohrer
B Rohrer, M Bernard, JD Morrow, F Rothganger, P Xavier, "Modelfree learning and control in a mobile robot," 5th Intl Conf on
Natural Computation, Tianjin, China, Aug 14-16, 2009.
B
Rohrer, JD Morrow, F Rothganger, PG Xavier, "Concepts from data,"
Brain-Inspired Cognitive Architectures Symposium, AAAI Fall
Symposium Series, Washington DC, Nov 5-7, 2009.

MATLAB, Python

Arrabales, R. Ledezma, A.
and Sanchis, A. "CERACRANIUM: A Test Bed for
Machine Consciousness
Research". International
Workshop on Machine
Consciousness. HongKong. June 2009.

Implemented in the Soarframework

Gobet, F., Lane, P.C.R., Croker, S., Cheng, P. C- http://www.cogsci.rpi.ed
H., Jones, G., Oliver, I., and Pine, J. M.
u/~rsun/clarion.html
Chunking mechanisms in human learning.
Trends in Cognitive Science, 5:236-243, 2001.
Gobet, F., & Lane, P. C. R. (2010). The
CHREST architecture of cognition: The role ofperception in general intelligence. The Third
Conference on Artificial General Intelligence.
Lugano, Switzerland.

CogPrime is a multi-representational system. The core
Beliefs-Desires-Intentions (BDI)
representation consists of a hypergraphs with uncertain architecture with events,
logical relationships and associative relations operating plans/intentions (procedural),
together. Procedures are stored as functional programs; beliefsets (declarative) andepisodes  are  stored  in  part  as  “movies”  in  a  simulation  activation levels
engine; and there are other specialized methods too.

Goertzel, Ben (2009). OpenCogPrime: A Cognitive
Synergy Based Architecture for Embodied GeneralIntelligence, Proceedings of ICCI-2009. See also
http://opencog.org
Goertzel, Ben et al. (2010). OpenCogBot: Achieving
Generally Intelligent Virtual Agent Control and
Humanoid Robotics via Cognitive Synergy, Proceedingsof ICAI-10, Beijing

Production Rules, Working
memory entries

Descriptives are shared knowledge resources computed SNePS, simultaneously a logic-based,
schemas, mental states
on demand and refreshed only when necessary Advisors assertional frame-based, and propositional
are domain-dependent decision rationales for actions.
graph based representation
Measurements are synopses of problem solving
experiences.

Sparse distributed representations.
patterns of neural firing rates and patterns of synaptic strengths. Sensory events
Memory and representations are
drive patterns of neural activation, and such activation-based representations may
distributed across a hierarchy of nodes.
drive further processing and the production of actions. Knowledge that is retained
Within each node representations are large for long periods is encoded in patterns of synaptic connections, with synaptic
sparse binary vectors.
strengths determining the activation patterns that arise when knowledge or
previous experiences are to be employed.

Perceptual knowledge - nodes and links in a beliefs, tasks, and concepts
Slipnet-like net with sensory data of various
types attached to nodes;
Episodic knowledge - boolean vectors(Sparce Distributed Memory;
Procedural knowledge - schemes a la
Schema Mechanism

Cognitive processor (including
production rule interpreter andworking memory), long term
memory, production memory,detailed perceptual-motor
interfaces (auditory processor,
visual processor, ocular motor
processor, vocal motor
processor, manual motor
processor, tactile processor)

Advisors are organized into three tiers. Tier-1 Advisors are 1) Knowledge Layer containing Semantic memory systems: working,fast and correct, recommend individual actions, and are
Memory, Episodic Memory, Quantified & semantic, episodic, procedural,
consulted in a pre-specified order. Tier-2 Advisors trigger conditional beliefs, Plans for non-primitive iconic (I/O); plus: cognitive map,in the presence of a recognized situation, recommend
acts, Plans to achieve goals, Beliefs about reward system, the engine(possible partially ordered) sets of actions, and arepreconditions & effects of acts, Policies
consulted in a pre-specified order. Tier-3 Advisors are(Conditions for performing acts), Selfheuristics, recommend individual actions, and areknowledge, Meta-knowledge; 2) Perceptuoconsulted together. Tier-3 Advisors' opinions express
Motor Layer containing implementations
preference strengths that are combines with weights
of primitive actions, perceptual structuresduring voting to select an action. A FORR-based system that ground KL symbols, deictic andlearns those weights from traces of its problem-solving
modality registers; 3) Sensori-Actuator
behavior.
Layer containing sensor and effector
controllers.

HTM is a biologically constrained model of
neocortex and thalamus. HTM models
cortex related to sensory perception,
learning to infer and predict from highdimensional sensory data. The model
starts with a hierarchy of memory nodes.
Each node learns to pool spatial patternusing temporal contiguity (uising variable
order sequences if appropriate) as theteacher. HTMs are inherently modality
independent. Biologically the model maps
to cortical regions, layers of cells, columnsof cell across the layers, inhibitory cells,
and non-linear dendrite properties. All
representations are large sparse
distributions of cell activities.

At the level of gross functional anatomy, most Leabra models employ a tripartite
view of brain organization. The brain is coarsely divided into prefrontal cortex, thehippocampus and associated medial-temporal areas, and the rest of cortex -"posterior" areas. Prefrontal cortex provides mechanisms for the flexible retention
and manipulation of activation-based representations, playing an important role inworking memory and cognitive control. The hippocampus supports the rapid
weight-based learning of sparse conjunctive representations, providing central
mechanisms for episodic memory. The posterior cortex mostly utilizes slow
statistical learning to shape more automatized cognitive processes, including
sensory-motor coordination, semantic memory, and the bulk of language
processing. At a finer level of detail, other "components" regularly appear in Leabrabased models. Activation-based processing depends on attractor dynamics
utilizing bidirectional excitation between brain regions. Fast pooled lateral
inhibition plays a critical role in shaping neural representations. Learning arises
from an associational "Hebbian" component, a biologically plausible error-driven
learning component, and a reinforcement learning mechanism dependent on thebrain's dopamine system.

Cognitive cycle (action-perception cycle)
an inference engine and an
acting as a cognitive atom. Higher level
integrated memory andprocesses implemented as behavior streams. control mechanism
Cognitive cycle includes sensory memory,
perceptual associative memory, workspace,transient episodic memory, declarative
memory, global workspace, procedural
memory, action selection, sensory motor
memoryO’Reilly,  R.C.  (1996),  ‘Biologically  Plausible  Error-­‐Driven  Learning  Using  Local  Activation  Differences:  The  Generalized  Recirculation  Algorithm’,  Neural  Computation,  8,  895–938.

Franklin, S. and F. G. Patterson, Jr. (2006).
The Lida Architecture: Adding New Modesof Learning to an Intelligent, Autonomous,
Software Agent. Integrated Design andProcess Technology, IDPT-2006, San Diego,
CA, Society for Design and Process Science.

Evertsz, R., Pedrotti, M., Busetta,
P., Acar, H., & Ritter, F. E. (2009).
Populating VBS2 with realistic
virtual actors. In Proceedings ofthe 18th Conference on Behavior
Representation in Modeling and
Simulation. 1-8. 09-BRIMS-04.

.Net Framework, code
Lisp, Java
written in C#. Runtime
based on CCR(Concurrency and
Coordination Runtime)and DSS (Decentralized
Software Services), part of
Robotics Developer
Studio 2008 R2.
http://www.consciousrobots.com/en/roboticsstudio/2.html

Java

The OpenCogPrime system implements CogPrime within overlay to JACK®
the open-source OpenCog AI framework, see
http://opencog.org. The implementation is mostly C++
for Linux, some components in Java; also a Scheme shellis used for interacting with the system.

Sandia National Labs, internal R&D funding

Disciple was initially implemented in Lisp and iscurrently implemented in Java

Samsonovich, A. V. and De Jong, George D, Hawkins J (2009) Towards a
K. A. (2005). Designing a selfMathematical Theory of Cortical Microaware neuromorphic hybrid. In circuits. PLoS Comput Biol 5(10). "On
K.R. Thorisson, H. Vilhjalmsson, Intelligence" Jeff Hawkins with Sandra
and S. Marsela (Eds.). AAAI-05
Blakeslee, 2005
Workshop on Modular
Construction of Human-LikeIntelligence: AAAI Technical
Report, volume WS-05-0

www.numenta.com

In Progress (C++, Java, Perl,
Prolog)Java, Python; features ACT-R Javabinding and Emergent
binding

SmalltalkCommon Lisp, using Loom as the
C with interfaces to almost any
underlying knowledge-engine; Java version language; Javain development

CommonLisp, C, C++, 8 networked computers, sensing
hardware

Many Leabra working memory models have been published, mostly focusing on the Explicitly included as a workspace withthe active part of the memoryrole of the prefrontal cortex in working memory.
significant internal structure including acurrent situational model with both real andvirtual windows, ans a conscious contents
queue,

Yes

declarative

Each specialist implement its
own.

Yes. Frequency modulation placed on neuron
spike outputs, with different modulation phasefor different objects in working memory.

No commitment to a specific theory ofworking memory

Many Leabra models of the learning and use of semantic knowledge, abstracted
from the statistical regularities over many experiences, have been published. These
include some language models.

Implemented automatically as part ofdeclarative memory via sparse distributed
memorythe whole memory is semantic

framesnone

Each specialist implement its
own.

Uses Powerloom as underlying knowledge Relational graph structures
engine; OWL based ontologicalrepresentation

FramesMany Leabra models of episodic memory have been published, mostly focusing on
the role of the hippocampus in episodic memory.

Both declarative memory and transient
episodic memory encoded via sparse
distributed memory

the part of memory that contains
temporal information

Yes (Implicit)

declarative or a spreading
activation network

Each specialist implement its
own.

Yes. Similarity circumstances (= cortical column
receptive fields) that are often detected at thesame time acquire ability to indirectly activate
each other.
Yes. Similarity circumstances (= cortical column
receptive fields) that change at the same timeacquire ability to indirectly activate each other.
An episodic memory is indirect activation of agroup of columns that all changed at the sametime at some point in the past. Because thehippocampal system manages the selection ofcortical columns that will change in response toeach sensory experience, information used forthis management is applicable to construction of
episodic memories

Yes: Defined as traces through theprocedural memory, below

Functional Sketchboard, Content Blackboard, Motor Feedback
Blackboard

Yes. Recommendation weights associated in thebasal ganglia with cortical column receptive field
detections instantiate procedural memory

Yes: Defined as tasks, which are functional Rules
elements, and methods, which are
behavioral elements

NuPIC development environment available "Emergent" open source software written largely in C++ (runs on manyfor PC and Mac. It is available under a free platforms)
research license and a paid commercial
license.

Numenta licesnses its software to various
commercial partners.

Office of Naval Research

National Science Foundation

DARPA IPTO BICA, virtualindoor/outdoor environments

yes

The active beliefs and intentions

Yes

Yes

yes: includes mental states of
the Self

Semantic memory?

yes

Encoded as chuncks

Yes, limited: Associations between chunks

Yes. Semantic information for a feature is obtained from thehistory of the agent's experience.

no (to be implemented)

Yes. Implemented by the network of chunks
in long-term memory.

yes. in both implicit andexplicit forms(chunks/rules and NNs)yes

encocoded as (weighted) beliefs
in beliefsets, uses ACT-R's
declarative memory equations

Not explicitly

In many descriptives

Yes, in SNePS

yes: includes schemas

Episodic memory?

yes

Not explicitly definedYes, limited: Builds on hippocampal spatial
and temporal representations

Yes. Individual transitions from experience to experience are an
episode.

no (to be implemented)

No,  although  “episodic”  links  are  used  in  some simulations.

yes. in both implicit andexplicit forms (chunks
and NNs)yes

Not explicetly defined, but would
be encocoded as beliefs inbeliefsets.

No

Yes, in task history and summary measurements

Yes, temporally-related beliefs in SNePS

yes: includes frozen mental state
assemblies

Procedural memory?

yes

Explicitly definedYes. Multiple explicitly defined neuralsystems for learning, planning ,and control
of action

Yes. Common sequences of transitions are reinforced and are more Extends Soar's procedural yes. Some processors
likely to be executed in the future.
memory to diagrammatic generate single orcomponents
complex behaviors.

Yes. Implemented by productions.

yes. in both implicit andexplicit forms(chunks/rules and NNs)

yes

Plans and intentions withactivation levels

Yes: production rules

Yes, Advisors can be weighted by problem progress, andrepeated sequences of actions can be learned and stored

Yes, in PML implemented in Lisp, could be yes: includes primitives
compiled from KL

cognitive map?

yes

Yes. Networks that learn entorhinal grid celland hippocampal place field representations
on lineNo explicit. The set of all transitions form a world model. This
serves as an implicit cognitive map.

No.

reward system?

Yes, Value Judgment processes
compute cost, benefit, risk

Yes. Model how amygdala, hypothalamus,
Yes. Reward is specified in the definition of the task. It can be an
and basal ganglia interact with sensory and arbitrary function of observed and unobserved states.
prefrontal cortex to learn to direct attention
and actions towards valued goals. Used tohelp explain data about classical andinstrumental conditioning, mental disorders(autism, schizophrenia), and decision making
under risk.

yes. Status assessment
mechanism in core layer.

Yes, Value Judgment processes compute cost, benefit,
risk

Uses ACT-R memory equations,
so memories and plans get
strengthened.

No

Yes; Advisor weights are acquired during self-supervised
learning.

Iconic memory (including Interface andImagery)?

Yes, image and map
representations

Emerges from role of top-down attentive
interactions in laminar models of how thevisual cortex seesno (to be implemented)

Yes.

Intended to emerge implicitly via combination of
sensory, declarative and simulative memory. Not
implemented/tested yet.

not particularly, would beapplication and IO system
specific.

Part of visual perceptual
processor

Yes, in some descriptives

Perceptual memory (if understoodseparately from iconic and working
memory)?

Yes, pixels are segmented andgrouped into entities and events

Yes. Model development of laminar visual
Yes. All different sensory modalities and combinations ofcortex and explain how both fast perceptual modalities are treated the same within BECCA.
learning with attention and awareness, andslow perceptual learning without attention orawareness, can occur.

Yes. Diagrammaticelements of perceptual
memnory

Yes, but only as
preprocessing buffers in
CERA sensor services.

Yes.

Yes,but currently only for vision. Further modalities will
be implemented later.

gets input from the world as
events. these events areprocessed by plans.

Yes

Attention and consciousness

Yes, can focus attention on
regions of interest. Is aware of self
in relation to the environment andother agents.

Yes. Clarifies how boundary, surface, andYes. The most salient feature at each time step is attended.
prototype attention differ and work together
to coordinate object and scene learning.
Adaptive Resonance Theory predicts a link
between processes of Consciousness,
Learning, Expectation, Attention, Resonance,
and Synchrony (CLEARS) and that All
Conscious States Are Resonant States.

Uses Soar's attention
framework

Yes, attention is
implemented as a bias
signal induced from thecore layer to the lower
levels global workspaces.
Other aspects ofconsciousness are also
considered. No explicit
representation of the self
implemented so far.

Attention plays an important role in thearchitecture, as it for example determines thenext eye fixation and what will be learnt.

Yes, can focus attention on regions and topics ofinterest. Is aware of self in relation to the environmentand other agents.

Represented by transient events,
goal structure, andintentions/beliefs whose
activation level is above thethresholdEmergent Phenomenon ofworking memory

Yes, some Advisors attend to specific problem features

Visual input?

Yes, color, stereo

Propositional (based on
chunks)Natural static and dynamic scenes,
Yes
psychophysical displays. Used to develop
emerging architecture of visual system fromretina to prefrontal cortex, including how 3D
boundaries and surface representations
form, and how view-dependent and viewinvariant object categories are learned undercoordinated guidance of spatial and object
attention.

Yes. Diagrams are thevisual input.

Yes, both real cam andVisual input is currently coded as list
synthetic images from the structures or arrays.
simulator.

Interaction of visual motor andvisual perceptual processors

Possible but not implemented

Auditory input?

No, not yet

Propositional (based on
chunks)Special modalities?

Yes, LADAR, GPS, odometry,inertial

ERE
Gat
Guardian
H-Cogaff
Homer
Imprint
MAX
Omar
PRODIGY
PRS
Psi-Theory
R-CAST
RALPH-MEA
Society of Mind
Subsumption architecture
Teton
Theo

Ng-Thow-Hing, V., K. R. Thórisson, R. K. Sarvadevabhatla, J.
Wormer and Thor List (2009). Cognitive Map Architecture:
Facilitation of Human-Robot Interaction in Humanoid Robots.
IEEE Robotics & Automation Magazine, March, 16(1):55-66.
Jonsdottir, G. R.

Matlab, Python

separate structure

Distributed heterogeneous module-based network, interacting
via blackboards, realtime performance

Murdock, J.W. and Goel, A.K. (2008). Meta- Laird, 2008 (AGI)
Case-Based Reasoning: Self-Improvement
through Self-Understanding. Journal of
Experimental & Theoretical Artificial
Intelligence , 20(1):1-36

Common Lisp

Yes, although we call it short-term memory.
Auditory short-term memory and visuospatial short-term memory are implemented.

4CAPS
AIS
Apex
Atlantis
CogNetCopycat
DUAL
Emotion Machine

Coward LA, Gedeon TO (2009) Implications ofResource Limitations for a Conscious Machine.
Neurocomputing 72:767-788; Coward, L. A.
(2010). The Hippocampal System as the Cortical
Resource Manager: a model connecting
psychology, anatomy and physiology. Advances
in Experimental Medicine and Biology 657, 315 364.

Common Lisp, Javayes

Murdock, J.W. and Goel, A.K. (2001). Meta- Laird, J.E., Rosenbloom, P.S., &Thórisson, K. R. (1996). Communicative Humanoids: A
Case-Based Reasoning: Using Functional Newell, A. (1986). Universal
Computational Model of Psycho-Social Dialogue Skills. Ph.D.
Models to Adapt Case-Based Agents.
Subgoaling and Chunking: The
Thesis, Media Laboratory, Massachusetts Institute ofProceedings of the 4th International
Automatic Generation and Learning Technology.
Conference on Case-Based Reasoningof Goal Hierarchies. Boston: Kluwer.
(ICCBR'01). Vancouver, Canada, July 30 Laird, J.E., Newell, A., &Thórisson, K. R. (1999). A Mind Model for Multimodal
August 2, 2001.
Rosenbloom, P.S., (1987). SOAR:
Communicative Creatures and Humanoids. International
An architecture for general
Journal of Applied Artificial Intelligence, 13(4-5): 449-486.
intelligence. Artificial Intelligence 33:
1-64.

Task-Method-Knowledge (TMK) models
Working memory encoded as aprovide functional models of what agents graph structure; Knowledgeknow and how they operate. They
represented as rules organized as
describe components of a reasoning
operators; semantic memory;
process in terms of intended effects,
episodic memory; menal imagery;incidental effects, and decomposition into reinforcement learning
lower-level components. Tasks include therequirements and intended effects ofsome computation. Methods implement atask and include a state-transition machine
in which transitions are accomplished bysubtasks.

Distributed modules with traditional Frames

Brom,  C.,  Pešková,  K.,  Cassimatis, N. L., Bignoli, P.,
Lukavský, J.: What does your Bugajska, M., Dugas, S.,
actor remember?
Kurup, U.,
Towards characters with a full Murugesan, A., & Bello, P. (inepisodic memory.
press). An Architecture for
Proceedings of 4th
Adaptive
International Conference on Algorithmic Hybrids. IEEE
Virtual Storytelling, LNCS,
Transactions on Systems,
SpringerMan, and Cybernetics.
Part B.

Original version: Common Lisp;
EPIC-X: C++

Yes. Recurrent shunting on-center offYes. Current percepts are folded together with a decayed version of Diagrammatic working
surround network that obeys LTM Invariance recent percepts. This creates a short history of salient percepts that memory added
Principle and Inhibition of Return rehearsal
functions as working memory.
law

Akshay Vashist, Shoshana Loeb(2010), Attention Focusing Modelfor Nexting Based on Learning andReasoning. BICA 2010.
(Submitted).

Cassimatis, N.L., Trafton, J.G., Coward, L. A. (1990). Pattern Thinking. New
Bugajska, M.D., & Schultz,
York: Praeger.
A.C. (2004). Integrating
cognition, perception andaction through mental
simulation in robots. Journalof Robotics andAutonomous Systems 49 (12): 13-23.

Procedural knowledge: Rules.
Semantic knowledge: relationalgraph structure, Episodic memory:
episodes of relational graphstructures

Thórisson, K. R. (1999). A Mind Model for Multimodal
Communicative Creatures and Humanoids. International Journalof Applied Artificial Intelligence, 13(4-5):449-486.
Thórisson, K. R. (2002). Machine Perception of Multimodal
Natural Dialogue. P. McKevitt (Ed.), Language, vision & music .
Amsterdam: John Benjamins.
Thórisson, K. R. (1998). Real-Time Decision Making in Face toFace Communication. Second ACM International Conference
on Autonomous Agents , Minneapolis, Minnesota, May 11-13,
16-23.
Thórisson, K. R. (1997). Layered Modular Action Control for
Communicative Humanoids. Computer Animation '97, Geneva,
Switzerland, June 5-6, 134-143.
Thórisson, K. R. (2002). Natural Turn-Taking Needs No Manual:
A Computational Theory and Model, from Perception to Action.
In B. Granström (Ed.), Multimodality in Language and Speech
Systems. Heidelberg: Springer-Verlag.

Samsonovich, A. V., Ascoli, G.
George D, Hawkins J (2009) Towards aA., De Jong, K. A., and Coletti, M. Mathematical Theory of Cortical MicroA. (2006). AAAI Technicalcircuits. PLoS Comput Biol 5(10)Report  WS-­‐06-­‐03,  pp.  129–134.  
Menlo Park, CA: AAAI Press.

Not explicitly defined

Tasks, methods, assertions, traces

Coward LA (2001) The Recommendation
Murdock, J.W. and Goel, A.K. (2003).
Architecture: lessons from the design of large
Localizing Planning with Functional
scale electronic systems for cognitive science.
Process Models. Proceedings of the
Journal of Cognitive Systems Research 2:111-156; Thirteenth International Conference on
Coward LA (2005) A System Architecture
Automated Planning & SchedulingApproach to the Brain: from Neurons to(ICAPS'03). Trento, Italy
Conscious-ness. Nova Science Publishers, New
York. SEE
Ulam, P., Goel, A.K., Jones, J., andhttp://cs.anu.edu.au/~Andrew.Coward/References Murdock, J.W. (2005). Using Model-Based.html
Reflection to Guide Reinforcement
Learning. Proceedings of the IJCAI 2005
Workshop on Reasoning, Representationand Learning in Computer Games.
Edinburgh, UK

Shapiro, S.C., & Bona, J.P. (in press). The
GLAIR cognitive architecture. AAAI
Technical Report FS-09-01. Menlo Park,
CA: AAAI Press

yes

Relational constraints,
A large set of heuristically defined similarity
constraint graphs, first-order circumstances, each of which is a group ofliterals, taxonomies, weight
information conditions that are similar and havematrixes.
tended to occur at the same time in pastexperience. One similarity circumstance does notcorrelate unambiguously with any one cognitive
category, but each similarity circumstance isassociated with a range of recommendation
weights in favour of different behaviours (suchLearning, Reasoning, Imagining,procedural knowledge
Modules using data structres Condition definition and detection (cortex);
Attention Focus, Time awareness, encoded as reactive rules;
and algorithms specialized
Selection of similarity circumstances to be
Expectation generation andepisodic and spatial memory for specific concepts. Achanged in each experience (hippocampus);
matching
encoded within a set of graph- focus of attention for
Selection of sensory and other information to be
based structures
exchaning information
used for current similarity circumstanceamong these modules. A
detection (thalamus); Assignment andfocus manager for guiding
comparison of recommendation weights tothe flow of attention anddetermine current behaviour (basal ganglia);
thus inference. Specialized
Reward management to change recommendation
modules for representing and weights (nucleus accumbens etc.); Management
making inferences aboutof relative priority of different types of behaviour
specific concepts.
(amygdala and hypothalamus); Recording andimplementation of frequently required behaviour
sequences (cerebellum)

Franklin, S., & Ferkin, M. H. (2008). Using
Broad Cognitive Models and Cognitive
Robotics to Apply Computational
Intelligence to Animal Cognition. In T. G.
Smolinski, M. M. Milanova & A.-E. Hassanien(Eds.), Applications of Computational
Intelligence in Biology: Current Trends andOpen Problems (pp. 363-394): SpringerVerlag.

Epstein, S. L. and S. Petrovic. 2010 Learning a Mixture of
Search Heuristics. Metareasoning in Thinking about
thinking, Cox, M. T. and A. Raja, editors. MIT Press.

Working memory?

Facts, Rules, frames (learned/
rules, operatorsdeclared), symbolic representationof raw sensory inputs, expectation
generation and matching

O'Reilly & Munakata (2000)

Franklin, Stan. (2007). A foundational
Rigid Flexibility: The Logic ofarchitecture for artificial general intelligence. Intelligence, Springer, 2006
In Advances in artificial general intelligence: and see
Concepts, architectures and algorithms,
http://sites.google.com/site/narswa
Proceedings of the AGI workshop 2006, ed. ng/home
Ben Goertzel and Pei Wang:36-54.
Amsterdam: IOS Press.

Extensible framework in Java.

Open source, in Java and Prolog

NSF Science of Design program; Self
Adaptive Agents project; Turn-based
strategy games

Support for Common Components

Propositional (Based on
chunks)Support for Common Learning
Algorithms

Cognitive map emerges

No explicit. An implicit iconic memory emerges from the transisionhistory between vision-based features.

yes. Mission-specificprocessors build 2D
maps.

yes

yes. in the form of amotivational subsystem
and a meta-cognitive
subsystem (MCS
determines rewards
based on the MS)

Currently handled via interfacing with external vision
currently depends on theprocessing tools. Tighter interlinkage with a hierarchical particular model
NN vision system is a topic of current research.

A fair number of Leabra models of automatized sequential action have been
Schemas a la Drescher
produced, with a smaller number specifically addressing issues of motor control.
Most of these models explore the shaping of distributed patterns of synaptic
strengths in posterior brain areas in order to produce appropriate action sequencesin novel situations. Some work on motor skill automaticity has been done. A few
models, integrating prefrontal and posterior areas, have focused on the applicationof explicitly provided rules.

the part of memory that is directly Yes (Implicit)
related to
executable operations

rules

Each specialist implement its
own.

yes

Leabra contains the mechanisms necessary to self-organize topographic
representations. These have been used to model map-like encodings in the visualsystem. At this time, it is not clear that these mechanisms have been successfully
applied to spatial representation schemes in the hippocampus.

as part of the memory

graph based and Bayesian

There is a spatial secialist that No. Requirement to conserve resources by using No commitment to a specific theory ofhas this functionality.
any one similarity circumstance (= receptive field) cognitive maps
to support multiple behaviours precludes theexistence of unambigous cognitive maps

yesLeabra embraces a few alternative models of the reward-based learning systems
dependent on the mesolimbic dopamine systems, including a neural
implementation of temporal difference (TD) learning, and, more recently, the PVLV
algorithm. Models have been published involving these mechanisms, as well asinteractions between dopamine, the amygdala, and both lateral and orbital areas ofprefrontal cortex.

Feeling & emotion nodes in perceptual
associative memoryyes (imagery was not
implemented)

In Leabra, iconic memory can result from activation-based attractor dynamics orfrom small, sometimes transient, changes in synaptic strength, including
mechanisms of synaptic depression. Imagery naturally arises from patterns ofbidirectional excitation, allowing for top-down influences on sensory areas. Little
work has been done, however, in evaluating Leabra models of these phenomena
against biological data.

Defined using various processed pixel
matrices

yes: input-output buffer (inpsychology, very short-term
perceptual memory and iconic
memory are synonyms)

Different aspects of perceptual memory can be supported by activation-basedlearning, small changes in synaptic strengths, frontally-mediated working memory
processes, and rapid sparse coding in the hippocampus.

Semantic net with activation passing. Nodes
may have sensory data of various sorts
attached.

experience-based and contextsensitive evaluation

Not directly implemented.
Extension possible.

Distribution of attention is
described by a special attributein instances of schemas.

perceptual structures in PML

Yes. Some receptive field detections areassociated with recommendations to increase or
decrease recently used behaviouralrecommendation weights

We have modeled covert attentional
In Leabra, attention largely follows a "biased competition" approach, with top-down Implemented a la Global workspace theory
mechanisms within HTMs although this is activity modulating a process that involves lateral inhibition. Lateral inhibition is a with global broadcasts recruiting possible
not in currently released product.
core mechanism in Leabra, as is the bidirectional excitation needed for top-down
actions in reponse to the current contents
modulation. Models of spatial attention have been published, including models
and, also, modulating the various forms ofConsciousness can be identified
that use both covert shifts in attention and eye movements in order to improvelearning.
with the content of the mental
object recognition and localization. Published models of the role of prefrontal
state "I-Now".
cortex in cognitive control generally involve an attention-like mechanism that allows
frontally maintained rules to modulate posterior processing. Virtually no work has
been done on "consciousness" in the Leabra framework, though there is some work
currently being done on porting the Mathis and Mozer account of visual awarenessinto Leabra.

Atttntion via expectation
generation and matching.

symbolic

Yes. Both raw video and symbolic
representations of raw visual
inputs.

HTMs are inherently modality independent An advanced Leabra model of visual object recognition has been produced which
although we have applied them to vision
receives photographic images as input.
tasks. We offer a Vision Framework forprogrammers and a Vision Toolkit
requiring no programming skills.

possible but not implemented

not sureboth symbolic andsubsymbolic (tailored for the
purposesov virtual worlds)

There is a spatial secialist that Yes. Maintained on the basis of indirect
has some of thisactivation of cortical columns recently active atfunctionality.
the same time

Relational graph structure

Encoded as graph structures(snapshots of working memory)

Functional Sketchboard, Content Blackboard, Motor Feedback
Blackboard, Frames

Frames, Action Modules, (limited implementation)

Limited body-centric spatial layout of selected objects

Functional descriptions of tasks allow
appraisal-based reward as well as
agents to determine success or failure of user-defined internal/external
those tasks by observing the state of the reward
world. Success or failure can then be used
to reinforce decisions made duringexecution.

No

Spatial relationships are encoded using
the underlying knowledge engine.

No

Explicitly defined

There is a spatial secialist that Yes. Based on indirect activation of corticalhas some of thiscolumns on the basis of recent simultaneousfunctionality.
activity

Perceptual representations at multiple levels (complexity) andtimescales  (see  “visual  input”  and  “auditory  input”  below).  

Attention.

Within-utterance attention span. Situated spatial model of
embodied self (but no semantic representation of self that
could be reasoned over).

Yes. Cortical columns have receptive fields
Implicit in the fact that methods constrain
defined by groups of similar conditions that
how and when subtasks may be executedoften occurred at the same time in past sensory (via a state-machine); each method can beexperiences, and are activated if the receptive
in only one state at a time, corresponding
field occurs in current sensory inputs. Attention to the attended-to portion of a reasoning
is selection of a subset of currently detectedtask (and directly linked to the attended-tocolumns to be allowed to communicate theirknowledge via the tasks requirements anddetections to other cortical areas. The selection is effects).
on the basis of recommendation strengths ofactive cortical columns, interpreted through thethalamus, and is implemented by placing afrequency modulation on the action potential
sequences generated by the selected columns.
Currently implemented by emulation of action
Not implemented, but would be handled
Propositional or relational
potential outputs of populations of simulatedby the underlying knowledge engine
sensory neurons

Yes. Temporally and spatially accurate vector model of upperhuman body, including hands, fingers, one eye. Via bodytracking suit, gloves and eyetracker.

様々な統合認知アーキテクチャ
Natural sound streams. Used to develop
emerging architecture of visual system forauditory streaming and speaker-invariant
speech recognition

Yes

Yes. SAR, LADAR, multispectral IR, nightvision, etc.

BECCA is modality agnostic. It can handle inputs originating fromany sensory modality.

Nono (to be implemented)

Auditory input is currently coded as text
input (segmented either as words, phonemes,or syllables).

Currently the only auditory input we handle is speech, via
an external speech-to-text engine. The architecture
supports it in principle.

SONAR, Laser Range
Finder.

Extends Soar's chunking
for diagrammaticcomponents

Auditory and speech objects,
spatialized auditory information

Reads text from the Internet ;-)Reinforcemenent learning

Yes. Learns parameters for
actuator backlash, braking,steering, and acceleration.

Yes, for productions (linear
discount version)

Yes: CogEM and TELOS models of how
amygdala and basal ganglia interact withorbitofrontal cortex etc

BECCA is an RL algorithm and represents a novel approach to RL.

Bayesian Update

Not implemented, but could be.

Yes, for memory retrieval

No, sort of: Includes some Bayes effects asemergent propertiesNono (could be implemented No.
in specific processors)

Hebbian Learning

Not implemented, but could be

No

Not formally, although the principle of associating co-occurring
signals is used extensively.

no (could be implemented No.
in specific processors)

yes

Yes

Gradient Descent Methods (e.g.,
Backpropagation)

Not implemented, but could beNoYes, sort of: Hebbian learning law isinsufficient. Both Hebbian and anti-Hebbian
properties are needed.
Yes, but not Backpropagation

No. BECCA does not use artificial neural networks.

no (could be implemented No.
in specific processors)

yes

Learning of new representations

Yes. Learns maps and trajectories
in new environments

Production compilation(forms new productions)Yes. Multiple kinds of self-organizationYesno (to be implemented)

no (could be implemented No.
in specific processors)

yes

Visual and auditory information processing

Goal-directed behavior

Global Workspace Theory. Learning (e.g. implicit learning, verbal
Multimodal sensory
learning); acquisition of first language
binding.
(syntax, vocabulary); expertise; memory; someproblem solving; concept formation

Yes. Chunks and templates (schemata) are
new chunks, new rules,
automatically and autonomously created as a new NN representations
function of the interaction of the input andthe previous state of knowledge.

Common General Paradigms
ModeledMain general paradigms

semantic meaning can be encoded insparse distributed representations

Yes. FORRSooth is an extended version that conducts
human-computer dialogues in real time

Has been done using off-the-shelf speech textual commands
recognition

We have worked with customers applying
HTMs to auditory tasks.

Accepts data from external databases

agents have used speech and navigation

HTMs have been applied to vision,
All senses are special, are they not?
audition, network sensors, power systems,
and other tasks.

While a few exploratory Leabra models have taken low-level acoustic features as
input, this modality has not yet been extensively explored.

Leabra embraces a few alternative models of the reward-based learning systems
dependent on the mesolimbic dopamine systems, including a neural
implementation of temporal difference (TD) learning, and, more recently, the PVLV
algorithm. Models have been published involving these mechanisms, as well asinteractions between dopamine, the amygdala, and both lateral and orbital areas ofprefrontal cortex.

possible but not implemented

No.

no built-in modalities, but allow
plug-in sensors
and actuators

Yes, for perception, episodic and procedural
memories via base-level activation

Yes.

Strengthening/weakening of
plans

No

Yes

yes (a version of it)

Yes

Retrieval of beliefs and access tocurrent intentions

No

Yes

no

HTM hierarchies can be understood in abelief propagation/Bayesian framework.

While Leabra does not include a mechanism for updating knowledge in a BayesNo
optimal fashion based on singular experiences, it's error-driven learning mechanism
does approximate maximum a posteriori outputs given sufficient iterated learning.

Yes.

No

Yes, with respect to groupings of Tier-3 Advisors.

yes

Yes

An associational learning rule, similar to traditional Hebbian learning, is one of thecore learning mechanisms in Leabra.

No.

We don't use this sort of algorithm explicitly, no

No

No

no

Yes, although this has been tested only in simple cases.

No

Yes, can learn new Advisors

yes

No

All "active" representations in Leabra are, at their core, patterns of neural firing
New representations (instrutionalist lerning)rates. These vectors of activity may be interpreted -- encoded and decoded -- infor perception, episodic and procedural
different ways, however. By analogy, all digital computer representations are strings memories
of bits, but they may be interpreted as structures and pointers and the like. The way
in which vectors of activity at "hidden" layers of neural units are interpreted isalmost always a matter of learning in Leabra models. In this way, internal
representations are always learned from experience.

at BICA 2010

Control of virtual-world agents. Natural language
BDI
processing. We are now starting to work with humanoid
robots but that's early-stage.

Disciple agents have been developed for a wide
Human performance, multiConstraint solving; game playing; robot navigation;variety of domains, including manufacturing [74], tasking, PRP Procedure, air traffic spoken dialogue
education [75], course of action critiquing [76],control
center of gravity determination [77,78], andintelligence analysis [79]. The most recent Disciple
agents incorporate a significant amount of genericknowledge from the Science of Evidence, allowing
them to teach and help their users in discoveringand evaluating evidence and hypotheses, throughthe development of Wigmorean probabilistic
inference networks that link evidence to hypotheses
in argumentation structures that establish therelevance, believability and inferential force ofevidence [80]. Disciple agents are used in courses
at various institutions, including US Army War
College, Joint Forces Staff College, Air War College,
and George Mason University [numbers refer to theabove citations].

reNot decided.

A biologically plausible error-correction learning mechanism, similar in performance No
to the generalized delta rule but dependent upon bidirectional excitation tocommunicate error information, is one of the core learning mechanisms in Leabra.

Yes

none

Currently implemented by emulation of action
potential outputs of populations of simulated
sensory neurons

Not implemented, but would be handled
by the underlying knowledge engine

Possible to extend.

Capable of representing newly
learned knowledge.

the episodic memory features
hebbian learning

Support for text-based
communication

Yes. Speech recognition (BBN Hark), custom real-time prosody
tracker with H* and L* detection.

No

Multimodal integration and realtime multimodal communicative
act interpretation

Yes. Increases in recommendation weights
Yes. Learns criteria for selecting
Yes, for operators (SARSA/Qassociated in the basal ganglia with corticalalternative methods for accomplisinglearning)column receptive field detections, on the basis of tasks, and also alternative transitions
rewards.
within a methods state-transition machine.

Yes, in a recent implementation (RadioShowHost)No

Not implemented, but could be.

NoNo

Yes, but with an overlay management that
determines whether Hebbian learning will occur
at any point in timeNo

Not implemented, but could beNoNo

Not implemented, but could beNo

No

Yes. A new representation is a new subset ofreceptive field detections, with some slightchanges to some receptive fields

Yes. Learns refinements of methods forexisting tasks and can respond to a
specification of some new task by
adapting the methods for some similartask.

Chunking (forms new rules); alsomechanisms to create new
episodes, new semantic memories

Nohttp://act-r.psy.cmu.edu/

A C T -R
daptive

ontrol ofhoughtationalJohn R Anderson

ACT-Rによる統合
http://act-r.psy.cmu.edu/actrnews/index.php?id=38

ACT-Rによる心の統合
ゴール

イマジナル

聴覚

外

発声

的

手続き知識
視覚

宣言的記憶

• 細分化された認知心理学への不満から、統合を目指す

運動

環
境

• モジュールのバッファを条件としプロダクションが発火

バッファ
→実行

バッファ

対応づけ→選択

発声

バッファ

システム

聴覚

視覚

バッファ

宣言的記憶

バッファ

イマジナル

プロダクション

バッファ

ゴール

バッファ

ACT-Rによる心の統合

運動

外
的
環
境

ACT-Rによる学習の統合• シンボリック学習• 宣言的知識の取得（事例ベース学習）• 新たなプロダクションの生成（コンパイル）• サブシンボリック学習• 宣言的知識の活性値を更新（記憶の活性化と減衰）• プロダクションの効用値の更新（強化学習）

問い
プロダクションシステムは前時代的なアー
キテクチャでは？

心の科学の最新の問題にアップデー
トできているのか？

心・脳・社会安西 (2011)

現代における最大の課題の1つ

心と脳と社会を総合的に理解すること

ACT-R・脳・社会
安西 (2011)

現代における最大の課題の1つ

心と脳と社会を総合的に理解すること

ACT-Rによって内と外を結ぶ

認知のACT-R

モジュールと脳部位の対応

2004年の論文での対応

目標モジュール�

大脳基底核
頭頂上葉
後頭葉

目標バッファ�
プロダクション
システム�

前頭連合野

宣言的モジュール�
検索バッファ�
対応付け�
選択�
実行�

視覚・聴覚バッファ�

知覚モジュール�

運動バッファ�

運動モジュール�

環境�

海馬
外側前頭前野
線条体
淡蒼球
視床下部
運動野
小脳

内へのアプローチ• 大脳基底核のニューラルネットモデルとACT-Rの対応づけ• ニューラルネッ

トでのACT-Rの実
装Stocco, A., Lebiere, C., O’Reilly, R., & Anderson, J. R. (2010). The role of the basal ganglia–
anterior prefrontal circuit as a biological instruction interpreter. In Biologically Inspired
Cognitive Architectures 2010 (pp. 153–162).

内へのアプローチ• 生理学における人体のモデルと
ACT-Rを結合• 学習や意思決定のパラメータを調整

Dancy, C. L., Ritter, F., & Berry, K. (2012.). Towards
adding a physiological substrate to ACT-R. In
Proceedings of BRIMS2012.

外へのアプローチ• ドライビングをするモデル• 携帯電話の操作とのマルチタスク状況を設定
Salvucci, D., & Taatgen, N. A. (2010). The
multitasking Mind. New York: Oxford University
Press.

• 軌道の乱れなどを予測

外へのアプローチ• ACT-Rエージェン
トから構成される社会• コミュニケーショ

ンを介して各々のゴールを達成Reitter, D., & Lebiere, C. (2011b). Towards cognitive models ofcommunication and group intelligence. In Proceedings of the 33rd Annual
Conference of the Cognitive Science Society (pp. 734-739).

外へのアプローチ

JCSS2011, HBESJ2011
コミュニケーションの成立に関与する認知基盤のモデル

Modeling Decision Making on the Use of Automation
Junya Morita ( JAIST, Japan), Kazuhisa Miwa, Akihiro Maehigashi, Hitoshi Terai (Nagoya University, Japan),Kazuaki Kojima (Waseda University, Japan), Frank E. Ritter (Penn State, USA)

コミュニケーションを成り立たせる認知的なアーキテクチャはどのようなものだろう？

Result: Successful simulates human data on using automation with the ACT-R utility update mechanism
1. Background• Machines will not completely substitute for human cognition even if it is highly developed• Monitoring and decision making about automation using are always needed

the ACT-R 6 architecture• Over-trust: A tendency to trust too much in automation

model to interact with a simulated task

• Distrust: A tendency to trust too little in automation

environment
represented as utilities of productions in

2. Previous study - Extended Decision Field Theory
Extended Decision Field Theory (EDFT model)

on Systems Man and Cybernetics, 36 (5), 943-959.

b1, b0and trust (T)BCA• T and SC dynamically changes in cyclic
computation reflecting Capabilities ofT

Displayz, s, ! (0, "2)#

P (T-SC)

auto (Ca) and manual controls (Cm)
Limitations: Abstract mathematical model

SC• No interaction with a task environment

if INFC=1
BC(n) = BC(n-1) + 1/b1!(C(n-1) -BC(n-1))
Performance (A) if INFC=0
BC(n) = BC(n-1) + 1/b0!(BCini -BC(n-1))
CA
T(n) = (1-s)!T(n-1) + s!BCA(n) + ! (n)SC (n) = (1-s)!SC(n-1) + s!BCM(n) + ! (n)External

Internalz, s, ! (0, "2)

BCM

Displayb1, b0Constrain conflict resolutions
Production

Monitoring
Store utility values

Visual location

ACT-R: 認知心理学の成果を集積する認知モデリングのSimulatedenvironment

プラットフォーム（行動データの正確な予測が可能）

Virtual keyboard• T: Trust on automation
Rely• SC: Self Confidence• BCA: Belief (subjective estimation) of CA• BCM Belief (subjective estimation) of CM• CA: Automation Capability• CM: Manual Capability
Reliance• INFC: A binary variable representing whether the Ca isavailable to the operator• b1: Transparency of the system interface
Intervene • b0: Dependency of the initial belief• s: Decay rate of T and SC• !: Noise (Mean 0, SD "2)• z: Initial value of P (T(0) - SC(0))• #: Threshold of P defining changes of Auto to Manual andvice versa
CM

4.2 Productions
Store the current vehicle position to the G-bufferStore the next goal position to the VL-buffer

and motor buffers

FindGoal
When Auto mode

When Manual mode

modules causes manual

Press left-key

disadvantages

Release left-key

Press right-key
Release right-key

KeepM
ToLeftToRight
LtoS
RtoS

キーを離す

Clear the G-bufferStore the current vehicle
position to the VL-buffer

PressSpace
Press spacebar

VL-buffer: Visual location buffer
G-buffer: Goal buffer: Conflicted productions (resolution with utility values)

手続き化,1/(!
;64>3!

忘却! .;64>30!

,1 /(!

Step 2: 移動

An artificial task for manipulating
Ca/Cm ratio

Environment• Reward triggers

Gaps
betweenrewards(from firing ToAuto/ToManual until finishing key press) andproduction• All rules fired since previous trigger receive rewards
fires• Reward values• Every screen updates except during mode switches• Auto mode: An automation system moves theTimevehicle• Changing modes: Press the space bar• Ca and Cm are success rates of control (keys do• When the vehicle is on the line: 10

not always work)

Motor

Production
Find-V* :5=02<62!

不一致: -1

メッセージ

Step 3: 学習

50点で終了

交換後に移動Find-G

Find-V
Find-G
LtoR

Press rightkey

Find-V

検索バッファ

Decision Cycle

Internal

the threshold in the EDFT modelTransitions of utility values• Run the manual mode model in the simulated task environment (100 runs)

to Manual• Datato Autoto AutoSuccess/
Failureif P > 0 andutility (ToAuto) > utility (KeepM)

utility (KeepA) - utility (ToManual)• Run the auto mode in the actual task environment (65 runs)

P (T-SC)• Participants executed the task only with manual mode (n = 65)

SC = utility (KeepM)

egs=> Confirms the manipulations

Bcm= u (KeepM)• The auto controls are better than the manual controls

if KeepM is selected-1 * (utility (KeepM) - utility (ToAuto))[Auto] r2 = .994, [Manual] r2 = .996

KeepA

Reliance
Select from• KeepM• ToLeft• ToRight• LtoS• RtoSCM

Success/
FailureCourse condition
Perceptual/Motor delay

Timeを中心としたモジュールの結合

6. シミュレーション（課題環境の操作を通して獲得）

宣言的知識を利用した意思決定
プリミティブな知識の利用から事例の利用へ学習によって推移

他者視点の取得を組み込む事例の利用パスに関する知識

Kullback–Leibler情報量の相乗平均

実験: 21ペア、シミュレーション: 30ペア
行動の偏り

得点の平均

Ca
70 60 50 40 30

記号に関する知識不明

左上から右上isa
パス
左上から右上isa
左上から右上isaパス
パス
左上から右上
左上から右上isa
isaパス
パス
移動前=左上移動前=左上移動前=左上移動前=左上移動前=左上
移動先＝右下移動先＝右下移動先＝右下移動先＝右下移動先＝右下

メッセージに利用された記号の割合
環境での並び順を反映

N: 選択肢の数Pi (n) : 選択肢nを選ぶ確率
E: 選択確率の期待値

チャンス
チャンス

Tnm

Auto use ratio
Significance• ACT-R modeling of time critical field (include meta-level decision making)• Predicts performance (Performance and decision interact with each other)

左上 右下

四角
isa
記号
四角
isa
記号
四角
isa
四角
isa記号
記号
四角
空白isa
isa記号
記号

• より複雑な環境での検討が今後必要

Number of Switches

Decrease with Ca / Cm levels

空白 四角• ただし、メッセージの文法構造・脳機能との対応については未検討• Tnm: メッセージ無しのテスト

テスト課題の結果

Performance increases with higher Ca/Cm ratio

自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序（特別なモジュールを追加する必要はない）•トライアルセッション: 60分が経過するか、終了条件に到達するまで実施• Tim: ターンテイク有りのテスト

Performance

プリミティブな決定• 本課題に限れば、既存の認知アーキテクチャによるモデルの構成は可能• セッションの構成

Cm

30 40 50 60 70
40 s 40 s 40 s 40 s 40 s
• Variables
40 s 40 s 40 s 40 s 40 s
40 s 40 s 40 s 40 s 40 s
• 5 levels of Ca (30-70) ×
40 s 40 s 40 s 40 s 40 s
5 levels of Cm (30-70)
40 s 40 s 40 s 40 s 40 s
• The model and the participants could chose × 50 runs in random order [model]
the auto or manual mode during the task
× 23 participants in random order [data]

プリミティブな知識の利用• シンボリック学習• 事例の蓄積• プロダクションコンパイル• サブシンボリック学習• 宣言的知識の活性値変化• 強化学習による効用値の変化• Tsm: ターンテイク無しのテスト

6. Simulation of the use of automation• To simulate the choice of the auto mode

ゴールモジュール

ACT-Rの特徴: プロダクションシステム

if P < 0 andutility (ToManual) > utility (KeepA)

P (Trust - Self confidence)• Performance increases with higher Ca / Cm levels

ゴールバッファ

成功したラウンドで状態を記憶CAegs
T = utility (KeepA)

自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
四角 空白 空白 四角 左上 右上 右下 右上 先手
四角 空白 空白 四角 左上 右上 右下 右上 先手
四角 空白 空白 四角 左上 右上 右下 右上 先手

四角 空白 空白 四角 左上 右上 右下 右上 後手

プロダクション間の競合解消

Course condition
Bca = u (KeepA)

to Manual

宣言的モジュール

External• The utility values of mode switching correspond

運動モジュール
運動バッファ

プロダクション

Find-V
Find-G• ToManual, ToAuto: 3• Run the auto mode in the simulated task environment (100 runs)• Aim

視覚バッファ

• KeepM, ToLeft, ToRight, LtoS, RtoS, KeepA: 10

5. Baseline Simulation=> Confirms human factors

視覚モジュール

Find-V

移動先・メッセージの決定

順序

四角 空白 空白 四角 左上 右上 右下 右上 不明

他者事例の利用
ゴールバッファFind-G

Press
space bar)0+!

自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序• 推論・意思決定
空白 四角 四角 空白 右下 右上 左上 右上 不明• 宣言的知識の適用
宣言的知識
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序• 効用値（+ ノイズ）を利用した
四角 空白 空白 四角 左上 右上 右下 右上 先手
四角 空白 空白 四角 左上 右上 右下 右上 先手

ントA

Find-G• When the vehicle is off the line: 0• ModelACT-Rの特徴: シンボリックな処理とサブシンボリックな処理の融合

エージェ

To-Auto• Initial utility values

自左 自右 パ左 パ右 自前 自後 パ前 パ後

宣言的知識

エージェントB

Keep-M

:5=02<62!

自己事例の利用

3. コミュニケーションの認知基盤

Find-V#-%0&"!
0+!

ゴールバッファFind-G
Keep-M

9>78>0#0 '!

#-%

○競合解消による選択

メッセージ作成

To-Left

Press Left
key



!!

Adaptive choices
Increase with increase of Ca level / Decrease with increase of Cm level

BRIMS2011, CogSci2011

Tim

Tsm

左シンボル選択

左シンボル選択

移動先の選択

環境での
並び順

事例データ

基礎データ
データ

モデル

終了条件に至ったペアの割合

0.66

1.00

終了条件に到ったラウンド数

48.42 (13.36)

57(16.97)

終了条件に至った時間 (sec)

1,830 (559)

1,238 (438)

ラウンドあたりの所要時間 (sec)

38.76 (11.51)

21.35 (2.02)

終了条件に至るまでの経過
10ラウンド毎の移動平均

right arrow keys to move the vehicle

10
10
10
10
10
10
10
10
10
10
10
10
10
10
10


.0!



（送信後ただちに）

KL情報量の平均• Manual mode: An operator presses the left and4.3 Learning and decision* :5=02<62!

! 0 !

3. ターンテイクの利用

3. Task - Line tracing task

;64>3!

9>78>0;64>3 '!

メッセージ送受信

4• Two modes of control* :5=02<62!

位置が一致:+2

成功の要素



宣言的記憶

（ACT-Rが説明する範囲）

2. シンボルシステムの共有

Relations between # and P



活性化

事例化

•事前に使い方の定まっていない新奇な9>78>0;64>3 '!

! .;64>30!

手続き記憶

強化学習

待機ループ

Step 1:メッセージ交換

記憶

学習



1.共通の行動傾向

Performance (M)• No prediction of human performance

認知
運動

手指運動

認知心理学で検討されてきた認知機能

シンボルコミュニケーションのメディアを利用

KeepA

ToManual

ToAuto

目標設定

コーディネーションゲーム（金野・森田・橋本, 2011）
Store the next goal position to
the G-buffer• The delays in perceptual and motor

リーチング

後手

,1/($ 0 #0 '

プランニング思考

知覚

先手

フローチャート

推論

2. 課題

FindVehicle• 11 productions using the perceptual•人間の認知＝複雑に絡み合った機能の集合•コミュニケーションに特化したモジュールは必要か？

聴覚2007) でどこまで説明できるか？

Press keys

意思決定

視覚

コミュニケーションに必要な認知機能

汎用的な認知アーキテクチャ (ACT-R: Anderson,

Motor

the ACT-R architecture

Gao, J., & Lee, J. D. (2006). Extending the decision field theory to model operators'
reliance on automation in supervisory control situations. IEEE Transactions• Reliance of automation is determined by• Self-conf = UtilityKeep-M• Trust = UtilityKeep-A

Goal

Observe the vehicle andthe line location• Trust and Self confidence are• Need to understand the process of decision making about automation using

4. モデル

モノの知覚
場所の知覚

Includes:• Current vehicle location• Current direction to move
• Current mode (auto/manual)• Previously used rule

• Implementation of the EDFT model in• Visual and motor modules allow the• Irrationality of automation using (Parasuraman & Riley, 1997)

the relation between self-confidence (SC)

1. 背景と目的

4. Model4.1 Architecture• Ironies of Automation (Bainbridge, 1983)

森田 純哉・金野 武司・橋本 敬
北陸先端科学技術大学院大学知識科学研究科

既存の認知心理学の成果を組み合わせることでどの程度の説明ができるのだろうか？

記号の使用率Goal: Construct a model that makes decisions about using an automation system through interaction with a task environment

ラウンドの遂行に要した時間
コンパイルにより減少したケース

長期にわたるループが発生したケース

Modeling Decision Making on the Use of Automation
Junya Morita ( JAIST, Japan), Kazuhisa Miwa, Akihiro Maehigashi, Hitoshi Terai (Nagoya University, Japan),Kazuaki Kojima (Waseda University, Japan), Frank E. Ritter (Penn State, USA)
Goal: Construct a model that makes decisions about using an automation system through interaction with a task environment
Result: Successful simulates human data on using automation with the ACT-R utility update mechanism
1. Background

4. Model4.1 Architecture• Ironies of Automation (Bainbridge, 1983)• Machines will not completely substitute for human cognition even if it is highly developed• Monitoring and decision making about automation using are always needed

represented as utilities of productions in

the relation between self-confidence (SC)on Systems Man and Cybernetics, 36 (5), 943-959.

BCA

computation reflecting Capabilities ofT

Displayz, s, ! (0, "2)

SC• No interaction with a task environment

Reliancez, s, ! (0, "2)

BCM

Displayb1, b0• T: Trust on automation• SC: Self Confidence• BCA: Belief (subjective estimation) of CA• BCM Belief (subjective estimation) of CM• CA: Automation Capability• CM: Manual Capability• INFC: A binary variable representing whether the Ca is

Rely

#

P (T-SC)

auto (Ca) and manual controls (Cm)
Limitations: Abstract mathematical model

if INFC=1
BC(n) = BC(n-1) + 1/b1!(C(n-1) -BC(n-1))
Performance (A) if INFC=0
BC(n) = BC(n-1) + 1/b0!(BCini -BC(n-1))
CA
T(n) = (1-s)!T(n-1) + s!BCA(n) + ! (n)SC (n) = (1-s)!SC(n-1) + s!BCM(n) + ! (n)External

b1, b0and trust (T)MotorPress keys

Simulatedenvironment
Virtual keyboard

the ACT-R architecture

Gao, J., & Lee, J. D. (2006). Extending the decision field theory to model operators'
reliance on automation in supervisory control situations. IEEE Transactions• T and SC dynamically changes in cyclic

Monitoring
Store utility values

Observe the vehicle andthe line location• Trust and Self confidence are

2. Previous study - Extended Decision Field Theory

Internal

Constrain conflict resolutions

Visual location

environment• Distrust: A tendency to trust too little in automation• Need to understand the process of decision making about automation using• Self-conf = UtilityKeep-M• Trust = UtilityKeep-A

Goal

Production

model to interact with a simulated task

• Over-trust: A tendency to trust too much in automation• Reliance of automation is determined by

the ACT-R 6 architecture• Visual and motor modules allow the• Irrationality of automation using (Parasuraman & Riley, 1997)

Extended Decision Field Theory (EDFT model)

Includes:• Current vehicle location• Current direction to move
• Current mode (auto/manual)• Previously used rule

• Implementation of the EDFT model inavailable to the operator• b1: Transparency of the system interface
Intervene • b0: Dependency of the initial belief• s: Decay rate of T and SC• !: Noise (Mean 0, SD "2)• z: Initial value of P (T(0) - SC(0))• #: Threshold of P defining changes of Auto to Manual andvice versa
CM

4.2 Productions

FindVehicle• 11 productions using the perceptual

Store the current vehicle position to the G-bufferStore the next goal position to the VL-buffer

and motor buffers
modules causes manual

When Auto mode

When Manual mode
Press left-key
Press right-key

disadvantages

FindGoalStore the next goal position to
the G-buffer• The delays in perceptual and motor

Release left-key
Release right-key

KeepM
ToLeftToRight
LtoS
RtoS

KeepA

ToManual

ToAuto
キーを離す

Clear the G-bufferStore the current vehicle
position to the VL-buffer

PressSpace
Press spacebar

VL-buffer: Visual location buffer
G-buffer: Goal buffer: Conflicted productions (resolution with utility values)

Performance (M)• No prediction of human performance

Relations between # and P
4

3. Task - Line tracing task
An artificial task for manipulating
Ca/Cm ratio• Two modes of control• Manual mode: An operator presses the left and4.3 Learning and decision

Environment

10
10
10
10
10
10
10
10
10
10
10
10
10
10
10

• Reward triggers

Gaps
between• Every screen updates except during mode switches
rewards(from firing ToAuto/ToManual until finishing key press) andproduction• All rules fired since previous trigger receive rewards
fires• Reward values

right arrow keys to move the vehicle• Auto mode: An automation system moves theTimevehicle• Changing modes: Press the space bar• Ca and Cm are success rates of control (keys do• When the vehicle is on the line: 10

not always work)

Production
Find-V
Find-G
To-Left

Find-V
Press Left
key

Find-G
Keep-M
Find-V
Find-G
LtoR

Press rightkey

Find-V
Find-G
Keep-M
Find-V
Find-G
To-Auto• When the vehicle is off the line: 0
Press
space bar

• Initial utility values

Find-V
Find-G• KeepM, ToLeft, ToRight, LtoS, RtoS, KeepA: 10

5. Baseline Simulation

Decision Cycle• ToManual, ToAuto: 3• ModelInternal

External• The utility values of mode switching correspond• Run the auto mode in the simulated task environment (100 runs)

the threshold in the EDFT modelTransitions of utility values• Run the manual mode model in the simulated task environment (100 runs)

to Manual• Datato Autoto AutoCourse condition
Success/
FailureBca = u (KeepA)
to Manual

if P > 0 andutility (ToAuto)

KeepA> utility (KeepM)

P (T-SC)• Participants executed the task only with manual mode (n = 65)

P (Trust - Self confidence)
SC = utility (KeepM)

egs=> Confirms the manipulations

Bcm= u (KeepM)
if KeepM is selected-1 * (utility (KeepM) - utility (ToAuto))[Auto] r2 = .994, [Manual] r2 = .996

Reliance
Select from• KeepM• ToLeft• ToRight• LtoS• RtoSCM

if P < 0 andutility (ToManual) > utility (KeepA)• Performance increases with higher Ca / Cm levels• The auto controls are better than the manual controlsCAegs
T = utility (KeepA)

utility (KeepA) - utility (ToManual)• Run the auto mode in the actual task environment (65 runs)=> Confirms human factors

Motor

Success/
FailureCourse condition
Perceptual/Motor delayTime

6. Simulation of the use of automation
• Aim

人工物との
つながり

Cm

Ca
70 60 50 40 30

30 40 50 60 70• To simulate the choice of the auto mode
40 s 40 s 40 s 40 s 40 s
• Variables
40 s 40 s 40 s 40 s 40 s
40 s 40 s 40 s 40 s 40 s
• 5 levels of Ca (30-70) ×
40 s 40 s 40 s 40 s 40 s
5 levels of Cm (30-70)
40 s 40 s 40 s 40 s 40 s
• The model and the participants could chose × 50 runs in random order [model]
the auto or manual mode during the task
× 23 participants in random order [data]

Performance
Performance increases with higher Ca/Cm ratio

Number of Switches

Auto use ratio
Decrease with Ca / Cm levels
Significance• ACT-R modeling of time critical field (include meta-level decision making)• Predicts performance (Performance and decision interact with each other)

Adaptive choices
Increase with increase of Ca level / Decrease with increase of Cm level

Morita, J., Miwa, K., Maehigashi, A., Terai,
H., Kojima, K., & Ritter, F. (2011). Modelingdecision making on the use of automation. In
Proceedings of the 33rd Annual Conferenceof the Cognitive Science Society (pp. 1971–1976).

自動化システム

単調な労働から我々を開放する人工物

過信による事故
不信による非合理路線追従課題
バランス感覚を検討する課題

過信と不信の• マニュアルモードとオートモードの切替が可能• 両モードの性能（移動速度）を独立に操作（30%-70%）
Maehigashi, A., Miwa, K., Terai, H.,
Kojima, A., & Morita, J. (2011).
Selection strategy of effort control:
allocation of function to manual
operator or automation system. In
Proceedings of 33rd annual conferenceof the cognitive science society (pp.
1977-1982).

ACT-Rモデルによる意思決定
オートモード（別のプログラムが操作）と知覚・運動サイクル

マニュアルモード（ACT-R自身が操作）を

FindVehicle
Store the current vehicle position to the G-bufferStore the next goal position to the VL-buffer

FindGoalStore the next goal position to
the G-buffer

When Auto mode

When Manual mode
Press left-key
Press right-key
Release left-key
Release right-key

KeepM
ToLeftToRight
LtoS
RtoS

ToAutoACT-Rが切り替える

ゴール

KeepA

ToManual

強化学習

キーを離す

PressSpace
Press spacebar

Clear the G-bufferStore the current vehicle
position to the VL-buffer

VL-buffer: Visual location buffer
G-buffer: Goal buffer: Conflicted productions (resolution with utility values)

to Manual

プロダクション

utility (KeepA) - utility (ToManual)P (Trust - Self confidence)-1 * (utility (KeepM) - utility (ToAuto))

視覚
自機とライン位置
の取得

運動
キー押し

to Autoto Autoto Manualシミュレーション結果
低マニュアル性能

高

自動化システムへの依存を再現

パフォーマンス
低 オート性能 高

オート性能
70 60 50 40 30

シミュレーション
条件（5 x 5）
マニュアル性能
30 40 50 60 70
40 s 40 s 40 s 40 s 40 s
40 s 40 s 40 s 40 s 40 s
40 s 40 s 40 s 40 s 40 s
40 s 40 s 40 s 40 s 40 s
40 s 40 s 40 s 40 s 40 s

Auto使用率
スイッチ回数示唆
ACT-Rによって自動化システムの利用に関わる意思決定を再現することに成功
数理モデルと具体的モデルを統合
EDFTモデル
自動化システムへの依存は信用と自信のバランスにより決定b1, b0
BCA

T

Displayz, s, ! (0, "2)

Rely

#

P (T-SC)

if INFC=1
BC(n) = BC(n-1) + 1/b1!(C(n-1) -BC(n-1))
if
INFC=0
Performance (A)BC(n) = BC(n-1) + 1/b0!(BCini -BC(n-1))
CA
T(n) = (1-s)!T(n-1) + s!BCA(n) + ! (n)SC (n) = (1-s)!SC(n-1) + s!BCM(n) + ! (n)External

Internal

Reliance• T: Trust on automation• SC: Self Confidence• BCA: Belief (subjective estimation) of CA• BCM Belief (subjective estimation) of CM• CA: Automation Capability• CM: Manual Capability• INFC: A binary variable representing whether the Ca isavailable to the operatorSC• b1: Transparency of the system interface
Intervene • b0: Dependency of the initial belief• s: Decay rate of T and SC• !: Noise (Mean 0, SD "2)• z: Initial value of P (T(0) - SC(0))• #: Threshold of P defining changes of Auto to Manual andz, s, ! (0, "2)

GAO AND LEE: EXTENDING THE DECISION FIELD THEORY TO MODEL OPERATORS’ RELIANCE ON AUTOMATION

BCM
b1, b0Gao, J., & Lee, J. D. (2006). Extending the decision field theory to model
operators' reliance on automation in supervisory control situations. IEEE
Transactions on Systems Man and Cybernetics, 36 (5), 943–959.

DisplayCM947

vice versa

Performance (M)Relations between # and P
4 Fig. 2. Hypothetical trajectory showing how the threshold interacts with the changing level of preference.
control capabilities are the primary factors influencing thethe threshold θ. Specifically, an operator who initially relies

コミュニケーションの成立に関与する認知基盤のモデル

つながり

1. 背景と目的

4. モデル

モノの知覚
場所の知覚

意思決定

視覚

コミュニケーションに必要な認知機能•人間の認知＝複雑に絡み合った機能の集合•コミュニケーションに特化したモジュールは必要か？

汎用的な認知アーキテクチャ (ACT-R: Anderson,

聴覚
リーチング

認知

手続き化

プラットフォーム（行動データの正確な予測が可能）,1/(!
;64>3!

忘却! .;64>30!

活性化

事例化

（ACT-Rが説明する範囲）,1 /(!

* :5=02<62!

;64>3!

9>78>0;64>3 '!

* :5=02<62!





2. 課題

Step 2: 移動

コーディネーションゲーム（金野・森田・橋本, 2011）.0!

! 0 !

メッセージ送受信（送信後ただちに）シンボルコミュニケーションのメディアを利用!!

位置が一致:+2

成功の要素

:5=02<62!

自己事例の利用自左 自右 パ左 パ右 自前 自後 パ前 パ後

宣言的知識

ACT-Rの特徴: シンボリックな処理とサブシンボリックな処理の融合

エージェントB)0+!

移動先・メッセージの決定

順序

四角 空白 空白 四角 左上 右上 右下 右上 不明
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
四角 空白 空白 四角 左上 右上 右下 右上 先手
四角 空白 空白 四角 左上 右上 右下 右上 先手
四角 空白 空白 四角 左上 右上 右下 右上 先手

他者事例の利用
ゴールバッファエージェ

自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序• 推論・意思決定
空白 四角 四角 空白 右下 右上 左上 右上 不明• 宣言的知識の適用
宣言的知識
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序• 効用値（+ ノイズ）を利用した
四角 空白 空白 四角 左上 右上 右下 右上 先手
四角 空白 空白 四角 左上 右上 右下 右上 先手

ントA
運動モジュール

四角 空白 空白 四角 左上 右上 右下 右上 後手

プロダクション間の競合解消

運動バッファ

プロダクション
検索バッファ

#-%0&"!
0+!

ゴールバッファ3. コミュニケーションの認知基盤

宣言的モジュール

9>78>0#0 '!

#-%

○競合解消による選択

メッセージ作成

3. ターンテイクの利用

視覚バッファ

Step 3: 学習

50点で終了

交換後に移動2. シンボルシステムの共有

視覚モジュール* :5=02<62!

不一致: -1メッセージ

1.共通の行動傾向



宣言的記憶

認知心理学で検討されてきた認知機能•事前に使い方の定まっていない新奇な9>78>0;64>3 '!

! .;64>30!

手続き記憶

強化学習

待機ループ

Step 1:メッセージ交換

記憶学習2007) でどこまで説明できるか？
ACT-R: 認知心理学の成果を集積する認知モデリングの

目標設定

運動
手指運動

後手

,1/($ 0 #0 '

プランニング思考

知覚

先手

フローチャート推論

ゴールバッファゴールモジュール

成功したラウンドで状態を記憶

ACT-Rの特徴: プロダクションシステム
を中心としたモジュールの結合

プリミティブな知識の利用• シンボリック学習• 事例の蓄積• プロダクションコンパイル• サブシンボリック学習• 宣言的知識の活性値変化• 強化学習による効用値の変化

6. シミュレーション

プリミティブな決定

自左 自右 パ左 パ右 自前 自後 パ前 パ後 順序
空白 四角

左上 右下

四角
isa
記号
四角
isa
記号
四角
isa
四角
isa記号
記号
四角
空白isa
isa記号
記号
記号に関する知識不明

左上から右上isa
パス
左上から右上isa
左上から右上isaパス
パス
左上から右上
左上から右上isa
isaパス
パス
移動前=左上移動前=左上移動前=左上移動前=左上移動前=左上
移動先＝右下移動先＝右下移動先＝右下移動先＝右下移動先＝右下（課題環境の操作を通して獲得）

宣言的知識を利用した意思決定
プリミティブな知識の利用から事例の利用へ学習によって推移

他者視点の取得を組み込む事例の利用パスに関する知識• 本課題に限れば、既存の認知アーキテクチャによるモデルの構成は可能• セッションの構成（特別なモジュールを追加する必要はない）•トライアルセッション: 60分が経過するか、終了条件に到達するまで実施• ただし、メッセージの文法構造・脳機能との対応については未検討• Tnm: メッセージ無しのテスト• より複雑な環境での検討が今後必要• Tsm: ターンテイク無しのテスト• Tim: ターンテイク有りのテスト

Kullback–Leibler情報量の相乗平均

実験: 21ペア、シミュレーション: 30ペア
行動の偏り

メッセージに利用された記号の割合
環境での並び順を反映

N: 選択肢の数

KL情報量の平均

テスト課題の結果

得点の平均

Pi (n) : 選択肢nを選ぶ確率
E: 選択確率の期待値

チャンス
チャンス

Tnm

Tim

Tsm

左シンボル選択

左シンボル選択

移動先の選択

環境での
並び順

事例データ

基礎データ
データ

モデル

終了条件に至ったペアの割合

0.66

1.00

終了条件に到ったラウンド数

48.42 (13.36)

57(16.97)

終了条件に至った時間 (sec)

1,830 (559)

1,238 (438)

ラウンドあたりの所要時間 (sec)

38.76 (11.51)

21.35 (2.02)

終了条件に至るまでの経過
10ラウンド毎の移動平均

Morita, J., Konno, T., & Hashimoto, T.
(submitted). The Role of imitation in
generating a shared communication
system.

森田 純哉・金野 武司・橋本 敬
北陸先端科学技術大学院大学知識科学研究科

既存の認知心理学の成果を組み合わせることでどの程度の説明ができるのだろうか？

記号の使用率

他者とのコミュニケーションを成り立たせる認知的なアーキテクチャはどのようなものだろう？

ラウンドの遂行に要した時間
コンパイルにより減少したケース

長期にわたるループが発生したケース問題

70000年前
15000年前
5000年前我々の社会に流通するシンボ
3500年前

ルコミュニケーションシステム

どのような認知的メカニズム
を介して成立したのか

仮説:

相互に模倣し合うこと
でシンボルコミュニケーションシステムが共有される

乳幼児の言語獲得における模倣 (Tomasello, 1999)

コーディネーションゲーム
▲●（左上にいるよ）

繰り返す

同じ所に行きたい２人がいる• Galantucci (2005) を参考

シンボルを組
み合わせたメッセージを交換ができる▲■（左下に行きます）
メッセージが通じなければすれ違いコーディネーションゲーム
◆●◆●（左上に行きます）（左上にいるよ）
繰り返す

ラウンドの繰り返しによりシンボルの意味に対する合意が形成＝コミュニケーションシステムの形成• Galantucci (2005) を参考

通じれば会えるコミュニケーション成立の実験• 通常のコミュニケーションを制限された被験者のペアが取り組む
ServerParticipant B’s view
Select symbols

Select symbols

Participant A’s view

Mouse

Mouse

Different sites

実験参加者: 21ペア利用できた図形:×2

終了条件: 同じ位置に移動した場合2点、移動できなかった場合-1点を加算し、50点に至ったら終了

31

コミュニケーション成立のモデル
エージェントの画面が交互に出現

エージェントを0.5秒毎に交代（非同期のインタラクション）

モデルの構成
課題の状態を表すスロット

視覚モジュールや宣言的モジュールにより埋められる

Order
Order
Order

loca%on
symbols
symbols
self partner
partner self
self partner
partnerfrom to from to le2rightle2right
le2rightle2right   1st
nil
nil
成功し
たラウ

ゴール

記憶

座標上のシンボルを取得

視覚

エージェ

プロダク

ンドで
状態を

現状の認識

キャラクターの位置を取得

宣言的

ション

アクション
決定

ントB

運動
マウス移動

Order
Order
Order

loca%on
symbols
loca%on
symbols
self loca%on
partner self symbols
partnerselfpartnerselfpartnerself partner
partnerfromself
to fromto le2rightle2right
prepostprepostle2rightle2right
prepostprepostle2rightle2right    2nd



1st1st

クリック

エージェントA
2体のACT-Rエージェント
が各々の課題環境を操作

事例の利用Order
Order

loca%on
symbols
loca%on
symbols
selfself partner
partner self
self partner
partner
fromfromtotofromfromtoto le2rightle2right
le2rightle2rightnil
   1st

ゴール

事例により空白のスロッ
トを埋める

現在のゴールバッファと

視覚

手続き知識

マッチする事例を検索
宣言的記憶

Order
Order
Order
Order

loca%on
symbols
loca%on
symbols
loca%on
symbols
self
partnerpartnerself loca%on
partner self
selfsymbols
partnerselfpartner
self
self
partnerself partner
partner
from
to
from
to
le2rightle2right
from
to
from
to
le2rightle2right
prepostprepostle2rightle2right
2nd
prepostprepostle2rightle2right







1st

1st1st

エージェ

運動

エージェントA
バッファに事例を呼び出しントB

模倣による利用
Order

loca%on
symbols
self partner self partner
partnerfrom to from to le2rightle2right
le2rightle2right   1stnil

フリップした状態でスロットを埋める視覚ゴール

自分とパートナーを
手続き知識フリップした事例を検索
宣言的記憶
Order
Order
Order
Order

loca%on
symbols
symbols
loca%on
symbols
self loca%on
partner selfpartnerself symbols
partnerselfpartnerselfpartnerself partner
partnerfromself
to fromto le2rightle2right
from
le2rightle2right
prepostprepostle2rightle2right
2nd
prepostprepostle2rightle2right
 
 
 
2nd
1st   1st

エージェ

運動

バッファに事例を呼び出し
エージェントA

ントB

模倣の果たす役割を検討する
シミュレーションを実施

1.0
0.8
ペアの割合

コミュニケーションに成功した

シミュレーション結果
0.6

データ

0.4

模倣ありモデル0.2

模倣なしモデル

0.0
25

45

65
85
ラウンド

105

125

模倣を加えることで、人間と同程度の速度でコミュニケーションシステムが成立

終了条件に至ったペアのみを対象

1.0
0.8
（内積）

ペア内でのメッセージの類似度

シミュレーション結果
0.6

データ

0.4

模倣ありモデル0.2

事例なしモデル

0.0
1

21

41
61
81
ラウンド
模倣によりペア内でメッセージが共有

データは模倣ありモデルに比べ低い類似度（人間にとって模倣は困難？）

101

示唆
• コミュニケーションの起源を探る構成論的ア
プローチ• 脳計測との統合

イメージ

まとめ
ACT-Rの魅力と困難

ACT-Rの魅力• 心の科学の全体を見とおす統合性• 人間の行動と対応付ける具体性• 意識をイージーに表現する分かりやすさ(p retrieve-case=goal>ISA coordinate
self-loc-pre =posi
self-left-symbol nil
self-right-symbol nil
state startrole unknown
step 1==>=goal>
state retrieved+retrieval>
ISA coordinate
self-loc-pre =posi- self-left-symbol nil- self-right-symbol nil- partner-left-symbol nil- partner-right-symbol nil
role leader
step 3)(p fill-slots-from-case(p fill-slots-from-pcase(p imitate-from-case=goal>=goal>=goal>ISA coordinate
ISA coordinate
ISA coordinate
self-loc-pre =posi
self-loc-pre =posi
self-loc-pre =posi
state retrieved
state retrieved-partner
state imitated-partner;role =role1;role =role1
role =role1
step 1step 1step 1=retrieval>=retrieval>=retrieval>
ISA coordinate
ISA coordinate
ISA coordinate
role =role1
role =role1
role =role2;self-loc-pre =posi;self-loc-pre =posi
self-loc-post =posi2
self-loc-post =posi2
self-loc-post =posi2
self-left-symbol =letter1
self-left-symbol =letter1
self-left-symbol =letter1
self-right-symbol =letter2
self-right-symbol =letter2
self-right-symbol =letter2
partner-left-symbol =letter3
partner-left-symbol =letter3
partner-left-symbol =letter3
partner-right-symbol =letter4
partner-right-symbol =letter4
partner-right-symbol =letter4
==>
==>
==>
=goal>(p imitate-leader=goal>=goal>
self-loc-post =posi2=goal>
self-loc-post =posi2
self-loc-post =posi2
self-left-symbol =letter3
ISA coordinate
self-left-symbol =letter1
self-left-symbol =letter1
self-right-symbol =letter4
self-loc-pre =posi
self-right-symbol =letter2
self-right-symbol =letter2
self-left-symbol-cur nil
self-left-symbol nil
self-left-symbol-cur nil
self-left-symbol-cur nil
self-right-symbol-cur nil
self-right-symbol nil
self-right-symbol-cur nil
self-right-symbol-cur nil
partner-left-symbol-predict =letter1
state imitate
partner-left-symbol-predict =letter3
partner-left-symbol-predict =letter3(p no-retrieve-casepartner-right-symbol-predict =letter2- role follower
partner-right-symbol-predict =letter4
partner-right-symbol-predict =letter4=goal>
state start
step 1state start
state start
ISA coordinate
)
==>
)
)
self-loc-pre =posi(p imitate-from-case=goal>
self-left-symbol nil(p imitate-from-pcase=goal>
state imitatedself-right-symbol nil(p retrieve-fail=goal>ISA coordinate+retrieval>state start=goal>ISA coordinate
self-loc-pre =posi
ISA coordinate
role unknown
ISA coordinate
self-loc-pre =posi
state imitated-partner- self-left-symbol nil
step 1state retrieved
state imitatedrole =role1- self-right-symbol nil
==>
step
1
role =role1
step 1- partner-left-symbol nil=goal>?retrieval>
step 1=retrieval>- partner-right-symbol nil
state random-decision
state
error
=retrieval>
ISA coordinate- self-loc-post nil)==>
ISA coordinate
role =role2
role follower=goal>
role =role2
self-loc-post =posi2
partner-loc-pre =posi
stateimitateself-loc-post =posi2
self-left-symbol =letter1
step 3)self-left-symbol =letter1(p imitate-fail2
self-right-symbol =letter2)
self-right-symbol =letter2=goal>
partner-left-symbol =letter3(p imitate-fail0(p imitate-fail0
partner-left-symbol =letter3
ISA coordinate
partner-right-symbol =letter4=goal>=goal>
partner-right-symbol =letter4
self-right-symbol =letter1==>
ISA coordinate
ISA coordinate- self-right-symbol-cur =letter1==>=goal>
self-left-symbol nil
self-left-symbol nil=goal>
state imitatedself-loc-post =posi2
self-right-symbol nil
self-right-symbol nil
self-loc-post =posi2
step 1
self-left-symbol =letter3
state imitatedstate imitatedself-left-symbol =letter3?retrieval>self-right-symbol =letter4
step 1step 1
self-right-symbol =letter4
state error
self-left-symbol-cur nil?retrieval>?retrieval>self-left-symbol-cur nil==>
self-right-symbol-cur nil
state error
state error
self-right-symbol-cur nil=goal>
partner-left-symbol-predict =letter1==>==>
partner-left-symbol-predict =letter1
state start
partner-right-symbol-predict =letter2=goal>=goal>
partner-right-symbol-predict =letter2)
state start
state random-decisionstate random-decisionstate start))))(p find-self=goal>ISA coordinate
self-loc-pre nil
state start
step 1==>=goal>
role unknown
state search+visual-location>isavisual-location
color blue
kind text)

ACT-Rの困難: その1• コーディングの難しさ• モデル＝独立したルールの結合×10

ACT-Rの困難: その2•

不幸にも、この本にまとめた進展は、人間の心に関する知識の蓄積ではなく、ACT-Rの進展とみなされがちである。Herbert Simonは、最後の数年間において、理論のブランドネームを見ることへの不満を述べていた。1988年の講演のアブストラクトに、次のように書いている:•

モデルの研究は、複雑なシステムによって実装される。そのシステムの中で複数のメカニズムが相互作用し、何らかの出力を生み出す。我々はモデルの研究の進展を、 L.T、 GPS、EPAM、Soar、
BACON、ACT-Rのようなプログラムの名前を用いることで語る。
しかし、本来はプログラムに埋め込まれたメカニズムこそ議論すべきである。ブランドネームは、メカニズム間の比較や分析を難しくし、研究グループ間での知識の交換を困難にさせる。物理学
者は、量子力学をハイゼンベルグブランド、シューレディンガー
ブランドなどに分割しなかったではないか。

•

NewellとSimonこそが心理学にはじめてブランドを持ち込んだと考えればこの不満は皮肉なことだ。

Anderson (2007) の最後の章

Unified theories of cognitionから
The unified theory of cognition
へ？