Lecture 2: Paradigms of Cognitive Science - David Vernon

An Introduction to

Artificial Cognitive Systems

David Vernon

Institute for Cognitive Systems

Technical University of Munich

www.vernon.eu

Copyright © 2012 David Vernon

Lecture 2

Paradigms of Cognitive Science


Cognitive Science

Cybernetics

1943-53

Cognitivism

1956-

(AI)

Emergent Systems

1958-

Network Dynamics

Synergetics

Dynamical Systems Theory

Enactive Systems

1970-

[Varela 88]


COGNITION

Self-Organization

Cognitivist

Hybrid

Emergent

Physical Symbol Systems

Newell’s Unified Theories

of Cognition

Computational paradigm

Dynamical

Systems

Connectionist

Systems

Enactive

Systems


Some Background

on

Cybernetics


Cybernetics

• The word cybernetics has its roots in the Greek word κυβερνητης or

kybernetes, meaning steersman

• It was defined in Norbert Wiener’s book Cybernetics, first published

in 1948, as “the science of control and communication” (this was the

sub-title of the book)

• W. Ross Ashby notes in his book An Introduction to Cybernetics,

published in 1956 that cybernetics is essentially “the art of

steersmanship”

– co-ordination, regulation, and control.

• The word governor derives from gubernator, the Latin version of

κυβερνητης


Cybernetics

• The word cybernetics has its roots in the Greek word κυβερνητης or

kybernetes, meaning steersman

• It was defined in Norbert Wiener’s book Cybernetics, first published

in 1948, as “the science of control and communication” (this was the

sub-title of the book)

• W. Ross Ashby notes in his book An Introduction to Cybernetics,

published in 1956 that cybernetics is essentially “the art of

steersmanship”

– co-ordination, regulation, and control.

• The word governor derives from gubernator, the Latin version of

κυβερνητης


Cybernetics

• Closed-loop control

• An action by the system causes some change in its environment

and that change is fed to the system via feedback that enables the

system to change its behaviour

• This "circular causal" relationship is necessary and sufficient for a

cybernetic perspective [Wikipedia]


Cybernetics

• “The essential goal of cybernetics is to understand and define the

functions and processes of systems that have goals and that

participate in circular, causal chains that move from action to

sensing to comparison with desired goal, and again to action.”

[Wikipedia]


Cybernetics

• Recent definition proposed by Louis Kauffman, President of the

American Society for Cybernetics

"Cybernetics is the study of systems and processes that interact with

themselves and produce themselves from themselves."


COGNITION

Self-Organization

Cognitivist

Hybrid

Emergent



of Cognition


Dynamical

Systems

Connectionist

Systems

Enactive

Systems


Different Paradigms of

Cognitive Systems

The Cognitivist Approach

Information Processing


Representationist Approach


‘The world we perceive is isomorphic with our perceptions of it

as a geometric environment’

[Shepard & Hurwitz ‘84]


‘Cognition is a type of computation’

‘People “instantiate” … representations physically as cognitive

codes and that their behaviour is a causal consequence of

operations carried out on these codes’

[Pylyshyn ‘84]


From The Tree of Knowledge – The Biological Roots of Human Understanding

H. R. Maturana and F. J. Varela, 1988


Cognitivist Systems

• Strong representations

– Explicit & symbolic

– Representations denote external objects

– Isomorphic

– Projection

– Implies an absolute and accessible ontology

– That is consistent with human expression …



Cognitivist Systems

• Representations

– Descriptive product of a human designer

– Can be directly accessed & understood by humans

– Human knowledge can be directly injected into an

artificial cognitive system


Cognitivist Systems

• But …

Programmer-dependent representations bias the system

‘blind’ the system (cf. Winograd & Flores)

• … can you anticipate every eventuality in your design?

• The semantic gap … and the Symbol Grounding problem


Cognitivist Systems

• Plugging the gap by …

– Machine learning

– Probabilistic modelling

– Better models

– Better logics

– Better reasoning

– Better everything

– … and still …


Cognitive science

is involved in an escalating retreat

from the inner symbol:

a kind of inner symbol flight

A. Clark, Mindware


Cognitivism and Artificial Intelligence


Cognitivism & Artificial Intelligence

• Physical symbol system approach to AI

• Intelligence

– The degree to which a system approximates a knowledge-level system

[Unified Theories of Cognition, Newell 90]

– A knowledge-level system: can bring all its knowledge to bear on every

problem

• Perfect knowledge -> complete use of knowledge

• Humans aren’t there yet!!

– Principle of maximum rationality [Newell 82]

• ‘If an agent has knowledge that one of its actions will lead to one of its goals,

then the agent will select that action’

– Rational analysis [Anderson 89]

• ‘The cognitive system optimizes the adaptation of the behaviour of the

organism’.



• Physical Symbol Systems

– Symbols are abstract entities that can be instantiated as tokens

– A physical symbol system has [Newell 90]:

• Memory (to contain the symbolic information)

• Symbols (to provide a pattern to match or index other symbols)

• Operations (to manipulate symbols)

• Interpretations (to allow symbols to specify operations)

• Capacities for

– Composability

– Interpretability

– Sufficient memory

– Symbol systems can be instantiated but … behaviour is

independent of the particular form of the instantiation



• Physical Symbol Systems [Newell and Simon 1975]

– The Physical Symbol System Hypothesis

• A physical symbol system has the necessary and sufficient means for

general intelligent action

• Any system that exhibits general intelligence is a physical symbol

system

• A physical symbol system is ‘a machine that produces through time an

evolving collection of symbol structures’




Symbol Systems

comprise

comprise

Symbol Structures /

Expresssions /

Patterns

Produce, destroy, modify

Processes

designate

Objects

designate

Processes

Can affect objects

Can be affected by objects

Can be interpreted: carry out the designated process




– The Heuristic Search Hypothesis

• The solutions to problems are represented as symbol structures

• A physical symbol system exercises its intelligence in problem-solving

by search

– Generating and progressively modifying symbol structures until ti produces

a solution structure

– Effective and efficient search

• ‘The task of intelligence is to avert the ever-present threat of the

exponential explosion of search’



• Physical Symbol Systems

– The Physical Symbol System Hypothesis

A physical symbol system has the necessary and sufficient means

of general intelligence

– What are the implications for humans???

– Natural and artifical intelligence is equivalent (why?)



Newell’s four levels

Social

10 5 –10 7 s; behaviours

Requires a symbolic level

Rational

10 2 –10 4 s; tasks requiring reasoning (find way home)

Cognitive

10 -1 –10 1 s; deliberate acts (reaching),

composed operations (shifting gear)

actions (steering a car into a gate)

Biological

10 -4 –10 -2 s; organelle, neuron, neural circuit

All knowledge is here



• (Cognitive) Architecture: defines the manner in which a cognitive agent

manages the primitive resources at its disposal

• Dictates representations and their deployment

• Dictates properties of cognitive system

– Organization & control strategies (coordination/cooperation;

modular/hierarchical)

– Memory, knowledge, representation (world models, delarative

representations, procedural representations, associate memory, episodic

knowledge, meta-knowledge, represenational structures)

– Types of learning (deliberative vs reflexive; monotonic vs non-monotonic)

– Types of planning

– Behaviour (coherence, saliency, & adequacy: consistency, relevance,

sufficiency)



• Unified Theories of Cognition

– Attempts to explain all the mechanisms of all problems in its

domain

– Now plausible (Newell) cf the Soar project

– Applies to both natural and artificial cognition



• Non-functional Attributes of Cognitive Architectures

– Generality (breadth of tasks)

– Versatility (ibid)

– Rationality (cf. consistency and repeatability)

– Scalability (cf. complexity)

– Reactivity (cf. unpredictability)

– Efficiency (cf. time and space constraints)

– Extendability (cf. reconfigurability)

– Taskability (cf. external direction)

– Psychological validity (cf. human models)





Emergent Approaches


COGNITION

Self-Organization

Cognitivist

Hybrid

Emergent



of Cognition


Dynamical

Systems

Connectionist

Systems

Enactive

Systems


Self-generated AI:

AI by orchestrating the processes that generate it

Luc Steels


Emergent Approaches

• Cognition is the process whereby an autonomous system

becomes viable and effective in its environment

• It does so through a process of self-organization

– System is continually re-constituting itself

– In real-time

– To maintain it operational identity

– Through moderation of mutual system-environment interaction and

co-determination

Maturana & Varela 87


• Co-determination

Emergent Approaches

– Cognitive agent is specified by its environment

– Cognitive process determines what is real or meaningful for the

agent

– The system constructs its reality (world) as a result of its operation

in that world

– Perception provides sensory data to enable effective action, but as

a consequence of the system’s actions

– Cognition and perception are functionally-dependent on the

richness of the action interface [Granlund]


Emergent Approaches

• Cognition is the complement of perception [Sandini]

– Perception deal with the immediate

– Cognition deals with longer time frames

• Primate model of cognitive learning is anticipative skill construction

(not knowledge acquisition)

• The root of intelligence is the ability to guide action and, at the same

time, improve that ability

• Embodied as physical systems capable of physical interaction with the

world


Emergent Approaches

‘Cognitive systems need to acquire information about the external

world through learning or association’

[Granlund’02]



Emergent Approaches

• Self-organization

– “Self-organizing systems are physical and biological systems in which

pattern and structure at the global level arises solely from interactions

among the lower-level components of the system.”

– “The rules specifying interactions among the system’s components are

executed only using local information, without reference to the global

pattern.”

• Emergence

– A process by which a system of interacting elements acquires qualitatively

new pattern and structure that cannot be understood simply as the

superposition of the individual contributions.

Camazine 2006


• Self-organization

Emergent Approaches

– Short-range Activator

• autocatalysis: promote its own productions

• Increase the production of an inhibitor (antagonist)

– Inhibitor

• Diffuses rapidly

– Result:

• Local increase in activation

• Long-range antagonistic inhibition which keeps the self-enhancing

reaction localized

Camazine 2006


Emergent Approaches

Local autocatalysis

Non-local negative feedback

Meinhardt 95


Emergent Approaches

Camazine 2006


Connectionist Systems


Connectionist Systems

• Rely on

– Parallel processing

– Non-symbolic distributed activation patterns in networks

– Not logical rules

• Neural networks are the most common instantiations

– Dynamical systems that capture statistical regularities or

associations


Dynamical Systems


COGNITION

Self-Organization

Cognitivist

Hybrid

Emergent



of Cognition


Dynamical

Systems

Connectionist

Systems

Enactive

Systems


• Dynamical Systems

Dynamical Systems

– A dynamical system is an open dissipative non-linear non-equilibrium

system

– System: large number of interacting components & large number of

degrees of freedom

– Dissipative: diffuse energy – phase space decreased in volume with

time (⇨ preferential sub-spaces)

– Non-equilibrium: unable to maintain structure or function without

external sources of energy, material, information (hence, open)

– Non-linearity: dissipation is not uniform – small number of system’s

degrees of freedom contribute to behaviour

• Order parameters / collective variables

Kelso ‘95: Dynamic Pattern – The Self-Organization of Brain and Behaviour


Dynamical System

q = N(q, p, n)

time derivative

state vector

noise

control parameters

From [Shoner Kelso 88]


Enactive Systems


Enaction

• Orthodoxy (cognitivist)

– World as the system experiences it is independent of the cognitive

system (knower)

• Enactive view

– Known and knower ‘stand in relation to each other as mutual

specification: they arise together’


Enaction

• Five key elements to enactive systems

– Autonomy

– Embodiment

– Emergence

– Experience

– Sense-making


Enaction

• Autonomy

– Self-maintenance

– Homeostasis

– Not controlled by outside agencies

– Stands apart from its environment


Enaction

• Embodiment

– Exists as a physical entity

– Directly interacts with its environment

• Structural coupling

• Mutual perturbation

– Constitutive part of the cognitive process


Enaction

• Emergence

– Cognitive behaviour arises from dynamic interplay between

component parts

– Internal dynamics

• Maintains autonomy

• Condition the system’s experiences through their embodiment in a

specific structure


Enaction

• Experience

– History of interaction with the world

– Interactions don’t control

– Interaction do trigger changes in system state

– Changes are structurally-determined

• Phylogeny



Enaction

• Sense-making

– Knowledge is generated by the system itself

– Captures some regularity or lawfulness in the interactions

– The ‘sense’ is dependent on the way interaction can take place

• Perception & Action

– Modify its own state (CNS) to enhance

• Predictive capacity

• Action capabilities

– Development: generative autonomous self-modification


Development

Progressive ontogenetic acquisition of anticipatory capabilities

– Cognition cannot short-circuit ontogeny

– Necessarily the product of a process of embodied development

– Initially dealing with immediate events

t

– Increasingly acquiring a predictive capability

t

Cognition and perception are functionally-dependent

on the richness of the action interface


Co-determination / Structural Coupling

Autonomony-preserving mutual interaction

Perturbation of the system is only effected by the environment

[Note: this ideogram and similar ones to follow were introduced in Maturana and Varela 1987]


Cognitive system: operationally-closed system with a nervous system

Nervous system facilitates a highly-plastic mapping between

sensor and motor surfaces

Perturbation by both environment and system (of receptors & NS)


Enaction

NERVOUS SYSTEM

(a) Facilitates huge increase in the number of possible

sensor-motor patterns (that result from structural coupling

with the environment)

(b) Creates new dimensions (DoF) of structural coupling

by facilitating association of internal states with the

interactions in which the system is engaged


t

t

Anticipation / Planning / Explanation / Prediction


INTERACTION

A shared activity in which the actions of each agent

Influence the actions of the other agents in the same interaction

Resulting in a mutually-constructed pattern of shared behaviour

[Ogden et al.]


Meaning emerges through shared consensual

experience mediated by interaction

Interaction is a shared activity in which the actions of each agent

influence the actions of the other agents engaged in the same

interaction, resulting in a mutually-constructed patterns of shared

behaviour

Ogden, Dautenhahn, Stribling 2002


Bond of Union

M. C. Escher, 1956

COGNITION & SENSE-MAKING

Cognition is a process whereby the issues that are important

for the continued existence of the cognitive entity

are brought forth: co-determined by the entity as it interacts

with the environment in which it is embedded


THE SPACE OF PERCEPTUAL POSSIBILITIES

Is predicated not on an objective environment,

but on the space of possible actions

that the system can engage in whilst still

maintaining the consistency of its coupling with the environment



Enaction

• Autopoiesis

– Autopoiesis is a special type of self-organization

– Requires self-specification and self-generation

(autopoiesis means literally `self-production')

– An autopoietic system is a special type of homeostatic

system (i.e. self-regulating system)

• regulation applies not to some system parameter

• but to the organization of the system itself.


Enaction

• Autopoiesis

– Thus, its organization is constituted by relations between

components that by their interaction and transformation

continuously regenerate the network of processes that

produce them

– That is, the relations among components -- a network of

processes of production --- produce the components

themselves

– A significant aspect of autopoiesis is that its function is to

`create and maintain the unity that distinguishes it from the

medium in which it exists'.


Enaction

• Autopoiesis

– Self-production

– System emerges as a coherent systemic entity, distinct from

its environment, as a consequence of processes of selforganization

– Three orders of system …


Enaction

• Autopoiesis

– First-order autopoietic systems correspond to cellular

entities that achieve physical identity through structural

coupling with the environment:

Environmental perturbations trigger structural changes that

permit it to continue operating


Enaction

• Autopoiesis

– Second-order systems correspond to meta-cellular entities

that exhibit autopoiesis through operational closure in their

organization

They specify a network of dynamic processes that don’t

leave the network (organizationally closed)

Engage in structural coupling with the environment, this time

with a nervous system ( -> much enlarged space of internal

states & much enlarge space of interaction)

– Cognitive systems (at agent level)


Enaction

• Autopoiesis

– Third-order systems exhibit coupling between second-order

(cognitive) systems

– Common ontogenetic drift between reciprocally-coupled

systems

– Shared epistemology / language

• Reflects but does not abstract the common medium in which

they are coupled

– 3 types of behaviour

• Instinctive behaviour (based on autopoietic self-organization)

• Ontogenic behaviour (based on its development)

• Communication / social behaviour


Enaction

• Autopoiesis

– Linguistic behaviours: intersection of ontogenic and

communication behaviours

– Facilitates creation of a common understanding of a shared

world (in which they are coupled)

– Language is an emergent consequence of the third-order

structural coupling of a socially-cohesive group of cognitive

entities


Enaction

• Autopoiesis

– Consciousness (mind) emerges when language and

linguistic behaviour is applied recursively to the agent itself

– Consciousness is an emergent property of social coupling

– Doesn’t have any independent existence (and you can’t

model it!)


Enaction

• Third-order systems: coupling between second-order

(cognitive) systems

– Recurrent (common) ontogenic drift from reciprocal coupling

– Instinctive behaviour based on second-order organization

(phylogenic evolution)

– Ontogenetic behaviour, development qua learning over its lifetime

– Communicative behaviour: third order structural coupling (social

coupling)

– Linguistic behaviours: establishment of a shared epistemology


The Hierarchy of Enactive Systems

Same approach: operational closure

Emergence of Language:

Explicit deliberative self-reflective reasoning

Rationalization of

interactions & perceptions:

2

Intelligent

(adaptive & anticipatory)

behaviour 1

3

Societies of

Cognitive Entities

Viable Organisms

Emergence of Cognition

Cellular Organization

Physical Instantiation

Structural coupling with

the environment

Multilevel Autopoeitic / Cognitive Systems


Enactive Cognition

MEANING

Emerges through shared consensual experience

mediated by interaction




Enactive Cognition


– Necessarily embodied

– Operating in synchronous real-time

Machine representations reflect

the machine’s epistemology


Enactive Cognition

Need to achieve synthetic cognition without falling back into using

cognitivist preconceptions basing the ‘design’ on representations

derived by external observers (us)


Bickhard’s Self-Maintenant &

Recursive Self-Maintenant Systems


Emergent Models

• “The grounds for cognition are adaptive far-fromequilibrium

autonomy – recursively self-maintenant

autonomy – not symbol processing nor connectionist

input processing.”


Emergent Models

• Autonomy: the property of a system to contribute to

its own persistence

• Different grades of contribution -> different levels of

autonomy


• Self-maintenant Systems

Emergent Models

– Make active contributions to its own persistence

– Do not contribute to the maintenance of the conditions for

persistence


Emergent Models

• Recursive self-maintenant systems

– Do contribute actively to the conditions for persistence

– Can deploy different processes of self-maintenance

depending on environmental conditions

– “they shift their self-maintenant processes so as to maintain

self-maintenance as the environment shifts” !!!


Emergent Models

• Far from equilibrium stability

– Non-thermodynamic equilibrium

– Requires that the system does NOT go to thermodynamic

equilibrium

– Completely dependent on their continued existence on continued

contributions from external factors

– Do require environmental interaction

– Necessarily open processes (which exhibit closed organization)


Emergent Models

• Self-maintenant and recursive self-maintenant systems are both

far-from-equilibrium systems

• Recursive self-maintenant systems do yield the emergence of

representations

– Function emerges in self-maintenant systems

– Representation emerges in a particular type of function

(“indications of potential interactions”) in recursively self-maintenant

systems


Emergent Models

Why development in humanoid form

is important for cognition


Emergent

Systems

Cognition is the process whereby

an autonomous system becomes

viable and effective in its

environment


Emergent

Systems

through moderation of mutual systemenvironment

Self-

Organization

The system is continually

re-constituting itself in real-time

to maintain its operational identity


Emergent

Systems

Self-

Organization

Codetermination

The system and environment are co-determined

through structural coupling and

contingent self-organization

Co-determination:

Agent is specified by its environment and the

cognitive process determines what is real or

meaningful for the agent

Agent constructs its reality as a result of its

operation in that world.


Emergent

Systems

Self-

Organization

Codetermination

Maturana's and Varela's idea of structural coupling of level one

autopoietic systems (MaturanaVarela87)

Kelso's circular causality of action and perception (Kelso95)

Organizational principles inherent in Bickhard's self-maintenant

systems (Bickhard00)


Emergent

Systems

Self-

Organization

Codetermination

This implies a particular phylogeny:

Phylogeny

1. To effect the homeostatic self-organization

2. To effect the structural coupling


Emergent

Systems

Self-

Organization

Codetermination

Phylogeny

Neuroscience

Developmental

Psychology

Recent studies in natural cognition

guide the identification of this phylogeny


Emergent

Systems

Self-

Organization

Codetermination

Development

Phylogeny

Development is the cognitive process of

establishing (discovering) the possible space of

mutually-consistent couplings

Bickhard's concept of recursive self-maintenance (Bickhard00)

Maturana's and Varela's level two and level three autopoietic systems (MaturanaVarela87)


Emergent

Systems

Self-

Organization

Codetermination

Development

Phylogeny

Ontogeny

Development implies ontogenic development:

the cognitive system develops its own

epistemology: its own system-specific history- and

context-dependent knowledge of its world


Emergent

Systems

Self-

Organization

Codetermination

Development

Phylogeny

Ontogeny

Neuroscience

Developmental

Psychology


Neural Nets

Dynamical

Systems

Emergent

Systems

Self-

Organization

Codetermination

Development

Phylogeny

Ontogeny


Emergent

Systems

Self-

Organization

Embodiment

Codetermination

Development

Phylogeny

Ontogeny

Emergent systems also implies embodiment …

But what type of embodiment?

It depends on the needs of the structural coupling


Emergent

Systems

Self-

Organization

Embodiment

Codetermination

Development

Phylogeny

Ontogeny

Structural Coupling

The system-environment perturbations must be rich enough

to drive the ontogenic development but not destructive of the self-organization

No guarantee that the cognitive behaviour will be consistent with human preconceptions


Emergent

Systems

Self-

Organization

Embodiment

Codetermination

Development

Phylogeny

Ontogeny

Humanoid

Meaning emerges through a shared consensual experience,

mediated by interaction (mutually-constructed shared pattern of behaviour)

Therefore, we require a humanoid embodiment to effect mutually-consistent meanings


Otherwise, this is what we’ll get …


Emergent

Systems

Self-

Organization

Embodiment

Codetermination

Development

Phylogeny

Ontogeny

Humanoid


Emergent

Systems

Self-

Organization

Embodiment

Codetermination

Development

Phylogeny

Ontogeny

Humanoid

Morphology is a constitutive component of both co-determination and development

Consequently, a plastic morphology is important: the embodiment shouldn’t be static


Emergent

Systems

Self-

Organization

Embodiment

Codetermination

Development

Phylogeny

Ontogeny

Humanoid

Cognitive

Development

Increasing complexity

in action space

Increasing degree

of Prospection




Hybrid Approaches


Hybrid Models

• Combination of cognitivist & emergent approaches

• Don’t use explicit programmer based knowledge

• Can use symbolic representations (& representational

invariances) but these are populated by the system itself as it

learns

• Still emphasize perception-action coupling

• Often with action-dependent perception



Hybrid Models


RECAP


Differences between Cognitivist & Emergent Paradigms

Differences between Cognitivist

& Emergent Paradigms

1. Computational operation

2. Representational framework

3. Semantic grounding

4. Temporal constraints

5. Inter-agent epistemology

6. Embodiment

7. Perception

8. Action

9. Anticipation

10.Adaptation

11.Motivation

12.Autonomy

13.Cognition

14.Philosophical foundation

[Vernon, Von Hofsten, Fadiga 2010]



What is Cognition?

Cognitivism

Information processing: rule-based

manipulation of symbols

Connectionism

Emergence of global states in a network of

simple components

Dynamical

Systems

A history of activity that brings forth change and

activity

Enactive

Systems

Effective action: history of structural coupling

which enacts (brings forth) a world

Adapted from [Thelen & Smith 94] and [Varela 88]


How Does it Work?

Cognitivism

Through any device that can manipulate

symbols

Connectionism

Through local rules and changes in the

connectivity of the elements

Dynamical

Systems

Through self-organizing processes of

interconnected sensorimotor subnetworks

Enactive

Systems

Through a network of interconnected elements

capable of structural changes undergoing an

uninterrupted history


What does a good cognitive system do?

Cognitivism

Represents the stable truths about the real

world, and solves problems posed to it

Connectionism

Develops emergent properties that yield stable

solutions to tasks

Dynamical

Systems

Becomes an active and adaptive part of an

ongoing and continually changing world

Enactive

Systems

Becomes part of an existing on-going world of

meaning (in ontogeny) or shapes a new one (in

phylogeny)


[From Varela 92]


Which Paradigm is Correct?

• Paradigms are not equally mature

• Dynamical systems

– Arguments are compelling BUT ..

– Not yet clear how to get higher-level cognition

• Cognitivist systems

– More advanced

– Not many achievements in generalization

– More brittle (in principle)

• Enactive (& Dynamical)

– SHOULD be much less brittle (mutual specification through co-development)

– But limited cognition at present

• Hybrid systems

– Best of both worlds?

– But unclear how one can really combine antagonistic philosophies


Which Paradigm is Correct?

• No on one has actually designed a complete cognitive system

• Still lots of disagreement on the right approach to take


Recommended Reading

Vernon, D. “Artificial Cognitive Systems – A Primer for Students”,

Chapter 2.

Vernon, D., Metta. G., and Sandini, G. “A Survey of Artificial

Cognitive Systems: Implications for the Autonomous

Development of Mental Capabilities in Computational Agents”,

IEEE Transactions on Evolutionary Computation, special issue

on Autonomous Mental Development, Vol. 11, No. 2, pp. 151-

180 (2007).

Vernon, D., von Hofsten, C., and Fadiga, L. A Roadmap for Cognitive

Development in Humanoid Robots, Cognitive Systems

Monographs (COSMOS), Springer, ISBN 978-3-642-16903-8

(2010); Chapter 5.


Lecture 2: Paradigms of Cognitive Science - David Vernon

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