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AGI & Attention
Helgi Páll Helgason
[email protected]
AI researcher, Ph.D. candidate
Center for Analysis and Design
of Intelligent Agents,
Reykjavik University
Intellifest 2012
 Today: Attention
 Friday
November 2nd: Attention
 Tuesday
November 6th: Autonomy
Intellifest 2012
 Importance
of attention for AI
 Example
of inspiration from human
attention (cognitive psychology)
 Attention
beyond the human level (for
meta-cognitive purposes)
 Design
of an attention mechanism
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 To
work on AI research, each researcher
or team needs to have
• A clear and explicit working definition of
intelligence
• A clear and explicit motivation
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 Displaying
human-like behavior?
 Solving
computationally complex problems
(in some unspecified amount of time)?
 Performing
isolated tasks that have
conventionally required humans?
 Adapting
to a complex, dynamic
environment with insufficient knowledge
and resources?
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 Development
and validation of
psychological models?
 Development
models?
and validation of neurological
 Development
and implementation of
practical, flexible and versatile autonomous
system?
 How
accurately do we want to replicate
existing biological mechanisms?
• To what degree are we biologically inspired?
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 Nature’s
way of dealing with complexity
under resource and time constraints
 No
real-world intelligence exists that
does not address the passage of time
head on
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“Intelligence is the capacity of a system
to adapt to its environment while
operating with insufficient
knowledge and resources.”
- Pei Wang
(Rigid flexibility: The Logic of Intelligence. Springer 2006)
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Intelligence is a capability of information
processing systems
Intelligence is adaptation:
The system’s solution of one problem is not
only determined by the problem itself, but
also prior experience
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Insufficient knowledge -> system will
usually not have the best solution at
hand
Insufficient resources -> system can
not consider (process) every
possibility nor store all information
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“If either time or computational
resources are infinite, intelligence is
irrelevant .”
- Dr. Kristinn R. Thórisson
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 In
the domain of intelligent systems, the
management of system resources is
typically called “attention”
 Biological (Human) Attention:
• Selective concentration on one aspect
environment while ignoring others
of the
 Artificial Attention:
• Resource management and control mechanism to
assign limited system resources to processing of
most relevant or important information
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Time constraints
ATTENTION
Abundant information
Limited resources
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Time constraints
INTELLIGENCE
Abundant information
Limited resources
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 “Narrow“
(classical) AI:
• Systems explicitly designed to solve specific,
reasonably well-defined problems
 E.g. Deep Blue, Watson, etc.
 Artificial
General Intelligence (AGI)
• Systems designed to autonomously learn novel
tasks and adapt to changing environments
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 When
tasks and environments are prespecified, we know in advance…
• what information is relevant to system operation
• how frequently the system has to sample
information
• how frequently the system has to act
• the resource requirements of the system
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



Substantial dynamic adaption to task not required
Information filtering can be pre-programmed if
characteristics of relevant information known in
advance
Resource management and processing handtuned for specific tasks and environments
Major reduction in complexity (compared to realworld tasks and environments)
• End up with limited and closed models of the real-world
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 When
tasks and environments are
unknown, we do not know in advance…
• what information is relevant to system operation
• how frequently the system has to sample
information
• how frequently the system has to act
• the resource requirements of the system
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 Must
assume up-front:
• Real world environmental complexity
• All information is potentially important
• Not just limited, but insufficient resources at
all times
• Dynamic tasks, environments and time
constraints
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
“Narrow” AI
• Substantial dynamic adaptation to task not required
• Data filtering can be pre-programmed if characteristics of useful data
known in advance
• Lower than real world task complexity
 Resource management and processing hand-tuned for specific scenarios
→ Attention not required (?)

AGI
• Real world environmental complexity assumed up-front
• Computational resources for the AI assumed to be insufficient at all times
 Complexity calls for data filtering and intelligent resource allocation
• Environments and tasks unknown at implementation time
 Resource management must be adaptive
→ Demands strong focus on resource management and
realtime processing
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

Real world is highly dynamic and complex,
provides abundance of information.
System resources not only limited, but
insufficient in light of amount of available
information.

Range of time constraints (many of which are
dictated by the environment) must be satisfied.

Unexpected
events
requiring
response may occur at any time.
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immediate
AGI SUMMER SCHOOL 2012
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“Everyone knows what attention is. It is the taking
possession by the mind, in clear and vivid form, of one
out of what seem several simultaneously possible objects
or trains of thought. Focalization, concentration, of
consciousness are of its essence. It implies withdrawal
from some things in order to deal effectively with
others, and is a condition which has a real opposite in the
confused, dazed, scatterbrained state which in French is
called distraction, and Zerstreutheit in German.”
- William James, 1890
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
Modern attention research started with the “cocktail
party effect” (Colin Cherry, 1953)

Number of attention models have been proposed,
most belonging to two classes:
• Early selection: Selection of information occurs early in
sensory pipeline based on shallow, primitive processing with
no or limited analysis of meaning
• Late selection: Selection of information based on deep
analysis of meaning, occurs late in sensory pipeline

Many early selection models contradicted by
observed human behavior (e.g. cocktail party
scenario)
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Knudsen EI: Fundamental components of attention.
Annu Rev Neurosci 2007, 30:57-78.
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 Two
types of attention:
• Top-down
 Deliberate, goal-driven, targets information related to
the tasks being performed
• Bottom-up
 Reactive, targets unexpected but potentially
important information
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
Information competes for limited system resources
• Allows processing decisions to be made as late as possible, when resource
availability is known

Early selection may be a problematic paradigm
• Ignoring information without analysis of meaning introduces operational risk



Two simultaneously active functions of attention can allow systems
to perform tasks while remaining reactive to unexpected events.
Top-down attention may be controlled by active goals and
predictions of the system to catch information related to current
tasks.
Bottom-up attention can be controlled by novelty and
unexpectedness of incoming information
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 General
Attention Mechanisms for
Cognitive Architectures
• My Ph.D. project 
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 Design
a general attention mechanism
intended for implementation in AGI
systems (cognitive architectures)
 In
progress: Implementation and evaluation
of resulting attention mechanism in state-ofthe-art cognitive architectures
 While
work is biologically inspired at highlevel, replication of any existing attention
mechanism is not a goal
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
Complete
• Targets all operational information (internal +
external)
• Top-down + Bottom-up

General
• No limiting assumptions about tasks, environments
or modalities

Uniform
• Data from all modalities treated identically (at some
level of processing)

Adaptive
• Learns from experience
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 Modality
neutral
 All
modalities treated identically, at some
level of processing
 Including
proprioception (internal
modalities, self-sensing)
 Architecture-independent
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 Attention
functionality implemented in
handful of AGI systems
• E.g. NARS, LIDA, CLARION
 Limitations:
• Data-filtering only (control issues ignored)
• External information only (internal states
ignored)
• Realtime processing not addressed
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 Amount
of available information constantly
assumed to exceed system processing
capacity
 Limited
system resources must be focused
on most relevant or important information
 Requires
capability to determine degree of
information importance, based on:
• Current operating context
• Time constraints
• Resource availability
Intellifest 2012
 Is
it sufficient to only evaluate relevance for
data?
• What about the relevance of system processes?
 Just another “box” in the sensory
• Or something more pervasive?
pipeline?
 Can
we retrofit existing AGI architectures
with attention?
 Can
we extend attention capabilities in
useful ways for AGI systems?
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 Quantify
current relevance of data
 Data relevance:
• Goal-related
• Novelty / Unexpectedness
 Quantify
current relevance of processes
 Process relevance:
• Operational experience
• Available data
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 Constructivist AI
• “From Constructionist to Constructivist AI”,
Thórisson 2009, BICA proceedings
 Systems manage own growth
• From manually constructed initial state
(bootstrap/seed)
 Methodology
for building flexible AGI
systems capable of autonomous selfreconfiguration at the architecture level
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
Internally, the system and its operation can be
viewed as a dynamic and complex environment
• Similar to external task environment

Meta-cognitive functions responsible for system
growth must also process information selectively
• Resources remain limited

Applying the same attention mechanism to
external and internal environments may produce
AI systems capable of performing tasks and
improving own performance while being subject
to realtime constraints and resources limitations.
Intellifest 2012

Introspection, self-growth, self-improvement

As the sum of internal system activity is a vast stream
of information…

applying attention to the internal environment can
lend significant support for meta-cognitive operation
by…

helping determine important information and
processes for meta-cognitive functions…

in the same way it supports task performance in the
external environment
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 While
design is architectureindependent, some requirements are
necessary
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
Data-driven
• All processing is triggered by the occurrence of data
• Eliminates the need for fixed control loops, allowing for
operation at multiple time scales and greater flexibility

Fine-grained
• Data and processing units are small but numerous
• Reasoning about small, simple components and their
effects is significantly more tractable than for larger, more
complex components
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
Predictive capabilities
• Capacity to generate predictions and expectations
• Necessary control data for top-down attention in addition
to goals

Unified sensory pipeline
• Data given identical treatment regardless of origin
(external, internal)
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Unified sensory pipeline: External (environmental) and
internal data handled identically at architecturelevel
Environment
(Real world)
Sampled data
Sensory
devices
Data items
New data
Processes
Data-driven: Processes are activated only when paired with compatible data
Fine-grained: Data and process objects are small and numerous
Actuation
devices
Commands
Goals / Predictions
Derived
Attentional
patterns
Predictive capabilities: Predictions are necessary
control information for top-down attention
Environment
(Real world)
Matching
Sampled data
Top-down
Sensory
devices
Data
biasing
Data items
Processes
Data and processess have priority values that are assigned by biasing.
Actuation
devices
Commands
Goals / Predictions
Environment
(Real world)
Derived
Attentional
patterns
Matching
Sampled data
Top-down
Sensory
devices
Data
biasing
Bottom-up
attentional
processess
Bottom-up
Data items
Evaluation
Processes
Actuation
devices
Commands
Goals / Predictions
Environment
(Real world)
Derived
Attentional
patterns
Matching
Sampled data
Top-down
Sensory
devices
Data
biasing
Bottom-up
attentional
processess
Bottom-up
Data items
Commands
Evaluation
Data -> Process
mapping
Processes
Process
biasing
Actuation
devices
Goals / Predictions
Environment
(Real world)
Derived
Attentional
patterns
Matching
Sampled data
Top-down
Sensory
devices
Data
biasing
Bottom-up
attentional
processess
Bottom-up
Data items
Commands
Evaluation
Data -> Process
mapping
Contextual process
evaluation
Processes
Contextualized
process
performance
history
Experience-based
process activation
Process
biasing
Actuation
devices

Cognitive architectures
•
NARS

•
LIDA

•
http://goertzel.org/agiri06/%5B4%5D%20StanFranklin.pdf
CLARION


https://sites.google.com/site/narswang/
https://sites.google.com/site/clarioncognitivearchitecture/
Publications:
•
•
•
Cognitive Architectures and Autonomy: A Comparative Review

Kristinn R. Thórisson, Helgi Páll Helgason

http://versita.metapress.com/content/052t1h656614848h/?p=4e1d01ba40e04d5d9f51da3977a8be04&pi=0
Attention Capabilities for AI Systems

Helgi Páll Helgason, Kristinn R. Thórisson

http://www.perseptio.com/publications/Helgason-ICINCO-2012.pdf
On Attention Mechanisms for AGI Architectures: A Design Proposal (to be published)

Helgi Páll Helgason, Kristinn R. Thórisson, Eric Nivel

http://www.perseptio.com/publications/Helgason-AGI-2012.pdf
Intellifest 2012
 Wiki
reading material for attention
 Additional
paper:
• On Attention Mechanisms for AGI Architectures:
A Design Proposal (to be published)
 Helgi Páll Helgason, Kristinn R. Thórisson, Eric Nivel
 http://www.perseptio.com/publications/Helgason-AGI2012.pdf
 Post
questions to Proboard
Intellifest 2012