Many existing AGI architectures are based on the assumption of infinite computational resources, as researchers ignore the fact that real-world tasks have time limits, and managing these is a key part of the role of intelligence. In the domain of intelligent systems the management of system resources is typically called “attention”. Attention mechanisms are necessary because all moderately complex environments are likely to be the source of vastly more information than could be processed in real-time by an intelligence’s available cognitive resources. Even if sufficient resources were available, attention could help make better use of them. We argue that attentional mechanisms are not only nice to have, for AGI architectures they are an absolute necessity. We examine ideas and concepts from cognitive psychology for creating intelligent resource management mechanisms and how these can be applied to engineered systems. We present a design for a general attention mechanism intended for implementation in AGI architectures.

1. Helgi Páll Helgason
helgi@perseptio.com
AGI 2012
Center for Analysis and Design
of Intelligent Agents,
Reykjavik University
Eric Nivel
eric.nivel@gmail.com
Kristinn R. Thórisson
thorisson@gmail.com

2. Why is attention necessary for AGI?
What is constructivist methodology?
How to design an attention mechanism
AGI 2012

3.  In the domain of intelligent systems, the
management of system resources is typically
called “attention”
 Biological (Human) Attention:
• Selective concentration on particular aspects of the
environment while ignoring others
 Artificial Attention:
• Resource management and control mechanism to
assign limited system resources to processing of most
relevant or important information
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4. AGI 2012
Time constraints
Abundant information Limited resources
ATTENTION
(Resource management)

5.  If we have detailed specifications of tasks
and environments at design time, we already
know:
• what kind of information is relevant to system
operation
• how frequently the system has to sample information
• how quickly the system needs to make decisions
• the resource requirements of the system
AGI 2012

6.  Major reduction in complexity (compared to real-world
tasks and environments)
• Information filtering can be pre-programmed if
characteristics of task-relevant information is known
in advance
• Resource management and processing can be hand-
tuned for specific tasks and environments in advance
 Substantial dynamic adaption to tasks not required
AGI 2012

7.  When tasks and environments are partially
specified or unspecified at design time, the
following is unknown:
• what kind of information is relevant to system
operation
• how frequently the system has to sample information
• how quickly the system needs to make decisions
• the resource requirements of the system
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8. AGI 2012
Levelofabstraction
(specification,goals)
Operating environment
Narrow AI
AGI
Learning
AGI systems are not
supplied at design time
with sufficient explicit
initial knowledge to
achieve all goals
Must learn to realize high-
level goals in the
operating environment
Must learn to perceive and
act meaningfully in the
environment
Initial knowledge for lower
levels of abstraction is
incomplete

9.  AGI system design must assume up-front:
• Incomplete knowledge of the world at boot time
• Real world complexity for environments and tasks
• All information is potentially important
• Not only limited, but insufficient resources at all times
• Dynamic tasks, environments and time constraints
AGI 2012

10.  “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
AGI 2012

11. A general attention mechanism for
implementation in AGI systems /
cognitive architectures
 Replication of natural attention mechanisms is not a goal
(but work is biologically inspired at a high level)
AGI 2012

12. AGI 2012
 Constructivist AI
• “From Constructionist to Constructivist AI”, Thórisson 2009, BICA
proceedings
 Targets systems that manage their own
growth
• From manually constructed initial state
(bootstrap/seed)
 Methodology for building flexible AGI systems
capable of autonomous self-reconfiguration at
the architecture level

13.  General
• No limiting assumptions about tasks, environments or modalities
• Architecture-independent
 Adaptive
• Learns from experience
 Complete
• Targets all operational information (internal and external)
• Top-down + Bottom-up
 Uniform
• Data from all modalities treated identically (at cognitive levels of
processing)
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14.  Attention functionality implemented in handful
of AGI systems
 Limitations:
• Data-filtering only (control issues ignored)
• External information only (internal states ignored)
• Realtime processing not addressed
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15. Intellifest 2012
 System-wide quantification of data relevance
 Data relevance:
• Goal-related (top-down)
• Novelty / Unexpectedness (bottom-up)
 System-wide quantification of process relevance
 Process relevance:
• Operational experience (“top-down”)
 Prior success or failure of individual processes to contribute to similar
or identical goals
• Available data (“bottom-up”)
 Available data may limit which processes can be run

16.  Internal system: another dynamic and complex
environment
• Similar to the external task environment
 Meta-cognitive functions responsible for system growth
must also process information selectively
• Resources remain limited
 Applying a single, unified attention mechanism to both
internal and external environments significantly
facilitates the creation of AGI systems capable of
performing tasks and improving own performance
while being subject to resource limitations and realtime
constraints.
AGI 2012

17. Data-driven
Fine-grained
Predictive capabilities
Unified sensory processes
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18. Data item Process
Data relevance quantified in saliency
parameter
Process relevance quantified in activation
parameter
Execution Policy
Execute most active processes on most salient
data items
(data item must match process input specification)
The high-level role of attention is to quantify and assign saliency and activation values

19. Data items
Processes
New data
Sensory
devices
Environment
(Real world)
Actuation
devices
Commands
Sampled data

20. Goals / Predictions
Attentional
patterns
Derived
Matching
Data items
Processes
Data
biasing
Top-down
Sampled data
Environment
(Real world)
Sensory
devices
Actuation
devices
Commands

21. Data items
Processes
Bottom-up
attentional
processess
Top-down
Bottom-up
Sampled data
Derived
Environment
(Real world)
Sensory
devices
Actuation
devices
Goals / Predictions
Attentional
patterns
Data
biasing
Commands
Evaluation
Matching

22. Data items
Processes
Top-down
Bottom-up
Process
biasing
Sampled data
Derived
Environment
(Real world)
Sensory
devices
Actuation
devices
Bottom-up
attentional
processess
Goals / Predictions
Attentional
patterns
Data
biasing
Commands
Data -> Process
mapping
Evaluation
Matching

23. Data items
Processes
Top-down
Bottom-up
Contextualized
process
performance
history
Contextual process
evaluation
Experience-based
process activation
Sampled data
Derived
Data -> Process
mapping
Environment
(Real world)
Sensory
devices
Actuation
devices
Bottom-up
attentional
processess
Evaluation
Goals / Predictions
Attentional
patterns
Matching
Data
biasing
Process
biasing
Commands

24.  Implementation of early version complete
 Evaluation in progress
AGI 2012

25. Intellifest 2012
Work supported by the European Project HUMANOBS – Humanoids that Learn Socio-Comunnicative
Skills Through Observation (grant number 231453).

26. Intellifest 2012

27.  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
AGI 2012
Thanks to:
Dr. Kristinn R. Thórisson
Eric Nivel
Kamilla Jóhannsdóttir