fluid concept architectures an experimentation platform
Post on 17-Jan-2018
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Fluid Concept Architectures
An experimentation platform
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Overview Rationale
Common problems in AI Running example Strategies
Fluid Concepts High-level Perception Parallel Terraced Scan
Architecture Conclusion
Benefits & shortcomings An experimentation platform
3/21
Common problems in AI Bird’s nest problem (Minsky)
•Complex construction•Parts are well designed (to the robot) and available
• Simple construction• Debris on floor not designed to build nests
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Common problems in AI Bird’s nest problem (Minsky)
Likewise: 9x9 chess?
“inability to handle the variation in real life”
Can we represent the fluidity of concepts?• overlapping and associative nature• blurry and shifting boundaries• adaptability to context
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Common problems in AI Frame problem
How to identify effectively which data are relevant in solving a problem (without first solving the problem)?
Is relevant in the solution? find a solution with
No time to try all data!
• Make educated guesses (e.g. heuristics)• Abstract data (how?)
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Common problems in AI Frame problem
Can we let relevance emerge through interplay between problem concepts and specific data?• relevant concepts shapes the abstraction of data • specific data adapts relevance of concepts
French flagblue, white, red
circle, trapezoid
rectangle
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Common problems in AI Combinatorial explosion
Can we use relevance to focus search?• avoid brute-force search
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Overview Rationale
Common problems in AI Running example Strategies
Fluid Concepts High-level Perception Parallel Terraced Scan
Architecture Conclusion
Benefits & shortcomings An experimentation platform
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Running example Letter recognition
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Overview Rationale
Common problems in AI Running example Strategies
Fluid Concepts High-level Perception Parallel Terraced Scan
Architecture Conclusion
Benefits & shortcomings An experimentation platform
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Strategies Fluid Concepts (Slipnet)
Concepts Associations Conceptual distance (resistance) Relations (labels) Relevance (activation) Activation decay Conceptual depth Sparking Activation spreading Label nodes Conceptual shifting
part
of
right
to
right to
below left
.45
.3
.65
.6
deep concepts decay slower
.4
.9
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Strategies High-level Perception (Workspace)
Percepts Mapping Abstracting Sparking Focus on:
“salient” percepts Relevant mappings Active mappings Low happiness
height: short .8width: wide
curvature:slight-left
shape: cupped
height: tall
tip1:NW
tip1: east
shape: cupped
.75
.5
.95
.3.6.75
.9
.8curvature: right
Contextually relevant concepts are activated
Percepts bound torelevance of these concepts
Relevance?
.8 .6.4 .7.9 1 .8 1
.9 .9.5 .4
.8 .4.6.9
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Strategies Parallel Terraced Scan
“A parallel investigation of many possibilities to different levels of depth, quickly throwing out bad ones and homing in accurately and rapidly on good ones.” (D.R.Hofstadter)
Build percepts in phases: Measure promise with a quick test If okay, examine closer If okay, build it
Work in (simulated) parallel How?
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Strategies Parallel Terraced Scan
Codelets (~ ant systems) Each performs tiny part of algorithm:
Scouts, examiners, builders Called with specific urgency:
Scouts are continuously added (low urgency) Follow-up codelets (urgency = promise of percept) Active concepts (high urgency)
Scheduler picks next codelet (stochasticly)
Strongest pressures commingle
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Overview Rationale
Common problems in AI Running example Strategies
Fluid Concepts High-level Perception Parallel Terraced Scan
Architecture Conclusion
Benefits & shortcomings An experimentation platform
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ArchitectureSlipnet
Node activation spreads through conceptual links
Activation is sparked with every mapping
Workspace
Coderack
Highly activated nodes spawn top-down codelets
Codelets call in follow-up codelets
Bottom-up codelets continuously added
Codelets enter workspace
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Overview Rationale
Common problems in AI Running example Strategies
Fluid Concepts High-level Perception Parallel Terraced Scan
Architecture Conclusion
Benefits & shortcomings An experimentation platform
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Benefits Handles concepts fluently
Flexible representations, flexible actions “Searches” through interpretation space
Not trying all possible combinations Does its own representation building
Sensitive to pressures from actual situation Much more independent Can generate original viewpoints
Remains symbolical Representations easily referenced and manipulated
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Shortcomings Hard to set up good domain
Requires thorough study Doesn’t learn (yet) Behavior depends on many parameters
Hard to see how change affects behavior Hard to experiment with
No flexible code base available Start from scratch
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An experimentation platform Component based approach
E.g. replace semantic network User writes objects, dynamically loaded
Codelets, percepts are very dynamic entities Uniform communication between components
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Generalizations Allow multiple workspaces (and schedulers)
Different “parts” in problem Delegate different levels of perception (≠ codelets,…)
Allow different approaches Information in codelets vs. network vs. percepts
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