Fluid Concept Architectures An experimentation platform

2/21 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

4/21 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

5/21 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?)

6/21 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 flag blue, white, red circle, trapezoid rectangle

7/21 Common problems in AI Combinatorial explosion Can we use relevance to focus search? avoid brute-force search

8/21 Overview Rationale Common problems in AI Running example Strategies Fluid Concepts High-level Perception Parallel Terraced Scan Architecture Conclusion Benefits & shortcomings An experimentation platform

9/21 Running example Letter recognition

10/21 Overview Rationale Common problems in AI Running example Strategies Fluid Concepts High-level Perception Parallel Terraced Scan Architecture Conclusion Benefits & shortcomings An experimentation platform

11/21 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 below left deep concepts decay slower.4.9

12/21 Strategies High-level Perception (Workspace) Percepts Mapping Abstracting Sparking Focus on: “salient” percepts  Relevant mappings  Active mappings  Low happiness height: short.8 width: wide curvature: slight-left shape: cupped height: tall tip1:NW tip1: east shape: cupped curvature: right Contextually relevant concepts are activated Percepts bound to relevance of these concepts Relevance?

13/21 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?

14/21 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

15/21 Overview Rationale Common problems in AI Running example Strategies Fluid Concepts High-level Perception Parallel Terraced Scan Architecture Conclusion Benefits & shortcomings An experimentation platform

16/21 Architecture Slipnet 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

17/21 Overview Rationale Common problems in AI Running example Strategies Fluid Concepts High-level Perception Parallel Terraced Scan Architecture Conclusion Benefits & shortcomings An experimentation platform

18/21 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

19/21 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

20/21 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

21/21 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