This project is an attempt of a (re)implementation of Phaeaco based on the Phd Dissertation from Harry Foundalis.

Motivation

Adaptive systems should have the capability to build, introspect and reason about the visual composition of a perceived scene. They should also be able to update(revise) this knowledge. It is strictly necessary that the system is able to decide which knowledge is not useful and can be disgarded. This all has to happen in

• a unsupervised fashion
• online while the system is running
• open ended manner all under the assumption of insufficient knowledge and resources (AIKR).

It has to happen

• unsupervised, because a teacher can't be a assumed inside a vehicle(spacecraft, robot, etc) :)
• online, training has to happen while the system is processing information
• open ended, because a real system must be open to new perceptions, knowledge and tasks at any time
• under AIKR because resources, inclusive (computation) time are finite and usually not sufficient for the tasks at hand

The author is not aware of many deep learning (DL) or machine learning(ML) systems which fullfill these requirements even in principle. It was possible since the 50s to train neural networks for the recognition of specific features in a supervised way, but this is not sufficient for systems which have to operate in the real world.

Implementation

Implemented features (mostly based on Phaeaco)

Vision processes

Process	Purpose	Implementationstate	current issues	tested?
A	sampling of points from raw black and white image	99% done	none	not directly because it is working fine
D	proposal of lines from points	working good enough	lines are proposed without bias, should prefer points which are close together	visual test
B	computation of altitude of points	sufficient	none	visual test
C	identifies if a point is a point of the endo- or exoskeleton	sufficient	some samples are not endosceleton, minor issue	visual test
E	finds line intersections	sufficient	none	visual test
F	Sends traces in imagespace for samples which are deeper than a threshold	partial	none?	no
G	curve detection	partial, ~20%?	?	no
H	tries to combine linedetectors	partial, is able to fuse lines	?	no
M	identify M features	partial, ~10%?	?	no
Z	zooms in to resolve small features	partial	?	no
K	detects "K" points, points where the K intersects	NOT IMPLEMENTED - TODO	-	-
(Fi)	detects filling and texture	sufficient?	none	visual test (partial)

Not implemented and not planned to be implemented soon, in order of importance

• Process i - detects dots in the raw image
• Process O - detects closed regions
• Process R - runs codelets from coderack
• Process P - "perception" - interface between Phaeaco’s retinal and cognitive levels
• Process Q - managed "quality"(process for attention mechanism)
• Process S - shirking of bitmap

Graph

Graph matching is just partially implemented and partially tested.

Nodes

Phaeaco stored and revised knowledge of the perceived scene and known structures as graphs. It revised the graph to enrich it with perceived or known knowledge.

Implemented

• AttributeNode
• FeatureNode
• NumeriosityNode
• PlatonicPrimitiveInstanceNode
• PlatonicPrimitiveNode

Codelets

Phaeaco processed modifications of the graph with a codelet based mechanism under AIKR. This was based on research from the FARG group. This implementation uses the same mechanism as described in Foundalis's dissertation.

Implemented

• Angle
• Barycenter
• EndPoint
• LineSegmentLength
• LineSegmentSlope
• SearchAndFuseRoughtlyEqualElements

currently not implemented features based on Phaeaco

• full graph matching
• a lot of graph nodes
• a lot of codelets and the interaction of codelets and the graph