1. Extending Analogical Generalization with Near-Misses (ALIGN)
Authors: S. Friedman (SIFT, USA), M. McLure & K. Forbus (Northwestern Uni., USA)
Presentation of Georgios Samaras, for master course “ADVANCED ARTIFICIAL INTELLIGENCE”, prof. Panagiotis Stamatopoulos.

2. Intro Learning concepts from examples, for cognitive systems
Similarity-based supervised learning, labeled examples
Near-miss by Winston (1970). His system used analogical matching to compare the structured representations
Winston-Limits: domain-specific analog. Matcher, teacher must know internal representation & label near-misses and one representation per concept => no disjunctive concepts
ALIGN built on structure-mapping (based on theory: analogy & similarity)
Pros: 1)learns characteristic+discriminative properties, 2) able to learn disjunctive categories, 3)auto-identifies near-misses

3. Background (1/4)
Structure-Mapping(SM): Analogical comparison = extract commonalities between examples, to form a concept.
Structure-Mapping Engine (SME) is a simulation of SM. Local-to-global process: Build local match hypotheses=>global mappings with similarity scores, candidate inferences (base<=>target) and analogy skolems (entities that do not match)

4. Background (1/4)
MAC/FAC: model of similarity-based retrieval built on SME. Finds 3 match cases from its library with the input and then uses SME to actually compute the similarity and return all 3 or the best.
Sequential Analogical Generalization Engine (SAGE): model of analog. Generalization build on SME. Successor to SEQL(the general idea is the same). Merge example to generalization context if similar enough, or make it a new cluster. Prune low probability expressions.

5. from Forbus

6. Background (2/4)

7. Background (4/4)~GogSketch: Sketch understanding system

8. Grassfire algo

9. Medial Axis Transform: Find the skeleton

10. Shock graphs, derived from skeleton, nodes relate to surrounding area->segment space

11. More on GogSketch

12. ALIGN: Detecting/exploiting Near-Misses with Analogy
Finds 1 Near-Miss from memory, with high similarity, but different label.
Use Near-Misses to learn which are the critical criteria for asserting category membership (inclusion hypothesis), derived from + → - and the sufficient criteria for blocking category membership (exclusion hypothesis), derived from - → +.

13. ALIGN: Revising Hypotheses with Analogical Generalization
Every label has a SAGE context. Update context with new example, prune generalized hypotheses that are not true for context's examples
The bigger the context, the more trustworthy
Multiply SAGE clusters for a context → disjunctive category structure. On new example, use structural similarity to determine which cluster(s) should be tested.

14. ALIGN: Classification via analogy
On new example, fetch from memory similar labeled examples.
Start by most similar: 1) map their correspondences, 2) test and match (watch out for exclusion/inclusion hypotheses).
If no match, repeat with next example (important for distinguishing similar categories)

15. Evalutaion ALIGN is the full system described so far
Prototypes is ALIGN without near-miss analysis
Examples is ALIGN without near-misses and analogical generalization (so, Examples do similarity-based retrieval over the library of training examples)

16. Classification of sketched objects in GogSketch. A case contains 4
Classification of sketched objects in GogSketch. A case contains 4.5 entities+31 facts

17. ALIGN had 62% accuracy

18. Classification in geospatial concepts by using GogSketch on Freeciv map. A case contains 8 entities+60 facts. Prototypes reduce to Examples.

19. Conclusion ALIGN is better from Prototypes and Examples
Sweet spot for Similarity threshold, min creates too many near-misses, max creates small generalizations
ALIGN requires orders of magnitude fewer examples than other existing models