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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. Extending Analogical Generalization with Near-Misses (ALIGN). Intro.

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Extending Analogical Generalization with Near-Misses (ALIGN)

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  1. 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. Extending Analogical Generalization with Near-Misses (ALIGN)

  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. Email 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 4.5 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

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