1. The Principle of Presence: A Heuristic for Growing Knowledge Structured Neural Networks Laurent Orseau, INSA/IRISA, Rennes, France

2. Neural Networks Efficient at learning single problems Fully connected Convergence in W 3 Lifelong learning: Specific cases can be important More knowledge, more weights Catastrophic forgetting -> Full connectivity not suitable -> Need localilty

3. How can people learn so fast? Focus, attention Raw table storing? Frog and Car and Running woman With generalization

4. What do people memorize? (1) 1 memory: a set of « things » Things are made of other, simpler things Thing=concept Basic concept=perceptual event

5. What do people memorize? (2) Remember only what is present in mind at the time of memorization: What is seen What is heard What is thought Etc.

6. What do people memorize? (3) Not what is not in mind! Too many concepts are known What is present: Few things Probably important What is absent: Many things Probably unrelevant Good but not always true -> heuristic

7. Presence in everyday life Easy to see what is present, harder to tell what is missing Infants lose attention to balls that have just disappeared The zero number invented long after other digits Etc.

8. The principle of presence Memorization = create a new concept upon only active concepts Independant of the number of known concepts Few active concepts -> few variables -> fast generalization

9. Implications A concept can be active or inactive. Activity must reflect importance, be rare ~ event (programming) New concept = conjunction of actives ones Concepts must be re-usable(lifelong): Re-use = create a link from this concept 2 independant concepts = 2 units -> More symbolic than MLP: a neuron can represent too many things

10. Implementation: NN Nonlinearity Graphs properties: local or global connectivity Weights: Smooth on-line generalization Resistant to noise But more symbolic: Inactivity: piecewise continuous activation function Knowledge not too much distributed Concepts not too much overlapping

11. First implementation Inputs: basic events Output: target concept No macro-concept: -> 3-layer Neuron = conjunction, unless explicit (supervised learning), -> DNF Output weights simulate priority

12. Locality in learning Only one neuron modified at a time: Nearest = most activated If target concept not activated when it should: Generalize the nearest connected neuron Add a neuron for that specific case If target active, but not enough or too much: Generalize the most activating neuron

13. Learning: example (0) Must learn AB. Examples: ABC, ABD, ABE, but not AB. A B C D E AB … Target already exists Inputs:

14. Learning: example (1) ABC: A B C D E AB 1 Disjunction 1/3 2/3 Conjunction 1 0 1 1-1/Ns N1 N1 active when A, B and C all active

15. Learning : example (2) ABD: A B C D E AB N1 >1/3 <1/3 1 2/3 N2 1/3 1 2/3 1/3

16. Learning : example (3) ABE: N1 slightly active for AB A B C D E AB N1 >>1/3 <<1/3 1 N2 1/3 1 2/3 >1/3 <1/3

17. Learning : example (4) Final: N1 has generalized, active for AB A B C D E AB N1 1/2 0 1 N2 1/3 1 2/3 Unuseful neuron Deleted by criterion

18. NETtalk task TDNN: 120 neurons, 25.200 cnx, 90% Presence: 753 neurons, 6.024 cnx, 74% Then learns by heart If inputs activity reversed -> catastrophic! Many cognitive tasks heavily biased toward the principle of presence?

19. Advantages w/r NNs As many inputs as wanted, only active ones are used Lifelong learning: Large scale networks Learns specific cases and generalizes, both quickly Can lower weights without wrong prediction -> imitation

20. But… Few data, limiting the number of neurons: not as good as backprop Creates many neurons (but can be deleted) No negative weights

21. Work in progress Negative case, must stay rare Inhibitory links Re-use of concepts Macro-concepts: each concept can become an input