Relational Learning: from Yesterday to Tomorrow

Relational Learning: from Yesterday to Tomorrow Lorenza Saitta Università del Piemonte Orientale saitta@mfn.unipmn.it

Why did I choose this topic? • This is how I knew Yves’ existence • As a team, we have worked on the topic since its beginning, knowing thus its hopes and deceptions • The topic is fascinating and rich of fundamental issues for understanding learning in both humans and machines Warning: This is NOT an overview of the field, but a collection of personal reflexions

Relation Learning has a peculiar history • Negative point: it is conceptually hard and computationally demanding • Positive point: it is apparently more close to human learning, w.r.t. previous Pattern Recognition approaches (even the syntactic ones) • Michalski’s « Human comprehensibility principle » • Instead of a progression « easy  difficult » in the research topics, we have seen, at the beginning, researchers launching themselves into RL with great enthusiasm, but a scarse sense of the feasible

The power of names • The consequence is a history of déjà vu, of dead ends, and of difficulties periodically re-emerging, that researchers try to solve again and again • Solution = Restart and change the label FOL Learning ILP SRL • It would be unfair to say that at each restart everything is the same. Some novelties are added each time • As restart is an effective technique in search, maybe it will be also effective for RL in the long term

The ambition of the origins • ML started officially in 1980 [Pittsburgh workshop] • RL is much older [~ 1970] • Meltzer Inverting deduction { E1, ¬E1 V E2 } • Morgan « « • Plotkin -subsumption • Winston ARCH -> Near-misses • Vere TOTH -> Counterfactuals • Conceptually interesting and still up-to-date today • Computationally infeasible, but, at the time, this was not an issue. The proposed algorithms were not meant to work: they were ideological and explorative in nature. • It is quite surprising that the first learning approaches (except Samuel’s checker) were actually relational. • RL attracted researchers as « honey attract flies » [as we say in Italy]

Keeping feet on the ground • But learning IS meant to work, even if it is relational • Under Michalski’s influence, between 1980 and 1990 many systems have been designed and implemented both in Europe and in the USA • Kodratoff et al. • Giordana et al. [Learning from a relational datbase, 1988] • Esposito et al. • Morik et al. • But not just learning systems but source of conceptual innovations: • Mitchell’s and De Jong’s EBL • Ganascia & Zucker’s « morions » • Carbonell’s learning in planning • Some concrete results were obtained

Generality • Crucial issue: how to define the generality relation if FOL? • Michalski • Buntine • Niblett • Flach -> « Yves lives in France », « Yves lives in Paris » • Kodratoff • Console & Saitta -> Frege’s theory of concepts • Generality ≠ Information content • The discussion died, but it is a pity, because the field got impoverished: received view -subsumption or covering • Relationships with abduction and with abstraction

Inductive Logic Programming Muggleton’s CIGOL Quinlan’s FOIL ILP • Automatic programming • Strongly logic-oriented. Mostly based on -subsumption. High computational complexity • h(x,y) name of a relation (extension -> intension) • Attributes -> Background knowledge • Needs powerful bias or human help • A theory of logical learning more than a practical approach. • Some success in applications. • Almost separated from mainstream ML. • Extensions towards Reinforcement learning, Clustering, Neural Networks.

Plateau and Phase Transitions • The covering test shows a phase transition in a range of parameters interesting for practical learning approaches • Concept with more than 4 chained variables cannot be learned due to the extremely high computational complexity of the test. • Top-down, hypothesis-and-test-based relational learning cannot go beyond stringent limits on the complexity of examples and hypotheses. • Could top-down, data-driven approaches overcome the limits?

Statistical relational learning ILP Probability SRL • Started by considering relations among examples • Evolved toward probabilitic logic. Probabilistic logics (PL) did not produce anything usable in the past. • By putting together two difficult subjects, is it possible that something simple will come out? By interference ? • Several interesting ideas, but no definite solution. Field at the beginning. • Domingos and Richardson (Markov networks) • Kersting and De Raedt (Bayest neworks) • Poole (Lifted inference) • Koller et al. (Probabilistic nertworks - Database-oriented approach)

P(X), Q(x) -> R(X,Y) P(X), Q(x) -> S(X,Y) Q(X), Q(Y) -> V(X,Y) -> R(X,Y) {a, b}

Future?

”I believe that the world market can be satured by maybe five computers” [Thomas Watson, IBM Chairman, 1943] ”A 640 KB memory should be sufficient for everyone" [Bill Gates, 1981] "Internet will undergo a catastrophic collapse in 1996" [Robert Metcalfe, Ethernet’s inventor]

Guesses • A radically new approach is needed to obtain substantial steps ahead • NO Logic, but get close to (take inspiration from) : • Cognitive Sciences • Human memory and learning • More complex concept representation (Barsalou) • Do the limitations to machine learning also apply to human learning? • Complex Systems • Graphs and networks • Agent Based Modeling • Simulation • Statistical Phisics • Ensemble phenomena • Collective learning (emergent phenomenon) • Abstraction and multi-resolution approaches

Guesses • Investigating the foundations • Phase transitions • Kolmogorov complexity • Chaitin’s theorem - Algorithmic version of the Halt problem • No program of complexity n can generate a number greater than n

Relational Learning: from Yesterday to Tomorrow