Human Learning in the Michalski Train Domain Ute Schmid Cognitive - - PowerPoint PPT Presentation

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Nov 17, 2022 242 likes •347 views

Human Learning in the Michalski Train Domain Ute Schmid Cognitive Systems Fakult at Wirtschaftsinformatik und Angewandte Informatik Otto-Friedrich Universit at Bamberg Dagstuhl, AAIP 2017 U. Schmid (Uni BA) HLCTrains AAIP 2017 1 /

SLIDE 1

Human Learning in the Michalski Train Domain

Ute Schmid

Cognitive Systems Fakult¨ at Wirtschaftsinformatik und Angewandte Informatik Otto-Friedrich Universit¨ at Bamberg

Dagstuhl, AAIP 2017

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 1 / 9

SLIDE 2

Human-like Computing

Make (AI) algorithms/programs work similar to human cognitive processing WHY?

◮ because humans (still) are better than AI systems in some domains –

try to understand, how humans do it (’psychonic’)

◮ because HCI moves from either simple information systens or full

automated systems to companion systems (human-computer teams) and therefore, computational and cognitive mechanisms has to be compatible

Gain novel/deeper insights into (high level) human cognitive processes

◮ Empirical studies ◮ Cognitive models

individual, e.g. for tutoring

r average
U. Schmid (Uni BA)

HLC–Trains AAIP 2017 2 / 9

SLIDE 3

Empirical Studies on Human Inductive Learning

Are learned models comprehensible/operational effectiv? Experiments concerning comprehensibility of ILP learned rules with/without predicate invention (family tree domain)

◮ Ute Schmid, Christina Zeller, Tarek Besold, Alireza Tamaddoni-Nezhad, Stephen

Muggleton (2017). How does Predicate Invention affect Human Comprehensibility? Post Proceedings ILP’16, Springer.

֒ → talk by Stephen

◮ Additional experiment: unknown, complex domain (chemistry) ◮ current experiment with non-computer science students, natural

language style rules

Study on effect of giving explanations (the learned rules) on trust and transfer And: learning in the Michalski train domain joint work with Jonas Troles and Johannes Birk (BA Psy), Christina Zeller

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 3 / 9

SLIDE 4

The Michalski Train Domain

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 4 / 9

SLIDE 5

The Michalski Train Domain (2)

riginally introduced by Larson & Michalski to explore human learning

(ct. Bruner, Goodnow & Austin) Competition with different sets of trains in 1994 (Michie, Muggleton) domain involving discrete valued features as well as relations Learning quite complex (recursive) rules Question: How good are humans in learning such complex rules? Based on Bruner et al. findings (1959, A Stuy of Thinking): conjunction of features, known as (easily) learnable, disjunction of features, known as highly difficult for humans Additionally: relation between features (of two cars with fixed ’distance’) and recursive relation

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 5 / 9

SLIDE 6

A Difficult Rule Learned by Metagol

Eastbound: First waggon is closed or somewhere in the rest of the train is a triangle load and first car is also short and the rest of the train is eastbound

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 6 / 9

SLIDE 7

Rules to be Learned in our Experiment

Trains with a strongly reduced set of features

◮ trains with three to seven waggons ◮ only three features: length (long/short), form (rectangle, oval,

trapezoid), load (square, triangle, circle)

Training sets: 5 east and 5 west To be learned rules (in a between subject design)

◮ conjunctive: first waggon load is triangle and third waggon has form

val (n = 10)

◮ disjunctive: first waggon load triangle or third waggon has form

trapezoid (n = 9)

◮ relational: any waggon form is rectangular immediately followed by a

waggon with load triangle (n = 10)

◮ recursive: first waggon form is rectangle and a waggon somewhere

behind has load circle (n = 9)

Test phase: 10 new trains (6 east, 4 west)

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 7 / 9

SLIDE 8

Results

differences between conjunctive/recursive and disjunctive/relational

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 8 / 9

SLIDE 9

Interpretation

The classic result concerning conjunctive and disjunctive rules has been confirmed within the train domain Recursive rule has been learned easily: fixed anchor first waggon and somewhere behine seems to be a natural concept Relational rule is of similar difficulty as disjunction: problem might be less immeadiately identifyable constraints

U. Schmid (Uni BA)

HLC–Trains AAIP 2017 9 / 9