9618Paper 3 · Advanced Theory§18.1

18. Artificial Intelligence

Graphs and graph algorithms (Dijkstra, A*), machine learning categories (supervised, unsupervised, reinforcement), deep learning, back-propagation and regression.

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Statometer39RegularNext Paper 380%

Marks a paper5.4 · 7%Rank#8 of 8 · #6 on P3Trend · last 12→Full stats

Everything for this topic — study hub

A2 Level · 9618 · Paper 3

This topic is rows 13–14 of the study route.Open it on your study route

Statometer — what 32 real papers say about this topic and each of its 2 syllabus bullets

Regular · #8 of 8 in A2 Level · recomputed with every new session

Regular#8 of 8 in A2 Level#6 on Paper 3→ Steady

Comes up most sessions for a few marks — know the definitions and one worked example.

Next Paper 3: 80%; 8 in 10 chance it is set
Marks a paper: 5.4 / 75; 7% of Paper 3 · fair share 17%
Appeared in: 26 / 32; Paper 3 sittings 2021–2026
Last set: May/Jun 2026; 9618/33 · Q9 · 8 marks · 3-series streak

Marks in each of the last 12 Paper 3 sittingsOct/Nov 24 → May/Jun 26

What the papers say

Set in 26 of 32 Paper 3 sittings — about 8 papers in 10.
Worth about 5.4 marks a paper (7% of Paper 3, well under its fair share of 17%).
Last set May/Jun 2026 · 9618/33 · Q9 for 8 marks — in the most recent series.
Steady at around 5.3 marks a paper year on year.
Lives on “Explain” and “Describe” — 86% of its questions: full sentences with a reason, not one-word answers.
48% of its questions involve a diagram, table or figure — practise with pen and paper.
Its biggest question so far: 10 marks (Oct/Nov 2021 · 9618/31 · Q9).
Inside the topic, §18.1 Graphs for AI carries the most marks (55%) and §18.1 Machine learning the least (46%).
It is examined mostly as AO1 (Knowledge & understanding, 64%), the rest AO2 (36%) — definitions and descriptions in syllabus words score.
The examiner has commented on 18 of its questions — read “What the examiner said” before you practise.

Command words

Share of questions using the word (a question can use several). What each wants →

Question shapes

≤ 6 mk20
7–9 mk7
10–12 mk2
13–15 mk0
16+ mk0

Average 6.1 marks a question · 48% with a figure or table · 38% with code · biggest 10 marks

Assessment objectives — how it is examined

Every part of every current-syllabus question filed under Cambridge's AO1 / AO2 / AO3 (from its command word and what it asks you to do), so you know whether this topic pays for definitions, for applying, or for judging and building.

AO1 64%

AO2 36%

AO1 Knowledge & understanding
AO2 Apply & analyse
AO3 Design, program & evaluate

Paper 3 as a whole

Paper 3	Syllabus	Measured
AO1 Knowledge & understanding	60%	54%
AO2 Apply & analyse	40%	46%
AO3 Design, program & evaluate	0%	0%

Syllabus = Cambridge's grid; measured = the bank's current-syllabus papers.

Inside the topic — every syllabus bullet, measured

Each part of each question is filed under the bullet it examines; the numbers are per Paper 3 sitting, exactly like the topic's. Open a bullet for its own Statometer.

18.1Graphs for AI#11 of 14 on Paper 3Regular · 4255% next paper2.7 marks16/32 sittings→ May/Jun 2026Regular · 4255%
2.7 · 55% of topic16/32May/Jun 2026latest series · →
Set most sessions for a few marks; know the definition and one example.Syllabus: purpose and structure of a graph; searching a graph with Dijkstra's and A* algorithms
Next Paper 3
55%
6 in 10
Marks a paper
2.7
4% of the paper · 55% of the topic
Asked in
16 / 32
Paper 3 sittings · 19 questions
Last asked
May/Jun 2026
9618/33 · Q9 · 5 marks · 3-series streak
Asked in 16 of 32 Paper 3 sittings — about 5 papers in 10.
About 2.7 marks a paper (4% of Paper 3; 55% of the topic's marks across its 2 bullets).
Last asked May/Jun 2026 · 9618/33 · Q9 (5 marks) — in the most recent series.
Usually “Show” or “Explain”.
Biggest chunk of marks so far: 8 in May/Jun 2026 · 9618/32 · Q9.
It is examined mostly as AO2 (Apply & analyse, 71%), the rest AO1 (29%) — you must apply it to the given data or scenario — work it out, trace it, explain it in context.
Last 12 sittings→ Steady
Assessment objectives
AO1 29%
AO2 71%
AO1 Knowledge & understanding
AO2 Apply & analyse
AO3 Design, program & evaluate
Show58%
Explain53%
Calculate47%
Describe37%
Practise its 19 questions Biggest: 8 marks · May/Jun 2026
18.1Machine learning#8 of 14 on Paper 3Regular · 4870% next paper2.7 marks21/32 sittings→ May/Jun 2026Regular · 4870%
2.7 · 46% of topic21/32May/Jun 2026latest series · →
Set most sessions for a few marks; know the definition and one example.Syllabus: artificial neural networks; deep learning, machine learning and reinforcement learning (supervised and unsupervised categories); back-propagation of errors and regression
Next Paper 3
70%
7 in 10
Marks a paper
2.7
4% of the paper · 46% of the topic
Asked in
21 / 32
Paper 3 sittings · 21 questions
Last asked
May/Jun 2026
9618/33 · Q9 · 3 marks · 3-series streak
Asked in 21 of 32 Paper 3 sittings — about 7 papers in 10.
About 2.7 marks a paper (4% of Paper 3; 46% of the topic's marks across its 2 bullets).
Last asked May/Jun 2026 · 9618/33 · Q9 (3 marks) — in the most recent series.
Usually “Explain” or “Describe”: full sentences with a reason, not one-word answers.
Biggest chunk of marks so far: 6 in Oct/Nov 2025 · 9618/32 · Q9.
It is examined almost entirely as AO1 (Knowledge & understanding, 98%) — definitions and descriptions in syllabus words score.
Last 12 sittings→ Steady
Assessment objectives
AO1 98%
AO1 Knowledge & understanding
AO2 Apply & analyse
AO3 Design, program & evaluate
Explain57%
Describe43%
State29%
Show29%
Practise its 21 questions Biggest: 6 marks · Oct/Nov 2025

Marks a paper, year by year

212223242526

Grey years are the previous syllabus (2021–2021, 3 questions) — history, not counted in the rates.

By exam series

May/Jun15/18 · 6.3 mk
Oct/Nov11/14 · 4.4 mk

Practise 29 real questions Smart Notes Quiz Flashcards Every topic on the Statometer

What you need to know2syllabus §18.1

18.1Graphs for AI — purpose and structure of a graph; searching a graph with Dijkstra's and A* algorithms
18.1Machine learning — artificial neural networks; deep learning, machine learning and reinforcement learning (supervised and unsupervised categories); back-propagation of errors and regression

Video lectures2ZAK's YouTube channel · play here

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Infographics2draw these the way the examiner expects · download as PNG

Graphs & Dijkstra's algorithm

Machine learning & neural networks

Browse all infographics →

Key terms12use these exact words in the exam

graphnodeedgeDijkstraA*heuristicsupervised learningunsupervised learningreinforcement learningneural networkback-propagationregression

Dotted terms are defined in the glossary.

Code help1referenced to the Cambridge pseudocode guide

Dijkstra on a tiny graph (adjacency matrix)

pseudocode §3 2D arrays Run in Playground

CONSTANT N = 4
CONSTANT INF = 9999
DECLARE W : ARRAY[1:4, 1:4] OF INTEGER
DECLARE Dist : ARRAY[1:4] OF INTEGER
DECLARE Visited : ARRAY[1:4] OF BOOLEAN
DECLARE i, j, U, Best : INTEGER
FOR i ← 1 TO N
   FOR j ← 1 TO N
      W[i, j] ← INF
   NEXT j
   Dist[i] ← INF
   Visited[i] ← FALSE
NEXT i
W[1,2] ← 4
W[1,3] ← 1
W[3,2] ← 2
W[2,4] ← 5
W[3,4] ← 8
Dist[1] ← 0
FOR i ← 1 TO N
   Best ← INF
   FOR j ← 1 TO N
      IF NOT Visited[j] AND Dist[j] < Best THEN
         Best ← Dist[j]
         U ← j
      ENDIF
   NEXT j
   Visited[U] ← TRUE
   FOR j ← 1 TO N
      IF W[U, j] < INF AND Dist[U] + W[U, j] < Dist[j] THEN
         Dist[j] ← Dist[U] + W[U, j]
      ENDIF
   NEXT j
NEXT i
FOR i ← 1 TO N
   OUTPUT "Node ", i, " distance ", Dist[i]
NEXT i

Playground examples1runnable program for this topic

Graph as an adjacency matrix — breadth-first search
A 2D BOOLEAN array holds the edges; BFS uses a queue to visit every node reachable from node 1.
A2PseudocodeTheory in code 9618 §18.1
Run

Declarative Lab2Prolog knowledge bases with sample queries

Routes through a network
Is there a path from one station to another? Directed edges plus a recursive route/2. Try a start with no outgoing edge.
A2Recursive rules 9618 §20.1
Run
Exam: classifying animals
Rules chained on rules — the expert-system style question. Ask what a given animal is and which animals are mammals.
A2Exam-style scenarios 9618 §20.1, §18 AI
Run

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