Lecture 9 Flashcards

1
Q

What is decision making?

A

The ability of a character to decide what to do

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2
Q

What is the difference between internal knowledge and external knowledge for a decision maker?

A

Internal: Character’s internal state (health, goals, etc.)

External: Information from the game environment (position of other entities, level layout etc.)

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3
Q

What is the output of a decision maker?

A

An action request

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4
Q

What is the difference between external changes and internal changes?

A

External: movement, animations

Internal: Beliefs, change in goals

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5
Q

What is a decision tree made up of?

A

Nodes

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6
Q

How does a decision tree work?

A

Each node has boolean condition and two children

Start at root node and proceed down tree making choices until a leaf node is encountered

Leaf node contains action

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7
Q

Can the same action be in multiple leaves?

A

Yes

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8
Q

Decisions can be ______________ to reflect AND or OR clauses

A

Put into series

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9
Q

What is the difference between binary and n-ary trees?

A

Binary: two children

N-ary: n children

N-ary marginally more efficient but less easily optimised.

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10
Q

When is a tree balanced?

A

If there are approximately the same number of leaves on each branch

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11
Q

What is the benefit of having a balanced tree

A

Guarantees that selecting an action will be performed in O(log2(n)) time

Unbalanced can be as bad as O(n)

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12
Q

When writing a decision tree, the most commonly used checks should be placed

a) close to the rood
b) close to the leaves

A

a) close to the root

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13
Q

How can a directed acyclic graph be produced?

What do you need to be careful of?

A

Allow certain nodes to be accessed by multiple branches

Don’t create infinite loops

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14
Q

What elements does an FSM have?

A

Finite set of states - wif FSM is in same state it will keep performing same action

Initial state

Finite set of transitions between states - triggered by internal or external events

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15
Q

What is a hierarchical state machine?

A

FSM that groups states belonging to the same context in a higher level state

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16
Q

How do transitions work in HFSMs?

A

Transitions within highlevel states work normally

Transitions between high level states are triggered from any state in the origin

17
Q

How do starting states work with HFSMs?

A

Starting high level state

Starting state within each high level state, used when transitioning in

18
Q

What are the advantages of behaviour trees?

A

Can do many things - path finding, planning etc.

Modular, scalable

Easy to develop - GUIs help with creation and manipulation

Can be combined into subtrees for more complex decisions

19
Q

What is a task?

A

An activity that, given some CPU time, returns a value

20
Q

What 3 elements does a basic behaviour tree consist of?

A

Conditions

Actions

Composites (collection of child tasks)

21
Q

Where do conditions and actions sit in the tree?

A

leaf nodes

22
Q

What 2 composite tasks are there?

A

Selector - returns success as soon as one child bejaviour succeeds (OR)

Sequence - returns success only if all child behaviours succeed

23
Q

What symbols are used for selectors and sequences?

A

Selector: Question mark

Sequence: Arrow

24
Q

What is partial ordering and why is it used?

A

Some random order mixed into some strict order

If not used behaviour can quickly become boring

25
What does a random selector do? What is its symbol?
Selects branches in a random order until one returns true TIlde with questionmark
26
What does a random sequence do? What is its symbol?
REturns true if all of its branches (selected in a random order) return true Wiggly arrow
27
What is a decorator? What are they often used for?
Add extended functionality to a task. Has a single child whose behaviour it modifies. Filters and loops
28
What does parallel do in BTs? How does it terminate?
Runs all its tasks simultaneously Terminate once one of the children fails Terminate once one of the children succeeds
29
Parallel can be used for continous condition checking T or F
True
30
What is a blackboard?
Place where any data can be stored, often per tree or subtree