Computer Game Programming Intelligence I: Basic Decision-Making Mechanisms
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1 Computer Game Programming Intelligence I: Basic Decision-Making Mechanisms Maxim Likhachev Robotics Institute Carnegie Mellon University
2 AI Architecture from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 2
3 Decision-making Framework e.g., health level, availability of ammunition, e.g., attack, flee, explore the sound, e.g., map, see enemy, hear sound, from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 3
4 Decision-making Framework e.g., health level, availability of ammunition, e.g., attack, flee, explore the sound, e.g., map, see enemy, hear sound, Any ideas for how to implement decision-making? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 4
5 Basic Decision-making Mechanisms for this Class Decision Trees Finite-state Machines Basic Behavior Trees Maxim Likhachev Carnegie Mellon University 5
6 Basic Decision-making Mechanisms for this Class Decision Trees Finite-state Machines Basic Behavior Trees Maxim Likhachev Carnegie Mellon University 6
7 Decision Trees Formalization of a set of nested if-then rules Very popular: easy-to-implement, intuitive (=easy-to-debug) and fast Require careful manual design (theoretically, learning trees is also possible) from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 7
8 Decision Trees Formalization of a set of nested if-then rules Very popular: easy-to-implement, intuitive (=easy-to-debug) and fast Require careful manual design (theoretically, learning trees is also possible) K-ary trees are possible but binary decision trees are much more common from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 8
9 Decision Trees Support for multi-valued input variables in binary decision-trees Example: Depending on the size of the enemy troops, attack, stand ground or retreat How to implement in a binary decision trees? Maxim Likhachev Carnegie Mellon University 9
10 Decision Trees Support for multi-valued input variables in binary decision-trees Example: Depending on the size of the enemy troops, attack, stand ground or retreat No Is N < 5? Yes No Stand Ground Is N < 2? Yes Attack N=size of the enemy troops Retreat Maxim Likhachev Carnegie Mellon University 10
11 Decision Trees Support for continuous input variables in binary decision-trees Example: Depending on the distance to the enemy, hand-to-hand combat, shoot, hide How to implement in a binary decision trees? Maxim Likhachev Carnegie Mellon University 11
12 Decision Trees Support for continuous input variables in binary decision-trees Example: Depending on the distance to the enemy, hand-to-hand combat, shoot, hide Is d < 20.0m? No No Shoot Is d < 5.0m? Yes Hand-to-hand combat Yes d=distance to enemy Hide Maxim Likhachev Carnegie Mellon University 12
13 Decision Trees Support for complex decision formulae Example: Attack whenever enemy is close OR (low-on-health AND not too far) Is enemy low-on-health? No No Yes Is enemy close? Yes Attack Do nothing No Is enemy too far? Yes Attack Do nothing Maxim Likhachev Carnegie Mellon University 13
14 Decision Trees Support for complex decision formulae Example: Attack whenever enemy is close OR (low-on-health AND not too far) Can we represent any Boolean formula with a binary decision tree? Proof? Is enemy low-on-health? No No Yes Is enemy close? Yes Attack Do nothing No Is enemy too far? Yes Attack Do nothing Maxim Likhachev Carnegie Mellon University 14
15 Decision Trees Support for complex decision formulae Example: Attack whenever Proof? enemy is close OR (low-on-health AND not too far) A OR (B AND C) Each row is a branch in the tree A B C Outcome No Yes No Yes True? Maxim Likhachev Carnegie Mellon University 15
16 Decision Trees Making the decision tree traversal fast is important Which decision tree is better for decision-making? vs. from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 16
17 Decision Trees Making the decision tree traversal fast is important Which decision tree is better for decision-making? What does it depend on? vs. from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 17
18 Decision Trees Making the decision tree traversal fast is important Which decision tree is better for decision-making? What does it depend on? vs. Frequency (probability) of outcome (e.g., what if A happens 99% of the time) The computational complexity of the test (e.g., what if testing for G is very expensive) from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 18
19 Decision Trees Making the decision tree traversal fast is important Merging the branches: from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 19
20 Decision Trees How to deal with the predictability of AI? Any ideas? Maxim Likhachev Carnegie Mellon University 20
21 Decision Trees How to deal with the predictability of AI? Random Decision Trees: To avoid switching every frame: use hysteresis (memory) from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 21
22 Basic Decision-making Mechanisms for this Class Decision Trees Finite-state Machines Basic Behavior Trees Maxim Likhachev Carnegie Mellon University 22
23 Finite State Machines Basic FSM Initial State from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 23
24 Finite State Machines Basic FSM Advantages over Decision Trees? Disadvantages over Decision Trees? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 24
25 Finite State Machines Basic FSM How to introduce unpredictability? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 25
26 Finite State Machines Hierarchical FSM How it changes to react to re-charge now alarm? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 26
27 Finite State Machines Hierarchical FSM from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 27
28 Finite State Machines Hierarchical FSM What if an additional alarm? Upper bound on # of states for N alarms? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 28
29 Finite State Machines Hierarchical FSM: strict hierarchy with only global alarms State: [State at Level i, State at Level 1] (for i active FSMs) All triggers get acted upon by FSM at level i Whenever FSM at Level i exits, FSM at level i-1 becomes dominant Its own FSM from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 29
30 Finite State Machines Hierarchical FSM: strict hierarchy with additional direct transitions Direct transitions between levels allow to leave the source state from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 30
31 Finite State Machines Hierarchical FSM: strict hierarchy with additional direct transitions Direct transitions between levels allow to Search leave state the is source left state When GetPower is done, Clean Up is entered from scratch from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 31
32 Finite State Machines Hierarchical FSM: strict hierarchy with additional direct transitions More complex example: from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 32
33 Finite State Machines Combining FSM and decision trees Why bother? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 33
34 Basic Decision-making Mechanisms for this Class Decision Trees Finite-state Machines Basic Behavior Trees Maxim Likhachev Carnegie Mellon University 34
35 Basic Behavior Trees Became very popular after Halo 2 game [2004] Maxim Likhachev Carnegie Mellon University 35
36 Basic Behavior Trees Became very popular after Halo 2 game [2004] Especially, when coupled with graphical interfaces to edit them Screenshot of GameBrains GUI Maxim Likhachev Carnegie Mellon University 36
37 Basic Behavior Trees Collection of simple tasks arranged in a tree structure One of the main advantages: Tasks and sub-trees can be reused!!! Screenshot of GameBrains GUI Maxim Likhachev Carnegie Mellon University 37
38 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Condition task tests for a condition Examples of behavior trees that consist of Conditions tasks only: Door open? Health level OK? Enemy close-by? Maxim Likhachev Carnegie Mellon University 38
39 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Action task alters the state of the game Examples of behavior trees that consist of Actions tasks only: Move to room Find a path Play audio sound Talk to the player Maxim Likhachev Carnegie Mellon University 39
40 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Condition and Action tasks are always at the leafs of the tree Composite task sequences through them Maxim Likhachev Carnegie Mellon University 40
41 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Condition and Action tasks are always at the leafs of the tree Composite task sequences through them Two types of Composite tasks: Selector returns as soon as the first leaf task is successful? Attack Flee Declare defeat Maxim Likhachev Carnegie Mellon University 41
42 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Condition and Action tasks are always at the leafs of the tree Composite task sequences through them Two types of Composite tasks: Selector returns as soon as the first leaf task is successful Sequencer returns as soon as the first leaf task fails? Attack Flee Declare defeat Door open? Move (into room) Maxim Likhachev Carnegie Mellon University 42
43 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Condition and Action tasks are always at the leafs of the tree Composite task sequences through them Two types of Composite tasks: Selector returns as soon as the first leaf task is successful? Sequencer returns as soon as the first leaf task fails Tasks are often parameterized Attack Flee Declare defeat Door open? Move (into room) Maxim Likhachev Carnegie Mellon University 43
44 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Example:? What does it do? Door open? Move (into room) Move (to door) Open door Move (into room) Maxim Likhachev Carnegie Mellon University 44
45 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Example:? What does it do? Door open? Move (into room) Move (to door)? Move (into room) Door unlocked? Open door Door locked? Barge door Maxim Likhachev Carnegie Mellon University 45
46 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Example:? Can the tree always be structured so that sequence and selector levels alternate? Door open? Move (into room) Move (to door)? Move (into room) Door unlocked? Open door Door locked? Barge door Maxim Likhachev Carnegie Mellon University 46
47 Basic Behavior Trees Type of tasks: Conditions, Actions and Composites Each returning either success or failure Example:? How to reduce predictability of the behavior? Door open? Move (into room) Move (to door)? Move (into room) Door unlocked? Open door Door locked? Barge door Maxim Likhachev Carnegie Mellon University 47
48 Basic Behavior Trees Behavior trees with Order Randomization for some Sequencers and Selectors Example: How to reduce predictability of the behavior? from Artificial Intelligence for Games by I. Millington & J. Funge Maxim Likhachev Carnegie Mellon University 48
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