Decisions trees
Decision trees are evaluated from root to leaf, bottom to top. For this to work properly, child nodes of each parent must represent all possible decisions for that node. If a node can be answered 'Yes, No and Maybe, then there must be 3 children, the 'Yes' node, 'No' node and 'Maybe' node.
As you see from the example, depending on the first decision the outcomes change, so if you aren't hungry the next step is go to sleep, but if you are hungry then the next decision is seeing if you have enough money for what you want to eat, if you have 25$ go to a restaurant, if not then buy a hamburger. This is just one example of a decision tree and you can change the outcomes to your liking.
State machines
A finite state machine (FSM) that is based on a hypothetical machine made of one or more states. Only a single state can be active at one time, so the machine must transition from one state to another in order to perform different actions.
For example one state of a game could be the game 'running', this state could change once the player 'dies' resulting in the 'game over' state and then that state changes straight back to the game running once the player hits the start button again. This is just one pathway of states, another state change from game running could simply be the player pausing the game, the game then changes into a state of being paused, the state is reverted once the player clicks 'resume' again.
Fuzzy logic
This is game logic that you and the AI character decides, so whether or not the AI character is 'hurt' or not is dependent on the developer and heuristics that are set up. For example the AI character could take damage resulting in 50% health remaining, whether or not behaviour of that AI changes is dependent on either how the AI is developed and heuristics, so the AI could be developed to run away when it has 50% health remaining, but what also can happen is heuristics could alter this value so that the AI may run away when he is at 70% health or even 30% health.
The term fuzzy means not clear, when something is not simply true or false then things need to be defined in a fuzzy manner. Fuzzy logic can be compared to human decision making meaning its not quite cut and clean yes or no when it comes to most things.
Decision trees are evaluated from root to leaf, bottom to top. For this to work properly, child nodes of each parent must represent all possible decisions for that node. If a node can be answered 'Yes, No and Maybe, then there must be 3 children, the 'Yes' node, 'No' node and 'Maybe' node.
As you see from the example, depending on the first decision the outcomes change, so if you aren't hungry the next step is go to sleep, but if you are hungry then the next decision is seeing if you have enough money for what you want to eat, if you have 25$ go to a restaurant, if not then buy a hamburger. This is just one example of a decision tree and you can change the outcomes to your liking.
State machines
A finite state machine (FSM) that is based on a hypothetical machine made of one or more states. Only a single state can be active at one time, so the machine must transition from one state to another in order to perform different actions.
For example one state of a game could be the game 'running', this state could change once the player 'dies' resulting in the 'game over' state and then that state changes straight back to the game running once the player hits the start button again. This is just one pathway of states, another state change from game running could simply be the player pausing the game, the game then changes into a state of being paused, the state is reverted once the player clicks 'resume' again.
Fuzzy logic
This is game logic that you and the AI character decides, so whether or not the AI character is 'hurt' or not is dependent on the developer and heuristics that are set up. For example the AI character could take damage resulting in 50% health remaining, whether or not behaviour of that AI changes is dependent on either how the AI is developed and heuristics, so the AI could be developed to run away when it has 50% health remaining, but what also can happen is heuristics could alter this value so that the AI may run away when he is at 70% health or even 30% health.
The term fuzzy means not clear, when something is not simply true or false then things need to be defined in a fuzzy manner. Fuzzy logic can be compared to human decision making meaning its not quite cut and clean yes or no when it comes to most things.
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