The A* algorithm combines the advantages of Dijkstra's algorithm and greedy best-first search to find the shortest path between two points.

Behavior trees provide a structured way to represent and manage AI tasks and decision-making processes.

The current state of an AI agent in a state machine is influenced by its previous state, its current actions, and its environment.

Rule-Based AI involves defining a set of rules that determine how AI agents should behave in different situations.

The primary goal of AI in games is to make the game more enjoyable for players by creating intelligent and engaging AI opponents or companions.

Machine Learning involves training a computer program to learn and improve its performance over time based on data and experience.

A navigation mesh is a data structure that represents the game world's geometry in a way that facilitates efficient pathfinding.

Cellular Automata involve simulating the behavior of natural phenomena by dividing the game world into cells and applying rules to determine how each cell changes over time.

Finite state machines are used in Game AI to represent the behavior of AI agents by defining a set of states and transitions between those states.

Simulation involves creating a virtual world that AI agents can explore and learn from, allowing them to develop new strategies and behaviors.

Decision trees are used in Game AI to make decisions based on input data by evaluating a series of conditions and selecting the appropriate action.

Deep Learning involves using neural networks with multiple layers to process and interpret data, enabling AI agents to learn complex patterns and relationships.

A blackboard system is a data structure that allows AI agents to share information and coordinate their actions by writing and reading data to a shared memory space.

Genetic Algorithms involve using evolutionary algorithms to optimize solutions to problems by simulating the process of natural selection.

Fuzzy logic is used in Game AI to make decisions based on uncertain or imprecise data by representing knowledge and reasoning in a way that allows for partial truth and uncertainty.