Answers.Body: <p>There are a few ways to tackle this. You could make an AI that is simply a series of IF statements, or you could actually make an AI that would actually take in the situation and come up with a sensible solution.</p>
  
  <ul>
  <li><p>IF Approach - You make a series of IF statements that come up with a sensible action to execute. This is the method that Minecraft uses. The resulting action were recorded from some of the best players.</p></li>
  <li><p>True AI - Have your Character execute random actions and learn the consequences of them. The, train it to execute various actions for certain scenarios.</p></li>
  </ul>
  
  <p>The main difference between these two approaches is that IF statements have a constant and predictable behavior, while the AI approach has a very bad startup value but ends up improving over time.</p>
  
  <p>There is no "best" method, it is up to you to choose one or the other or a mix of both.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-05-01T12:52:33.403Z
Answers.Text: Hey, If I missed any other approaches, please put a comment.
Answers.UserDisplayName: FreezePhoenix
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-05T10:40:46.903Z
Answers.Text: I think you missed search methods, which could be used instead of or to augment AI-based methods. Search algorithms are often used for path-finding in game environments, and can easily be extended to other actions than movement, provided there is some heuristic to measure success. There is a strong relation between search algorithms like MCTS and AI planning algorithms.
Answers.UserDisplayName: Neil Slater
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-05-01T12:47:15.267Z
Answers.LastActivityDate: 2018-05-01T12:47:15.267Z
Answers.UserDisplayName: FreezePhoenix
Answers.Body: <p>I suggest starting with a much simpler scenario of a 1v1 linear gladiator combat. Once the AI is developed for the simplest case scenario, it could be expanded to conditions beyond the 1v1, such as 1v2 or multiple opponents in a free-for-all.</p>
  <p>The best approach is going to be whatever you are most comfortable programming. A simple neural network implementation would be effective.</p>
  <p>The scenario you've listed in your question doesn't have an optimal solution regarding game theory. The game space and the programming methodology will limit any AI solution.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-09-05T14:58:56.810Z
Answers.LastActivityDate: 2023-09-05T14:58:56.810Z
Answers.UserDisplayName: Ted Ferguson
Body: <p>Let's assume a common game scenario of several characters in a combat arena. Each character has different strengths and weaknesses. The arena has traps and tools. Suppose the characters had only very basic moves such as step in a direction, shoot, climb, duck, pick up item,  use item, drag heavy object. Each move has a chance of success  based on the context (e.g. range to target). What AI, machine learning, or evolutionary approach could be used to generate personalized tactics for each character based on repeated runs of the scenario?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-05-01T12:41:30.843Z
Comments.Text: It depends on how long you want to spend training or if you just want a setup AI that works like a charm the first day.
Comments.UserDisplayName: FreezePhoenix
Tags: <game-ai><problem-solving>
Title: Developing character tactics via repeated trials
UserDisplayName: davideps
ThreadText: # Developing character tactics via repeated trials
  
  (Asked by: username_0 on 2018-04-30)
  
  Let's assume a common game scenario of several characters in a combat arena. Each character has different strengths and weaknesses. The arena has traps and tools. Suppose the characters had only very basic moves such as step in a direction, shoot, climb, duck, pick up item, use item, drag heavy object. Each move has a chance of success based on the context (e.g. range to target). What AI, machine learning, or evolutionary approach could be used to generate personalized tactics for each character based on repeated runs of the scenario?
  
  **Tags:** game-ai, problem-solving
  
  > **Comments:**
  > **username_1**: It depends on how long you want to spend training or if you just want a setup AI that works like a charm the first day.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2018-05-01. Score: 0
  
  There are a few ways to tackle this. You could make an AI that is simply a series of IF statements, or you could actually make an AI that would actually take in the situation and come up with a sensible solution.
  
  * IF Approach - You make a series of IF statements that come up with a sensible action to execute. This is the method that Minecraft uses. The resulting action were recorded from some of the best players.
  * True AI - Have your Character execute random actions and learn the consequences of them. The, train it to execute various actions for certain scenarios.
  
  The main difference between these two approaches is that IF statements have a constant and predictable behavior, while the AI approach has a very bad startup value but ends up improving over time.
  
  There is no "best" method, it is up to you to choose one or the other or a mix of both.
  
  > **Comments:**
  > **username_1**: Hey, If I missed any other approaches, please put a comment.  
  > **username_2**: I think you missed search methods, which could be used instead of or to augment AI-based methods. Search algorithms are often used for path-finding in game environments, and can easily be extended to other actions than movement, provided there is some heuristic to measure success. There is a strong relation between search algorithms like MCTS and AI planning algorithms.  
  
  ---
  
  ### Answer by username_3 on 2023-09-05. Score: 0
  
  I suggest starting with a much simpler scenario of a 1v1 linear gladiator combat. Once the AI is developed for the simplest case scenario, it could be expanded to conditions beyond the 1v1, such as 1v2 or multiple opponents in a free-for-all.
  
  The best approach is going to be whatever you are most comfortable programming. A simple neural network implementation would be effective.
  
  The scenario you've listed in your question doesn't have an optimal solution regarding game theory. The game space and the programming methodology will limit any AI solution.
  
  
  ---
  
  