Rule-based systems are a fundamental AI technique used to model decision-making processes. They consist of a set of "if-then" rules that define the behavior of game characters based on specific conditions. This approach is straightforward and highly interpretable, making it a popular choice for implementing AI in video games.

• Definition: A rule is a conditional statement that specifies an action to be taken when certain conditions are met.
• Structure: Typically follows an "if-then" format.
  • Example: if (enemy_in_sight) then (attack)

• Definition: A collection of rules that define the behavior of the AI.
• Organization: Rules are often organized in a priority order to resolve conflicts when multiple rules are applicable.

• Definition: The component that applies the rules to the current state of the game to determine the actions to be taken.
• Types:
  • Forward Chaining: Starts with the available data and applies rules to infer conclusions.
  • Backward Chaining: Starts with a goal and works backward to determine the necessary conditions to achieve that goal.

Let's implement a simple rule-based system for a game character that decides whether to attack, defend, or retreat based on the state of the game.

1. Define the Rules

2. Create the Inference Engine

3. Define the Game State

4. Determine the Action

• Rules: We defined four rules that cover different scenarios based on the character's health and whether an enemy is in sight.
• Inference Engine: This function iterates through the rules and applies the first rule that matches the current state.
• Game State: Represents the current conditions of the game, such as the character's health and whether an enemy is visible.
• Action: The inference engine determines the appropriate action based on the game state and rules.

Task: Add a new rule to the system that makes the character seek health packs if their health is between 20 and 50 and no enemy is in sight.

Solution:

1. Define the New Rule:

   new_rule = {"condition": lambda state: not state["enemy_in_sight"] and 20 < state["health"] <= 50, "action": "seek_health_pack"}
   

2. Add the New Rule to the Rule Base:

   rules.append(new_rule)
   

3. Test the Updated System:

   game_state = {
       "enemy_in_sight": False,
       "health": 30
   }
   action = inference_engine(game_state, rules)
   print(f"The character will: {action}")
   

Expected Output:

Task: Modify the inference engine to use backward chaining to determine the actions needed to achieve a goal, such as "survive".

Solution:

1. Define the Goal:

   goal = "survive"
   

2. Modify the Inference Engine:

   def backward_chaining(goal, state, rules):
       if goal == "survive":
           if state["health"] > 20:
               return "defend" if state["enemy_in_sight"] else "patrol"
           else:
               return "retreat"
       return "idle"
   

3. Test the Backward Chaining System:

   game_state = {
       "enemy_in_sight": True,
       "health": 15
   }
   action = backward_chaining(goal, game_state, rules)
   print(f"The character will: {action}")
   

Expected Output:

• Overlapping Rules: Ensure that rules are mutually exclusive or have a clear priority to avoid conflicts.
• Complex Conditions: Simplify conditions to make the rule base easier to manage and understand.
• Performance: Optimize the inference engine for performance, especially in complex games with many rules.

Rule-based systems provide a straightforward and interpretable method for implementing AI in video games. By defining clear rules and using an inference engine, game developers can create intelligent behaviors for game characters. This module covered the basics of rule-based systems, provided practical examples, and included exercises to reinforce the concepts. In the next module, we will explore more advanced decision-making techniques such as behavior trees.