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Informed Search Game, Minimax Algorithm, and Alpha-Beta Pruning Informed search game is a strategy used in artificial intelligence (AI) to determine the best move to make in a game. It involves evaluating the potential outcome of a move and selecting the move that has the highest probability of leading to a win. The minimax algorithm is a commonly used approach in informed search game, which is used to determine the optimal move in a two-player game....

Sure, here’s a rewrite of the previous answer in Markdown format, while avoiding the use of pre-built modules wherever possible: Building an Expert System in Python using FOL for Meal Recommendation To build an expert system in Python that employs First-Order Logic (FOL) for a meal recommendation system based on available ingredients and meal type preference, you can follow the steps below: [[Define the Knowledge Base]]: Define a knowledge base using FOL to represent the available ingredients and meal type preferences....

Sure, here’s an implementation of the inference engine for the meal recommendation system: Inference Engine # Define the inference engine def infer_meal_recommendation(knowledge_base, available_ingredients): # Check if the available ingredients match any of the premises in the rules for rule in knowledge_base['rules']: match = True for premise in rule['premises']: if premise.startswith('not('): # Check if the ingredient is not in the available ingredients if premise[4:-1] in available_ingredients: match = False break else: # Check if the ingredient is in the available ingredients if premise not in available_ingredients: match = False break if match: return rule['conclusion'] # No rule matches the available ingredients return None This implementation defines an infer_meal_recommendation() function that takes the knowledge_base and the available_ingredients as inputs and returns the recommended meal as an output....

Sure, here’s an implementation of the user interface for the meal recommendation system: # Define the user interface def recommend_meal(): # Get the available ingredients from the user ingredients = input('Enter the available ingredients, separated by commas: ').split(',') # Get the meal preference from the user preference = input('Enter your meal preference (breakfast, lunch, or dinner): ') # Define the knowledge base knowledge_base = { 'rules': [ {'premises': ['eggs', 'bacon', 'not(bread)', 'not(cheese)'], 'conclusion': 'omelette'}, {'premises': ['bread', 'bacon', 'lettuce', 'tomato', 'not(mayonnaise)'], 'conclusion': 'BLT'}, {'premises': ['bread', 'turkey', 'lettuce', 'mayonnaise', 'not(bacon)', 'not(ham)'], 'conclusion': 'turkey sandwich'}, {'premises': ['pasta', 'cheese', 'bacon', 'not(tomato)', 'not(lettuce)', 'not(bread)', 'not(mayonnaise)'], 'conclusion': 'spaghetti carbonara'}, {'premises': ['chicken', 'pasta', 'cheese', 'not(bacon)', 'not(tomato)', 'not(lettuce)', 'not(bread)', 'not(mayonnaise)'], 'conclusion': 'chicken parmesan'} ] } # Infer the recommended meal recommended_meal = infer_meal_recommendation(knowledge_base, ingredients) # Display the recommended meal to the user if recommended_meal: print('Based on your available ingredients and meal preference, we recommend', recommended_meal, 'for', preference) else: print('Sorry, we could not find a suitable meal recommendation based on your available ingredients and meal preference....

# Define the knowledge base knowledge_base = { 'meal_preferences': { 'breakfast': ['eggs', 'bacon', 'breakfast sandwich'], 'lunch': ['BLT', 'turkey sandwich', 'ham sandwich', 'grilled cheese'], 'dinner': ['spaghetti carbonara', 'chicken parmesan', 'hamburger', 'grilled chicken'] }, 'rules': [ {'premises': ['eggs', 'bacon', 'not(bread)', 'not(cheese)'], 'conclusion': 'omelette'}, {'premises': ['bread', 'bacon', 'lettuce', 'tomato', 'not(mayonnaise)'], 'conclusion': 'BLT'}, {'premises': ['bread', 'turkey', 'lettuce', 'mayonnaise', 'not(bacon)', 'not(ham)'], 'conclusion': 'turkey sandwich'}, {'premises': ['pasta', 'cheese', 'bacon', 'not(tomato)', 'not(lettuce)', 'not(bread)', 'not(mayonnaise)'], 'conclusion': 'spaghetti carbonara'}, {'premises': ['chicken', 'pasta', 'cheese', 'not(bacon)', 'not(tomato)', 'not(lettuce)', 'not(bread)', 'not(mayonnaise)'], 'conclusion': 'chicken parmesan'} ]} # Define the user interface def recommend_meal(): # Get the available ingredients from the user ingredients = input('Enter the available ingredients, separated by commas: ')....