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Abstract Semantic networks are a form of knowledge representation used in artificial intelligence, linguistics, and cognitive science. They consist of a set of nodes (representing concepts or entities) connected by edges (representing relationships between them). These networks visually represent how knowledge is structured and how concepts are related. ### Key Features of Semantic Networks: 1. **Nodes**: Represent objects, concepts, or entities (e.g., "Dog," "Cat," "Animal"). 2. **Edges**: Represent relationships between nodes (e.g., "is a," "has," "part of"). 3. **Hierarchical Structure**: Often organized in a hierarchy, where more specific nodes inherit attributes from more general ones (e.g., "Dog" is a type of "Animal"). 4. **Linking Words/Predicates**: Relationships between nodes are usually labeled with predicates like "is a," "part of," "located in," etc. 5....

Abstract  A **Rule-Based System (RBS)** is an artificial intelligence system that uses rules as the primary means for decision-making or problem-solving.   These systems consist of: 1. **Knowledge Base**: Contains a set of rules that are generally structured in the form of "IF condition THEN action" or "IF condition THEN conclusion."     2. **Inference Engine**: The component that applies the rules to the knowledge base to derive conclusions or make decisions based on input data. It matches the conditions of the rules with the available data and executes the appropriate actions. 3. **User Interface**: Allows interaction between the system and the user, such as inputting data and receiving results. ### Key Features: - **Declarative nature**: Knowledge is represented in the form of rules. - **Transparency**: The reasoning process is typically understandable and explainable, as it follows explicit rules. - **Flexibility**: Easy to update by adding or modify...

Abstract Knowledge representation and reasoning (KRR) is a field of artificial intelligence (AI) that focuses on how to represent information in a way that computers can use to solve problems. KRR is important because it allows AI programs to make deductions and answer questions intelligently, similar to how humans do.    Here are some key aspects of KRR:    Representation KRR uses techniques like ontologies and knowledge graphs to represent information in a way that computers can understand. These techniques allow for the representation of relationships and hierarchies within the data.    Reasoning KRR uses reasoning engines to perform logical operations on the represented knowledge to derive new information. This includes drawing conclusions, making inferences, and solving problems.    Applications KRR can be used for a variety of applications, such as path planning, diagnosing medical conditions, and having natural language dialogs.  ...

Abstract : Game Playing is an important domain of artificial intelligence. Games don’t require much knowledge; the only knowledge we need to provide is the rules, legal moves and the conditions of winning or losing the game. Both players try to win the game. So, both of them try to make the best move possible at each turn. Searching techniques like BFS(Breadth First Search) are not accurate for this as the branching factor is very high, so searching will take a lot of time. So, we need another search procedures that improve – Generate procedure so that only good moves are generated. Test procedure so that the best move can be explored first. The most common search technique in game playing is Minimax search procedure. It is depthfirst depth-limited search procedure. It is used for games like chess and tic-tac-toe. The minimax algorithm is a recursive algorithm used in game theory to find the best move for a player in a game. It's based on the assumption that the other p...