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Questions and Answers
What role does knowledge play in an intelligent system?
Which representation uses logical symbols to express relationships?
How is knowledge defined in the provided content?
What is a proposition in the context of propositional logic?
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Which logical operator represents the idea of 'if ... then ...'?
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What does the expression P Q represent in propositional logic?
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Which of the following is a limitation of propositional logic?
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In first order logic, what is the role of predicates?
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What does the expression X.likes(X, apples) indicate?
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Why might propositional logic be insufficient for expressing certain statements?
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Study Notes
Knowledge Representation
- Knowledge representation stores and processes knowledge effectively.
- Knowledge is crucial for intelligent systems to make informed decisions.
- Knowledge bases (KBs) contain facts about the world.
Types of Knowledge Representation
- Data: Primitive, verifiable facts, often changing and voluminous.
- Information: Interpreted data.
- Knowledge: Relationships between datasets (information), used for deduction. Generalizes data and incorporates information about the behavior of world models.
General Knowledge Representation Schemas
- Logic-Based Representation: Uses first-order predicate logic (FOL).
- Procedural Representation: Employs production rules.
- Network Representation: Includes semantic networks and conceptual graphs.
- Structural Representation: Utilizes scripts, frames, and objects.
Logic Based Representation
Propositional Logic
- Represents knowledge as propositions, statements that are either true or false.
- Uses logical operators:
- Conjunction (and) ()
- Disjunction (or) ()
- Negation (not) ()
- Implication ( )
- Equivalence ().
Propositional Logic Limitations
- Only handles true or false values.
- Represents entire propositions as single symbols, limiting the representation of objects with properties.
- Difficulty in adapting to knowledge base modifications.
- Lack of expressiveness, requiring multiple propositions to define complex concepts.
First Order Logic (FOL)
- Extends propositional logic, incorporating objects, properties, and relations.
- Allows for beliefs about truth, falsehood, or uncertainty of relations.
- Uses constructs:
- Constants: Represents objects (e.g., john, apples)
- Predicates: Represents properties and relations (e.g., likes(john, apples))
- Functions: Transform objects (e.g., likes(john, fruit_of(apple_tree)))
- Variables: Represents any object (e.g., likes(X, apples))
- Quantifiers: Qualify variable values:
- Universal quantifier (): True for all objects (e.g., X.likes(X, apples))
- Existential quantifier (): True for at least one object (e.g., X.likes(X, apples))
First Order Logic and Prolog
- Prolog is a declarative programming tool for building artificial intelligence programs based on FOL.
- Prolog's foundation is first-order logic syntax, consisting of facts and rules.
Procedural Representation
Production Rules
- Represent knowledge as sets of “if condition then action” pairs.
- Follow a match-resolve-act cycle:
- Match: Checks if rule conditions hold.
- Resolve: Chooses a rule from the set of firing rules (conflict resolution).
- Act: Executes the chosen rule's action.
- Rules can write knowledge to working memory, potentially triggering other rules.
Network Representation
Semantic Networks
- Depict objects as nodes in a hierarchical network of relationships.
- Allow for inheritance of properties, analogous to object-oriented programming (OOP).
- Minimize redundancy by representing objects with the least number of nodes and links.
Structural Representation
Frame Representations
- Use a data structure, known as a frame, to represent knowledge about specific objects or concepts.
- Developed by Marvin Minsky in the 1970s for use in frame-based expert systems.
- Offer more intuitive value support compared to semantic networks, with slots containing constraints for acceptable values.
- Can be easily implemented using OOP techniques.
- Enable straightforward control of inheritance.
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Description
This quiz explores the essential concepts of knowledge representation, including different types such as data, information, and knowledge. It also covers various representation schemas like logic-based and procedural representations, essential for intelligent systems development.
