Knowledge Representation and Reasoning

Knowledge and Knowledge Representation

• Knowledge handling involves two main aspects: representing knowledge and reasoning within that representation.
• Knowledge representation aims to store and process knowledge effectively to support intelligent decision-making by intelligent agents (programs).
• A knowledge base (KB) is essential for intelligent agents to make decisions, and it comprises facts about the world.
• Knowledge differentiates from data and information by defining relationships between sets of data, facilitating information deduction.
• Knowledge is general and encompasses information about the behavior of abstract world models.

General Knowledge Representation Schemas

• Logic-based representation: Utilizes first-order predicate logic (FOL).
• Procedural representation: Leverages production rules.
• Network representation: Employs semantic networks and conceptual graphs.
• Structural representation: Employs scripts, frames, and objects.

Logic-based Representation: Propositional Logic

• Represents knowledge as propositions, which are statements that are either true or false.
• Logical operators connect propositions, including:
  • Conjunction (and) (∧)
  • Disjunction (or) (∨)
  • Negation (not) (¬)
  • Implication (→)
  • Equivalence (↔).

Limitations of Propositional Logic

• Handles only true or false values, limiting its expressiveness.
• Represents entire propositions as single symbols, making it challenging to represent properties and relations between objects.
• Lacks flexibility in handling changes to the knowledge base.
• It struggles to express complex statements concisely, such as describing all locations being clean in the vacuum cleaner example.

First-Order Logic (FOL)

• Extends propositional logic by introducing relations about objects and functions on those objects.
• Allows for beliefs regarding the truth, falsity, or uncertainty of these relations.

First-Order Logic Constructs

• Constants: Represent objects (e.g., john, apples).
• Predicates: Represent properties and relations (e.g., likes(john, apples)).
• Functions: Transform objects (e.g., likes(john, fruit_of(apple_tree))).
• Variables: Represent any object (e.g., likes(X, apples)).
• Quantifiers: Qualify variable values:
  • Universal (∀): True for all objects (e.g., ∀X.likes(X, apples)).
  • Existential (∃): Exists at least one object (e.g., ∃X.likes(X, apples)).

First-Order Logic and Prolog

• Prolog: A declarative programming tool used for developing artificial intelligence programs.
• Prolog is based on First-Order Logic syntax:
  • Facts: Represent basic knowledge.
  • Rules: Define relationships between facts.

Procedural Representation: Production Rules

• Defined as sets of "if condition then action" pairs.
• Operate in a match-resolve-act cycle:
  • Match: The agent checks if each rule's condition holds.
  • Resolve: Multiple rules might fire, requiring conflict resolution to choose one rule from the conflict set.
  • Act: The selected rule "fires," and the action is carried out.
• Working Memory:
  • Rules can write knowledge to working memory.
  • This knowledge can trigger other rules.

Network Representations: Semantic Networks

• Nodes in semantic networks represent objects.
• Semantic networks create hierarchical structures of object relationships.
• Inheritance of properties is akin to object-oriented programming (OOP).

Explore the fundamentals of knowledge handling, including its representation and reasoning processes. This quiz covers various knowledge representation schemas such as logic-based, procedural, network, and structural representations, highlighting their roles in intelligent agents. Test your understanding of how knowledge differs from data and information.