Knowledge Based Systems - ICS 2405

Course Overview

  • ICS 2405 Knowledge Based Systems course focuses on artificial intelligence and knowledge-based systems, emphasizing program development within specific application domains.
  • Topics include AI techniques, case studies for system development principles, knowledge representation, and acquisition, with hands-on experience in building expert systems.
  • Aims to develop computer programs competent in cognitive tasks and computational models of human intelligence within Cognitive Science.
  • Learning outcomes include understanding knowledge-based systems representation, automatic reasoning, and inductive/deductive learning, with the ability to implement small knowledge-based systems.

Assessment

  • Assignments: 20% (online submission)
  • CAT: 20% (written, at JKUAT main campus or approved centers)
  • Final Examination: 60% (written, at JKUAT main campus or approved centers)

Key Concepts

  • Artificial Intelligence (AI): Making computer systems that mimic human intelligence, including perception, natural language processing, reasoning, problem-solving, and learning.
  • Knowledge-Based Systems (KBS): Software systems using explicit knowledge representation and reasoning mechanisms for high-level problem-solving.
  • Knowledge Engineering: Developing KBS, including knowledge acquisition, representation, and validation.
  • Inference: Drawing conclusions from facts and rules.
  • Knowledge Representation: Structuring knowledge for AI systems.

Types of Intelligence

  • General
  • Linguistic-Verbal
  • Logical-Mathematical
  • Musical
  • Spatial
  • Intrapersonal
  • Interpersonal
  • Naturalist
  • Bodily-Kinesthetic

AI History

  • 1943: McCulloch & Pitts - Boolean circuit model of the brain
  • 1950: Turing's "Computing Machinery and Intelligence"
  • 1950s: Early AI programs (e.g., Samuel's checkers, Newell & Simon’s Logic Theorist)
  • 1956: Dartmouth meeting - "Artificial Intelligence" term adopted
  • 1966–74: AI discovers computational complexity
  • 1969–79: Early development of knowledge-based systems
  • 1980–88: Expert systems industry boom
  • 1988–93: Expert systems industry decline (“AI Winter”)
  • 1985–95: Neural networks regain popularity
  • 1988–: Resurgence of probabilistic methods; Nouvelle AI (ALife, GAs, soft computing)

Turing Test

  • Tests a computer's ability to exhibit human-like intelligence.
  • Requires:
    • Natural language processing
    • Knowledge representation
    • Reasoning
    • Learning and adaptation

Characteristics of AI

  • Symbolic Processing: Manipulating symbols rather than numbers.
  • Heuristics: Using rules of thumb for problem-solving.
  • Inferencing: Reasoning with facts and rules.
  • Pattern Matching: Describing objects based on qualitative features.
  • Knowledge Processing: Managing facts, concepts, and relationships.
  • Knowledge Bases: Using collected knowledge for problem-solving.

Advantages of AI

  • Permanent and consistent.
  • Easy to duplicate and disseminate.
  • Can be less expensive than human intelligence.
  • Faster execution of certain tasks.

Components of Knowledge Based Systems

  • Knowledge Base: Contains domain knowledge.
  • Inference Engine: Reasoning mechanism.
  • User Interface: Communication between user and KBS.
  • Explanation Facilities: Explains reasoning.
  • Learning Facilities: Allows system to learn.

Knowledge Engineering Process

  • Knowledge acquisition
  • Knowledge Validation
  • Knowledge Representation
  • Inferencing
  • Explanation

Types of Knowledge Based Systems

  • Expert Systems: Mimic decision-making.
  • Neural Networks: Model brain functions, pattern recognition.
  • Case Based Reasoning: Learn from past experiences.
  • Fuzzy Logic Systems: Handle uncertain knowledge.

Knowledge Acquisition

  • Extracting knowledge from experts and converting it for computer use.
  • Characteristics: experiential, descriptive, qualitative, undocumented, changing.
  • Process:
    • Identification
    • Conceptualization
    • Formalization
    • Implementation
    • Testing

Players in KBS Development

  • Domain Expert: Provides expertise.
  • Knowledge Engineer: Designs and builds the system.
  • Programmer: Implements the system.
  • Project Manager: Manages the project.
  • End-User: Uses the system.

Knowledge Representation

  • Framework for storing and manipulating knowledge.
  • Uses: learning, retrieval, reasoning.
  • Schemes: Natural language, logic, production rules, semantic networks, frames, etc.

Logic Representation

  • Uses syntax, semantics, and proof theory.
  • Types: Propositional, Predicate, Fuzzy, Temporal, Description Logics.

Semantic Networks

  • Represent knowledge using interconnected nodes and arcs.
  • Nodes: concepts, entities, attributes, events, values.
  • Arcs: Relationships between concepts.

Frames

  • Represent stereotyped information.
  • Features: Object attributes and values.