Knowledge Representation in Artificial Intelligence: Expert Systems and Epistemology, Slides of Introduction to Computing

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CS613 INTRODUCTION TO

ARTIFICIAL INTELLIGENCE

Lecture 6

Knowledge Representation

The Goal of Expert Systems

• We need to be able to separate the actual meanings of words with the reasoning process itself.
• We need to make inferences w/o relying on semantics.
• We need to reach valid conclusions based on facts only.

Arguments in Logic

• An argument refers to the formal way facts and rules of inferences are used to reach valid conclusions.
• The process of reaching valid conclusions is referred to as logical reasoning.

How is Knowledge Used?

• Knowledge has many meanings – data, facts, information.
• How do we use knowledge to reach conclusions or solve problems?
• Heuristics refers to using experience to solve problems – using precedents.
• Expert systems may have hundreds / thousands of micro-precedents to refer to.

Categories of Epistemology

• Philosophy • A priori
• A posteriori • Procedural
• Declarative • Tacit

A Priori Knowledge

• “That which precedes”
• Independent of the senses
• Universally true
• Cannot be denied without contradiction

Procedural Knowledge

Knowing how to do something:

• Fix a watch
• Install a window
• Brush your teeth
• Ride a bicycle

Declarative Knowledge

• Knowledge that something is true or false
• Usually associated with declarative statements
• E.g., “Don’t touch that hot wire.”

Knowledge in Rule-Based Systems

• Knowledge is part of a hierarchy.
• Knowledge refers to rules that are activated by facts or other rules.
• Activated rules produce new facts or conclusions.
• Conclusions are the end-product of inferences when done according to formal rules.

Expert Systems vs. Humans

• Expert systems infer – reaching conclusions as the end product of a chain of steps called inferencing when done according to formal rules.
• Humans reason

Metaknowledge

• Metaknowledge is knowledge about knowledge and expertise.
• Most successful expert systems are restricted to as small a domain as possible.
• In an expert system, an ontology is the metaknowledge that describes everything known about the problem domain.
• Wisdom is the metaknowledge of determining the best goals of life and how to obtain them.

Knowledge processing phases

• Knowledge representation
  • Formal representation of domain knowledge
  • Production rules, semantic network, frame, formal logic
• Knowledge utilization
  • Reasoning mechanism
  • Deduction, induction, abduction, hypothetical reasoning, analogical reasoning
  • Highly related to knowledge representation
• Knowledge acquisition or refinement
  • Interview with experts
  • Bottleneck: how to manage a huge amount of knowledge?
  • Machine learning

Knowledge types

• Declarative knowledge
  • “What” knowledge
• Procedural knowledge
  • “How” knowledge Examples
  • There is a way to get from this campus to Tokyo (declarative knowledge)
  • Take the bicycle to the station and then take the shinkansen to Tokyo (procedural knowledge)

Knowledge types

• Heuristic knowledge or shallow knowledge
  • Not complete, but usually holds
  • Based on experience
  • Individual, non-specific, non-logical, informal
  • Used directly for problem solving or to improve the efficiency of the problem solving process
  • when the engine makes sound A, part B could be broken
• Theoretical knowledge or deep knowledge
  • The mathematical and logical knowledge behind the problem domain
  • Usually combined with heuristic knowledge in problem solving
  • Model based reasoning : object modeling, causal networks