Knowledge Representation in AI Explained in Simple Terms

In recent years, knowledge-centric AI applications like self-driving cars, intelligent medical diagnosis systems, personal assistants, and content moderation tools have become increasingly prominent in our lives. The exponential progress in this field has sparked both optimism as well as valid concerns regarding transparency and ethics.

But how exactly are these intelligent systems able to exhibit complex human-like capabilities based on the knowledge they have access to? What does it mean for an AI application to "understand" the real-world and make smart decisions accordingly?

The key technique powering this new generation of AI lies in knowledge representation – encoding facts, concepts, relationships and procedures about the world in a format that computer systems can process and reason over.

In this guide, we will demystify knowledge representation in AI and survey different techniques used to model knowledge along with their relative strengths and weaknesses.

The Quest for Building Thinking Machines

The pioneering research work in knowledge representation for AI was fueled by the urge to create thinking machines that solved problems based on formulated representations of real-world scenarios, much like humans leveraged their knowledge and experience.

Herbert Simon and Allen Newell‘s General Problem Solver program developed at RAND Corporation and later Carnegie Mellon University in 1959, demonstrated elementary problem formulation, breaking down goals, object representations and planning abilities.

In the 1970s and 80s, the advent of expert systems encoding domain knowledge provided by human specialists, allowed advanced automated reasoning over specific topics like medical diagnosis and mineral exploration. Prominent systems like MYCIN and DENDRAL paved the way for modern knowledge representation techniques.

Types of Knowledge

Let‘s survey the spectrum of knowledge that AI agents need to work with:

  • Declarative Knowledge – Explicit facts about objects, people, events and concepts relationships that help describe the environing world
  • Procedural Knowledge – Implicit skills, techniques, strategies and processes for sequencing multi-step actions and solving problems
  • Heuristic Knowledge – Guiding rules accumulated from past solving specific types of problems
  • Structural Knowledge – Understanding of taxonomic hierarchies and relationships between higher and lower level concepts

For instance, declarative knowledge would capture that "Ottawa is the capital of Canada" while procedural knowledge can encode the steps to plan a trip from Toronto to Ottawa.

Approaches for Representation

Let‘s explore some popular techniques to represent knowledge in AI systems:

Logical Representations

Facts and rules are encoded using rigorous formal logic with precisely defined syntax and semantics grounded in mathematical logic. This allows systematic reasoning through deduction, abduction, induction.

Semantic Networks

A graph-based structure where nodes denote concepts/entities and arcs capture relationships between them. Allows inheritance of attributes along relational links. Intuitive for humans to model knowledge.

Production Rules

Rules encoded as modular IF-THEN conditional statements, where IF clause specifies a condition and THEN clause provides the action to be taken. Easy for domain experts to edit and maintain.

Frame-based Representation

A structured representation where knowledge is organized into skeletal frames consisting of different informational slots that are filled with specific values and attributes about the entity.

Design Considerations

Some key criteria for evaluation of knowledge representation schemes:

  • Expressiveness – Ability to model different types of knowledge
  • Inference capabilities – Support for automated reasoning mechanisms
  • Modularity – Extendable through incremental knowledge acquisition
  • Uncertainty handling – Representing and operating on probabilistic information

There are inherent tradeoffs between representational simplicity, reasoning efficiency and semantic completeness that system designers need to grapple with.

Real-World Applications

Let‘s look at some interesting applications powered by knowledge representation and reasoning:

Medical Expert Systems

Domain expertise from veteran doctors encoded as production rules and ontologies allows diagnosis systems to reason over patient symptoms and suggest appropriate treatments.

Intelligent Assistants

Assistants like Siri, Alexa and Google Assistant incorporate common sense knowledge and contextual understanding through representation schemes like Cyc, ConceptNet to enable conversational responses.

Fraud Detection

Graph-based representation of known fraud patterns and semantic networks encoding suspicious entities, relationships and activities allows robust detection models.

As AI solution permeate different aspects of society, smarter knowledge representation continues to be the crucial enabler behind their capabilities providing the contextual understanding and reasoning proficiency to work effectively.

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