Chapter 5_Knowledge Representation and Reasoning

Humans

• Are best at understanding, reasoning, and interpreting knowledge;

• knows things (knowledge) and, as per their knowledge, performs various actions in the real world.

Knowledge Representation and Reasoning (KR, KRR)

• The part of Artificial Intelligence concerned with AI agents thinking and how thinking contributes to intelligent behavior of agents.

• Responsible for representing information about the real world so that a computer can understand and utilize this knowledge to solve complex real-world problems.

• A way of describing how we can represent knowledge in AI;

• it is not just storing data into a database, but also enables an intelligent machine to learn from that knowledge and experience so it can behave intelligently like a human.

Agent

Anything that perceives its environment through sensors and acts upon that environment through actuators.

Human-Agent,

Robotic Agent,

Software Agent

Types of Agent (3)

Human-Agent

An agent that has eyes, ears, and other organs which work as sensors, and hands, legs, and vocal tract which work as actuators.

Robotic Agent

An agent that can have cameras, an infrared range finder, and NLP for sensors, and various motors for actuators.

Software Agent

An agent that can have keystrokes and file contents as sensory input, acting on those inputs and displaying output on the screen.

Object,

Events,

Performance,

Meta-knowledge,

Facts,

Knowledge Base

Kinds of Knowledge That Need to Be Represented in AI Systems (6)

Object

All the facts about objects in our world domain.

Events

The actions which occur in our world.

Performance

Describes behavior which involves knowledge about how to do things.

Meta-knowledge

Knowledge about what we know.

Facts

Truths about the real world and what we represent.

Knowledge Base (KB)

• The central component of knowledge-based agents;

• a group of sentences (here, "sentences" is a technical term, not identical to the English language).

Knowledge

Awareness or familiarity gained by experiences of facts, data, and situations.

Declarative Knowledge, Procedural Knowledge, Meta-knowledge, Heuristic Knowledge, Structural Knowledge

Types of Knowledge in Artificial Intelligence (5)

Declarative Knowledge

• Knowledge that is about knowing something;

• includes concepts, facts, and objects.

• Also called descriptive knowledge and expressed in declarative sentences.

• Simpler than procedural language.

Procedural Knowledge

• Also known as imperative knowledge;

• responsible for knowing how to do something and can be directly applied to any task.

• Depends on the task on which it can be applied.

• Includes rules, strategies, procedures, agendas, etc.

Meta-knowledge

Knowledge about the other types of knowledge.

Heuristic Knowledge

• Represents knowledge of some experts in a field or subject.

• Rules of thumb based on previous experiences and awareness of approaches, which are good to work with but not guaranteed.

Structural Knowledge

• Basic knowledge for problem-solving.

• Describes relationships between various concepts, such as kind of, part of, and grouping of something.

• Describes the relationship that exists between concepts or objects.

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AI Knowledge Cycle

The components an artificial intelligence system has for displaying intelligent behavior.

Perception,

Learning,

Knowledge Representation and Reasoning,

Planning,

Execution

Components of the AI Knowledge Cycle (5)

Simple Relational,

Inheritable,

Inferential,

Procedural

Approaches to Knowledge Representation (4)

Simple Relational Knowledge

• The simplest way of storing facts, using the relational method, where each fact about a set of objects is set out systematically in columns.

• Famous in database systems, where the relationship between different entities is represented.

• Has little opportunity for inference.

Inheritable Knowledge

• An approach where all data must be stored into a hierarchy of classes.

• All classes should be arranged in a generalized form or a hierarchical manner.

• Contains inheritable knowledge which shows a relation between instance and class, called the instance relation.

• Every individual frame can represent the collection of attributes and its value.

Instance relation

The relation between an instance and a class in Inheritable Knowledge.

Inferential Knowledge

• An approach that represents knowledge in the form of formal logic.

• Can be used to derive more facts.

• Guarantees correctness.

Procedural Knowledge (as a KR approach)

• An approach that uses small programs and codes which describe how to do specific things and how to proceed.

• One important rule used is the If-Then rule.

• Various coding languages such as LISP and Prolog can be used.

• Can easily represent heuristic or domain-specific knowledge.

• It is not necessary that we can represent all cases in this approach.

If-Then rule

The important rule used in the Procedural Knowledge approach.

LISP, Prolog

Coding Languages Usable in Procedural Knowledge (2)

Representational Accuracy,

Inferential Adequacy,

Inferential Efficiency,

Acquisitional Efficiency

Requirements (Properties) for a Knowledge Representation System (4)

Representational Accuracy

The KR system should have the ability to represent all kinds of required knowledge.

Inferential Adequacy

The KR system should have the ability to manipulate the representational structures to produce new knowledge corresponding to the existing structure.

Inferential Efficiency

The ability to direct the inferential knowledge mechanism into the most productive directions by storing appropriate guides.

Acquisitional Efficiency

The ability to acquire new knowledge easily using automatic methods.