- 61 Views
- Uploaded on
- Presentation posted in: General

Knowledge Engineering

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Knowledge Engineering

Second Term Fourth Year

- Data:
Data is unprocessed facts and figures without any added interpretation or analysis.

- Information:
Data that has been interpreted so that it has meaning for the user.

- Knowledge:
Knowledge is combination of information, experience and insight that may benefit the individual or the organization.

Data

Information

Knowledge

applied

for a purpose

build and

process

Definition: The art of bringing the principles and tools of AI research to solve complex problems using expert’s knowledge.

Sources of Knowledge

Knowledge

Representation

Knowledge

Validation

Knowledge

Base

Explanation, Justification

Inferencing

- Shallow Knowledge:
- Shallow knowledge refers to representation of only surface level information that can be used to deal with very specific situations.
- The shallow knowledge may be presented in terms of IF-THEN rules.
Example: If gasoline tank is empty car will not start

- The Shallow version represents input-output relationship of a system.
- It can be presented in terms of IF-THEN rules.
- Shallow knowledge may be insufficient in describing complex situations.

- Deep Knowledge:
- Deep knowledge refers to the internal and causal structure of a system and considers the interactions among the system’s components.
- Human problem solving is based on deep knowledge of a situation.
- Deep knowledge can be applied to different tasks and different situations.
- This knowledge is difficult to computerize.

- Declarative Knowledge:
- Declarative knowledge is a descriptive representation of knowledge.

- Procedural Knowledge:
- Procedural knowledge considers the manner in which things work under different set of circumstances.

- Semantic Knowledge:
- Semantic knowledge reflects cognitive structure that involves the use of the long-term memory.
- words and other symbols.
- word symbol meanings and usage rules.
- word symbol referents and interrelationships.
- algorithms for manipulating symbols, concepts and relations.

- Semantic knowledge reflects cognitive structure that involves the use of the long-term memory.

- Episodic Knowledge:
- Episodic knowledge is autobiographical, experimental information organized as a case or an episode. It is thought to reside in long term memory, usually classified by time and place.

- Metaknowledge:
- Metaknowledge means knowledge about knowledge. In AI, meta-knowledge refers to knowledge about the operation of knowledge based systems, that is, about its reasoning capabilities.

Assumption of (traditional) AI work is that:

- Knowledge may be represented as “symbol structures” (essentially, complex data structures) representing bits of knowledge (objects, concepts, facts, rules, strategies ...)
- E.g. “red”represents colour red.
- “Car1” represents my car.
- red(car1) represents fact that my car is red.

- Intelligent behaviour can be achieved through manipulation of symbol structures

- Knowledge representation languages have been designed to facilitate this.
- Rather than use general C++/Java data structures, use special purpose formalisms.
- A KR language should allow you to:
- represent adequately the knowledge you need for your problem (representational adequacy).
- do it in a clear, precise and natural ways.
- allow you to reason on that knowledge, drawing new conclusions.

- Representational Adequacy: the ability to represent all kinds of knowledge that are needed in that domain.
- Inferential Adequacy: the ability to manipulate the representational structures in such a way as to derive new structures corresponding to new knowledge inferred from old.
- Inferential Efficiency: the ability to incorporate into the knowledge structure additional information that can be used to focus the attention of the inference mechanisms in the most promising directions.
- Acquisitional Efficiency: the ability to acquire new information easily.

- Logic
- Frames/Semantic Networks/Objects
- Rule based systems

- A Logic is a formal language, with precisely defined syntax and semantics, which supports sound inference. Independent of domain of application.
- Different logics exist, which allow you to represent different kinds of things, and which allow more or less efficient inference.
- propositional logic, predicate logic, temporal logic, modal logic, description logic..

- But representing some thing in logic may not be very natural, and inferences may not be efficient. More specialized languages may be better..

- In general a logic is defined by
- syntax: what expressions are allowed in the language.
- Semantics: what they mean, in terms of a mapping to real world
- proof theory: how we can draw new conclusions from existing statements in the logic.

- Propositional logic is the simplest..

- The Propositional Logic allows facts about the world to be represented as sentences formed from:
- Propositional symbols: P, Q, R, S...
- And:
- Or:
- Not:
- Implies:
- If and only if:
- Wrapping parentheses: (...)
- logical constants: true, false, unknown

- `It is humid': Q
- `It is humid and hot': QP
- `If it is hot and humid, then it is raining':
(PQ) R

- ‘it is humid but not hot’: PQ

For Propositional Expressions P, Q and R:

- (P) P
- (PQ) (PQ)
- The Contrapositive Law: (PQ) (QP)
- de Morgan’s Laws: (a)(PQ) (PQ) and
(b)(PQ) (PQ)

- The Commutative Laws: (PQ) (QR) and
(PQ) (QR)

- The Associative Laws: ((PQ)R) (P(QR))
((PQ)R) (P(QR))

- The Distributive Laws: P(QR)) (PQ) (PR)
P(QR)) (PQ) (PR)

(i) Show that (PQ) (PQ)

Proof:

(ii) Show that (PQ) (PQ)

Proof:

- An expression that is always true (under any interpretation is called a tautology.
- If A is tautology, we write ╞ A
- A logical expression that is a tautology is often described as being VALID.
Examples: Ali lives in Karachi Ali lives in Pakistan, A man is mortal, a lion is an animal.

- If an expression is false in any interpretation, it is described as being CONTRADICTORY:
A¬ A

Example: I study in MUET I am not student of MUET.

- A sentence is SATISFIABLE, if and only if there is some interpretation in some world for which it is true.
- Some expressions are SATISFIABLE, but not valid. They are true under some interpretation but not under all interpretations.
- teacher not a student
- intelligent student position holder
- I live in Sindh I live in Karachi
A contradictory expression is clearly not satisfiable so is described as being UNSATISFIABLE.

Show that the following formula is valid:

((PH)H) P

Proof:

Since formula is true for all possible combinations of truth values, the formula is valid.

For each of the following sentences, decide whether it is valid, satisfiable, unsatisfiable or neither.

- Smoke Smoke
Answer: Valid

(b)Smoke Fire

Answer: Satisfiable

(c)(Smoke Fire)(Smoke Fire)

Answer: Satisfiable

(d)Smoke Fire Fire

Answer: Valid

(e)((Smoke heat)Fire) ((SmokeFire)(HeatFire)

Answer: Valid

(f)(Smoke Fire)((Smoke heat)Fire)

Answer: Valid

H.W.: show the validity of (d), (e) and (f) using truth tables.

- Inference is the process of deriving new sentences from old ones.
- Inference Rules are patterns of sound inference that can be used to find proofs.

- Let B can be derived from A by inference. This is written as
- Some Popular Inference Rules
(i) Modus Ponens or Implication Elimination:

If A is true and A=> B is true, then conclude B is true.

(ii) And Elimination: (From a conjuction, you can infer any of the conjuctants.

(iii) And Induction:(From a list of sentences, you can infer their conjuction)

(iv) Or-Induction:(From a sentence, you can infer its disjuction with anything else at all).

(v) Double-Negation Elimination: (From a doubly negated sentence, you can infer a positive sentence.)

(vi) Unit Resolution: (From a disjunction, if one of the disjuncts is false, then you can infer the other one is true.

(vii) Resolution:

or equivalently,

- In propositional calculus, each atomic sentence (P, Q etc.) denotes a proposition of some complexity. There is no way to access the components of an individual assertion. Predicate calculus provides this ability.
- Example:
In Propositional logic, we may write the entire sentence:

P = “It rained on Tuesday”

In Predicate Calculus, we may create a predicate weather that describes a relationship between a date/day and weather:

weather (Tuesday, rain)

Through inference rules, we can manipulate predicate calculus expressions, accessing their individual components and inferring new sentences.

- Predicate calculus allows expressions to contain variables.
- Variables let us create general assertions about classes of entities:
Example: we could state that for all values of X, where X is a day of the week, the statement

weather(X, rain)

is true; i.e. It rains everyday.