Introduction to Artificial Intelligence 2071

a. Medical Diagnosis System: Utility Based Agent

b. Satellite image analysis system: Goal Based Agent

c. Part-picking robot: Goal Based Agent

d. Refinery Controller: Utility Based Agent

a. BFS finds the solution as DFS and iterative deepening enter the infinite loop due to no repeated state checking.

b. Using BFS: A --> C

Learning is acquiring new or modifying existing knowledge, behaviors, skills and may involve synthesizing different types of information.

A Machine Learning Framework is an interface, library or tool which allows developers to more easily and quickly build machine learning models. Learning framework is required because it provides a clear, concise way for defining machine learning models using a collection of pre-built, optimized components. 

Learning Framework:

It consists of the following components:

• Learning element: This element is responsible for making improvements.
• Performance element: It is responsible for selecting external actions according to the percepts it takes.
• Critic: It provides feedback to the learning agent about how well the agent is doing, which could maximize the performance measure in the future.
• Problem Generator: It suggests actions which could lead to new and informative experiences.

Example:

Performance element: turning, accelerating, breaking are the performance elements on road.

Learning element: learn rules for breaking, accelerating, learn geography of the city.

Critic: quick right turn across three lanes of traffic, observes reaction of other drivers.

Problem generator: Try south city road.

Knowledge representation (KR) is the study of how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge. Knowledge Representation is the method used to encode knowledge in Intelligent Systems.

Approaches to knowledge representation

•  Rule-based

        – IF <condition> THEN <conclusion>

•  Object-based

        – Frames

        – Scripts

        –Semantic Networks

        – Object-Attribute-Value(O-A-V Triplets)

•  Example-based : Case-based Reasoning (CBR)

Rule-based approach:

Rule-based systems are used as a way to store and manipulate knowledge to interpret information in a useful way. In this approach, idea is to use production rules, sometimes called IF-THEN rules. The syntax structure is

                IF <premise> THEN <action>

<premise> - is Boolean. The AND, and to a lesser degree OR and NOT, logical connectives are possible.

<action> - a series of statements

E.g. ―If the patient has stiff neck, high fever and an headache, check for Brain Meningitis‖. Then it can be represented in rule based approach as:

IF <FEVER, OVER, 39> AND <NECK, STIFF, YES> AND <HEAD, PAIN, YES> THEN add(<PATIENT,DIAGNOSE, MENINGITIS>)

Object based approach:

Frames: With this approach, knowledge may be represented in a data structure called a frame. A frame is a data structure containing typical knowledge about a concept or object. Each frame has a name and slots. Slots are the properties of the entity that has the name, and they have values or pointer to other frames.

E.g. 

Semantic Networks: Knowledge is represented as a collection of concepts, represented by nodes. Thus, semantic networks are constructed using nodes linked by directional lines called arcs. For e.g.

etc.

Let food = F, party = P, drinks = D

A sentence that is expressed as a disjunction of conjuction of literals is said to be in disjunctive normal form (DNF). E.g. (P∧Q)∨(P∧R)

DNF conversion steps

1. Eliminate ↔ rewriting P↔Q as (P→Q)∧(Q→P)

2. Eliminate → rewriting P→Q as ¬P∨Q

3. Use De Morgan‘s laws to push ¬ inwards:

        - rewrite ¬(P∧Q) as ¬P∨¬Q

        - rewrite ¬(P∨Q) as ¬P∧¬Q

4. Eliminate double negations: rewrite ¬¬P as P

5. Use the distributive laws to get DNF:

        - rewrite P∧(Q∨R) as (P∧Q)∧(P∧R)

6. Use:

        - (P∧Q) ∧ R as P∧Q ∧ R

        - (P∨Q)∨R as P∨Q∨R

Symbolic AI: 

• Symbolic AI refers to the fact that all steps are based on symbolic human readable representations of the problem that use logic and search to solve problem.
• Key advantage of Symbolic AI is that the reasoning process can be easily understood – a Symbolic AI program can easily explain why a certain conclusion is reached and what the reasoning steps had been.
• A key disadvantage of Symbolic AI is that for learning process – the rules and knowledge has to be hand coded which is a hard problem.

Non-symbolic AI:

• Non-symbolic AI systems do not manipulate a symbolic representation to find solutions to problems. Instead, they perform calculations according to some principles that have demonstrated to be able to solve problems without exactly understanding how to arrive at the solution.
• Examples of Non-symbolic AI include genetic algorithms, neural networks and deep learning.
• A key disadvantage of Non-symbolic AI is that it is difficult to understand how the system came to a conclusion. This is particularly important when applied to critical applications such as self-driving cars, medical diagnosis among others

Semantic network of given knowledge:

Natural language processing is a technology which involves converting spoken or written human language into a form which can be processed by computers, and vice versa. NLP is composed of two part: NLU (Natural Language Understanding) and NLG (Natural Language generation).

Steps of Natural Language Processing (NLP)

Input/Source

• The input of a NLP system can be written text or speech.

• Quality of input decides the possible errors in language processing that is high quality input leads to correct language understanding.

Segmentation

• The text inputs are divided in segments (Chunks) and the segments are analyzed. Each such chunk is called frames.

Syntactic Analysis

• Syntactic analysis takes an input sentence and produces a representation of its grammatical structure.
• A grammar describes the valid parts of speech of a language and how to combine them into phrases.
• The grammar of English is nearly context free.

Grammar: A computer grammar specifies which sentences are in a language and their parse trees. A parse tree is a hierarchical structure that shows how the grammar applies to the input. Each level of the tree corresponds to the application of one grammar rule.

Parse Tree:

Semantic Analysis

• Semantic analysis is a process of converting the syntactic representations into a meaning representation.
• This involves the following tasks:

                    - Word sense determination

                    -  Sentence level analysis

Word sense: Words have different meanings in different contexts.

Example:Mary had a bat in her office.

        bat = ``a baseball thing’

        bat = ``a flying mammal’

Sentence Level Meaning:

Once the words are understood, the sentence must be assigned some meaning

Examples:

• She saw her duck.
• I saw a man with a telescope.

Non-examples: Colorless green ideas sleep furiously - >This would be rejected semantically as colorless green would make no sense.

Pragmatic Analysis

• Pragmatics comprises aspects of meaning that depend upon the context or upon facts about real world.
• These aspects include:

                - Pronouns and referring expressions.

                - Logical inferences, that can be drawn from the meanings of a set of propositions.

                - Discourse structure: the meaning of a collection of sentences taken together.

Examples:

Jack fell. Jill brought him a band-aid.

        Jack got hurt and Jill wanted to help.

We got seven letters today

        We got only seven letters (and not eight).