Artificial Intelligence - Unit Wise Questions

The Turing test, proposed by Alan Turing  was designed to convince the people that whether a particular machine can think or not. The test involves an interrogator who interacts with one human and one machine. Within a given time the interrogator has to find out which of the two the human is, and which one the machine.

To pass a Turing test, a computer must have following capabilities:

• Natural Language Processing: To communicate easily.
• Knowledge Representation: To store facts and rules.
• Automated Reasoning: To draw conclusion from stored knowledge.
• Machine Learning: To adopt new circumstances and detect pattern.

Additional requirements for the “total Turing test”: computer vision, speech recognition, speech synthesis, robotics.

Critics of Turing test:

• Test is not reproducible, amenable or constructive to mathematical analysis as it is more important to study the underlined principles of intelligence than to duplicate example.

• Trying to evaluate machine intelligence in terms of human intelligence is fundamental mistake. It focuses too much on the behavior of conversation.

Thinking humanly and Thinking rationally are concerned with thought process and reasoning.

Thinking Humanly

Defⁿ: The exciting new effort to make computers think machines with minds, in the full and literal sense.

If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. We need to get inside the actual workings of human minds. There are two ways to do this: through introspection--trying to catch our own thoughts as they go by--or through psychological experiments. Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a computer program. If the program's input/output and timing behavior matches human behavior, that is evidence that some of the program's mechanisms may also be operating in humans. 

Thinking Rationally

Defⁿ: The study of mental faculties through the use of computational models.

The laws of thought are supposed to implement operation of the mind and their study initiated the field called logic. It provides precise notations to express facts of the real world.

It also includes reasoning and "right thinking" that is irrefutable thinking process. Also computer program based on those logic notations were developed to create intelligent system.

Artificial Intelligence (AI) is the part of computer science concerned with designing intelligence computer systems i.e. systems that exihibit the characteristics we associate with intelligence in human behaviour.

The behaviours of AI are as follows:

1. Learn from experience and apply knowledge acquired from experience

2. Handle Complex Situations

3. Solve problems with important information is missing: Decision must be made even when we lack information or have inaccurate information.

4. Determine what is important

5. React quickly and correctly to a new situation

6. Understand visual images

7. Process and manipulate symbols

8. Be creative and imaginative

9. Use heuristics

Turing Test

The Turing test, proposed by Alan Turing  was designed to convince the people that whether a particular machine can think or not. The test involves an interrogator who interacts with one human and one machine. Within a given time the interrogator has to find out which of the two the human is, and which one the machine.

To pass a Turing test, a computer must have following capabilities:

• Natural Language Processing: To communicate easily.
• Knowledge Representation: To store facts and rules.
• Automated Reasoning: To draw conclusion from stored knowledge.
• Machine Learning: To adopt new circumstances and detect pattern.

Additional requirements for the “total Turing test”: computer vision, speech recognition, speech synthesis, robotics.

The Turing test, proposed by Alan Turing  was designed to convince the people that whether a particular machine can think or not. The test involves an interrogator who interacts with one human and one machine. Within a given time the interrogator has to find out which of the two the human is, and which one the machine.

To pass a Turing test, a computer must have following capabilities:

• Natural Language Processing: To communicate easily.
• Knowledge Representation: To store facts and rules.
• Automated Reasoning: To draw conclusion from stored knowledge.
• Machine Learning: To adopt new circumstances and detect pattern.

Additional requirements for the “total Turing test”: computer vision, speech recognition, speech synthesis, robotics.

Critics of Turing test:

• Test is not reproducible, amenable or constructive to mathematical analysis as it is more important to study the underlined principles of intelligence than to duplicate example.

• Trying to evaluate machine intelligence in terms of human intelligence is fundamental mistake. It focuses too much on the behavior of conversation.

 “System that think rationally” and “System that act rationally”  measure an ideal concept of intelligence, which is called rationality.

System that think rationally

Defⁿ: The study of mental faculties through the use of computational models.

The laws of thought are supposed to implement operation of the mind and their study initiated the field called logic. It provides precise notations to express facts of the real world.

It also includes reasoning and "right thinking" that is irrefutable thinking process. Also computer program based on those logic notations were developed to create intelligent system.

System that act rationally

Defⁿ: “Computational Intelligence is the study of design of intelligent agents”
E.g. ”Rational agent approach” , agent is the one who decide what to do and then perform action by receiving percepts from the environment.

Artificial intelligence is about designing systems that are as intelligent as humans. It involves trying to understand human thought and an effort to build machines that emulate the human thought process.

System that think like humans

Defⁿ: The exciting new effort to make computers think machines with minds, in the full and literal sense.

E.g. a program that think like humans, i.e. a cognitive modeling approach in which once we have sufficiently precise theory of mind , it become possible to express the theory as computer program.

System that act like humans

Defⁿ:  ”The art of creating machines that perform functions that require when performed by people” .
E.g. Turing Test approach: Which is based on the indistinguishability from undeniably intelligent entities.
           You enter a room which has a computer terminal. You have a fixed period of time to type what you want into the terminal, and study the replies. At the other end of the line is either a human being or a computer system.                   
           If it is a computer system, and at the end of the period you cannot reliably determine whether it is a system or a human, then the system is deemed to be intelligent.

A rational agent is an agent which has clear preferences and models uncertainity via expected value. It always performs right action, right action  means the action that causes the agent to be most successful in the given percept sequence.

Rational agent is capable of taking best possible action in any situation.

For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

The agents rational behavior depends upon:

• Performance measures
• Prior environment knowledge
• Actions
• Percept sequence upto now

A rational agent is an agent which has clear preferences and models uncertainity via expected value. It always performs right action, right action  means the action that causes the agent to be most successful in the given percept sequence.

Rational agent is capable of taking best possible action in any situation.

For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

Episodic task environment vs Sequential task environment

In Episodic environment, the agent's experience is divided into atomic 'episodes' (each episode consists of the agent perceiving then performing a single action i.e. agent's single pair of perception & action) and every episode is independent of each other. The subsequent episodes do not depend on actions occured in previous episodes. For e.g. an agent sorting defective part in an assembly line, pick and place robot agent.

In Sequential environment, current actions may affect all future decisions. In sequential environment, an agent requires memory of past action to determine the next best action. For e.g. a taxi driving agent or Chess playing.

PEAS Framework for:

Soccer Playing Agent

Performance Measure (P): To Play, Make Goal & Win the Game.

Environment (E): Soccer, Team Members, Opponents, Referee, Audience and Soccer Field.

Actuators (A): Navigator, Legs of Robot, View Detector for Robot.

Sensors (S): Camera, Communicators and Orientation & Touch Sensors.

Internet Shopping Assistant

Performance measure: price, quality, appropriateness, efficiency

Environment: current and future WWW sites, vendors, shippers

Actuators: display to user, follow URL, fill in form

Sensors: HTML pages (text, graphics, scripts)

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. 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

When we have a decision and based on the decision if we fetch the initial dat that supports goal then the process is called as backward chaining. Backward chaining starts with a goal and then searches back through inference rules to find the facts that support the goal.

E.g. "If it is raining then we will take umbrella". Here we have our possible conclusion "we will take umbrella". If we are taking umbrella then it can be stated that " it is raining". Here based on conclusion we guessed that the data can be "it is raining". This process is called backward chaining.

• The system that uses backward chaining tries to set goals in order which they arrive in the knowledge base.
• While searching, the backward chaining considers those parts of knowledge base which are directly related to the considered problem or backward chaining never performs unnecessary inferences.
• Backward chaining is an excellent tool for specific type of problem such as diagnosing & debugging.

Here, when search reaches at node C it stucks in loop. So we can't reach at goal node. Therefore, Greedy Best First Search is not complete for the given state space.

When we have some data and we make a decision based on this data then the process is called as forward chaining. Forward chaining starts with the available data and uses inference rules to extract more data until a goal is reached.

Forward chaining has the capability of providing a lot of data from the available few initial data or facts.

Forward chaining is a very popular technique for implementation to expert system, and system using production rules in the knowledge base. For expert system that needs interruption, control, monitoring and planning, the forward chaining is the best.

Examples:

1. While diagnosing a patient the doctor first check the symptoms and medical condition of the body such as temperature, blood, pressure, pulse, blood etc. After that, the patient symptoms are analysed and compared against the predetermined symptoms. Then the doctor is able to provide the medicine according to the symptoms of the patient.

2. "if it is raining then we will take umbrella". Here "it is raining" is data and "we will take umbrella" is a decision. It was alrready known that it is raining that is why we are going to take umbrella. This is forward chaining.

Uninformed (Blind) Search does not use any domain knowledge. This means it does not any information to judge where the solution is likely to lie. Uninformed search methods use only the information available in the problem definition.

For E.g.

Breadth First Search

It expands the shallowest unexpanded node first. Starting from the root node (initial state) explores all children of the root node, left to right. If no solution is found, expands the first (leftmost) child of the root node, then expands the second node at depth 1 and so on …until a solution is found.

E.g.

The state space is commonly defined as a directed graph in which each node is a state and each arc represents the application of an operator transforming a state to a successor state. A solution is a path from the initial state to a goal state.

State Space representation of Vacuum World Problem:

States: two locations with or without dirt: = 8 states.

Initial state:  Any state can be initial

Actions:  {Left, Right, Suck}

Goal test: Check whether squares are clean.

Path cost:  Number of actions to reach goal.

 Representing the root finding problem having four cities in to state representation:

The above problem can be formulate as:

States: All four cities. {Oradea, Zerind, Sibiu, Arad}

Initial State:  Current city where we are. For e.g. Oradea

Actions: Drive between cities or choose next city.

Goal test: Check whether the agent is in Arad and 4 cities have been visited.

Path Cost: Sum of distances.

    A solution is a sequence of actions leading from the initial state to a goal state

In goal formulation, we decide which aspects we are interested in and which aspects can be ignored. In the goal formulation process, the goal is to be set and we should assess those states in which the goal is satisfied. In problem formulation, we decide how to manipulate the important aspects, and ignore the others. So, without doing goal formulation, if we do the problem formulation, we would not know what to include in our problem and what to leave, and what should be achieved. So problem formulation must follow goal formulation. That means problem formulation must be done only after the goal formation is done.

In the state space representation of a problem, nodes of a graph correspond to partial problem solution states and arcs represent steps in a problem- solving process. An initial state, corresponding to the given information in a problem instance, forms the root of the graph. The graph also defines a goal condition, which is the solution to a problem instance. State space search characterizes problem solving as the process of finding a solution path from the start state to a goal state. Arcs of the state space correspond to steps in a solution process and path through the space represent solutions in varying stages of completion. Paths are searched, beginning at the start state and continuing through the graph until either the goal description is satisfied or they are abandoned.

E.g.

State Space representation of Vacuum World Problem:

States: two locations with or without dirt: = 8 states.

Initial state:  Any state can be initial

Actions:  {Left, Right, Suck}

Goal test: Check whether squares are clean.

Path cost:  Number of actions to reach goal.

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

AI problems can be readily modeled as state spaces, where we want to find the best possible solution, that successfully solves a particular task, among all the available candidate solutions in the solution space. Using different searching algorithms we can find the best possible solution. So searching is important in AI.

Breadth First Search

It expands the shallowest unexpanded node first. Starting from the root node (initial state) explores all children of the root node, left to right. If no solution is found, expands the first (leftmost) child of the root node, then expands the second node at depth 1 and so on …until a solution is found.

E.g.

Depth First Search

It expands the deepest unexpanded node first.

It expands the root node, then the leftmost child of the root node, then the left most child of that node and so on. Only when the search hits dead end does the search backtrack.

E.g.

Uniform cost searching algorithm is used for weighted state space. In this algorithm,

• Priority queue for storing nodes in state space is maintained, where least cost paths are given higher priority.
• Node at head of the queue is expanded first.
• The queue is updated at each expansion of nodes (deletion of node visited & insertion of node to be visited).

Given state space:

The information which is used to search the space more efficiently is called heuristic information.

Ways of using heuristic information:

• Deciding which node to expand next, instead of doing the expansion in a strictly breadth-first or depth-first order;

• In the course of expanding a node, deciding which successor or successors to generate, instead of blindly generating all possible successors at one time;

• Deciding that certain nodes should be discarded, or pruned, from the search space.

The function g(n) gives the cost of the path from initial state to the node n. Using h(n) = -g(n) for the heuristic function in a greedy search, then, will cause the algorithm to always select the node with the highest path cost so far (the largest g(n)) to expand next, since this will give us the smallest h(n) (i.e. the most negative value). If all operations have the same cost value associated with them, then the largest g(n) will always correspond to the longest path in the search tree and the greedy search will emulate depth-first search.

If we set h(n) = g(n) we get breadth first search.

A heuristic function is said to be admissible if it is no more than the lowest-cost path to the goal. In other words, a heuristic is admissible if it never overestimates the cost of reaching the goal.

In greedy search, the evaluation function is defined by    f(n) = h(n)

               Where, h(n) is an estimate of cost from node n to goal node.

But in A* search, the evaluation function is defined by  f(n) = g(n) + h(n) 

            Where, g(n) is sum of actual costs incurred while travelling from root node (start node) to node n.

                         h(n) is an estimate of cost from node n to goal node

                         f(n) is estimated total cost of path through n to goal.

A* search gives us optimal solution if the heuristic function is admissible.

Proof:

AI problems can be readily modeled as state spaces, where we want to find the best possible solution, that successfully solves a particular task, among all the available candidate solutions in the solution space. Using different searching algorithms we can find the best possible solution. So searching is important in AI.

Blind Search does not use any domain knowledge. This means it does not any information to judge where the solution is likely to lie. E.g. Breadth first search, Depth first search, Uniform cost search etc.

Breadth First Search

It expands the shallowest unexpanded node first. Starting from the root node (initial state) explores all children of the root node, left to right. If no solution is found, expands the first (leftmost) child of the root node, then expands the second node at depth 1 and so on …until a solution is found.

E.g.

Depth First Search

It expands the deepest unexpanded node first.

It expands the root node, then the leftmost child of the root node, then the left most child of that node and so on. Only when the search hits dead end does the search backtrack.

E.g.

Blind search is extended to informed search by using domain-dependent (heuristic) information in order to search the space more efficiently. It uses the heuristic function h(n) that estimates how close we are to a goal. 

AI problems can be readily modeled as state spaces, where we want to find the best possible solution, that successfully solves a particular task, among all the available candidate solutions in the solution space. Using different searching algorithms we can find the best possible solution. So searching is important in AI.

Breadth First Search

It expands the shallowest unexpanded node first. Starting from the root node (initial state) explores all children of the root node, left to right. If no solution is found, expands the first (leftmost) child of the root node, then expands the second node at depth 1 and so on …until a solution is found.

E.g.

Depth First Search

It expands the deepest unexpanded node first.

It expands the root node, then the leftmost child of the root node, then the left most child of that node and so on. Only when the search hits dead end does the search backtrack.

E.g.

Alpha Beta pruning is needed to eliminate unnecessary nodes from state space. It has two values alpha and beta.

Alpha is the best (i.e. maximum) value found so far at any choice point along the path for MAX.

Beta is the best (i.e. minimum) value found so far at any choice point along the path for MIN.

AI problems can be readily modeled as state spaces, where we want to find the best possible solution, that successfully solves a particular task, among all the available candidate solutions in the solution space. Using different searching algorithms we can find the best possible solution. So searching is important in AI.

Informed search uses domain-dependent (heuristic) information in order to search the space more efficiently. It uses the heuristic function h(n) that estimates how close we are to a goal. The function is used to estimate the cost from  a state n to the closest goal.

Types of Informed Search:

• Greedy Best-first Search
• A* Search

In above given search, nodes are expanded based on the value of evaluation function. Nodes having minimal value of evaluation function are expanded first.

Greedy Best-first Search

• It expands the node that appears to be closest to the goal.
• The evaluation function is defined by    f(n) = h(n)

                Where, h(n) is an estimate of cost from node n to goal node

                            h(n) = 0 for goal node.

A* Search

The evaluation function is defined by    f(n) = g(n) + h(n) 

            Where, g(n) is sum of actual costs incurred while travelling from root node (start node) to node n.

                         h(n) is an estimate of cost from node n to goal node

                         f(n) is estimated total cost of path through n to goal.

Baye's theorem is a way to apply conditional probability for prediction. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events.

Mathematically, Baye's theorem is defined as:

Bayes theorem provides a way to resolve existing predictions or theories (update probabilities) given new or additional evidence. This, in turn, makes the prediction more accurate.

Applications of Bayes theorem:

1.  Medical science: Baye's rule is used for predicting a particular disease based on the symptoms and physical condition of the patient.

2. Weather forcasting: Baye's rule is a powerful algorithm for predictive modeling weather forcast.

3. Robotics: Baye's rule is used to calculate the probability of a robot's next steps given the steps the robot has already executed.

4. Finance: Baye's theorem can be used to rate the risk of lending money to potential borrowers.

    etc.

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

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

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

CNF 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 CNF:

        - 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

E.g. Converting to CNF

Inference: Inference is a process by which new sentences are derived from existing sentences in KB. E.g. Modus tollens is a rule of inference which derives new knowledge.

Reasoning: Reasoning is the act of deriving a conclusion from certain premise using given methodology.

 Probabilistic reasoning is using logic and probability to handle uncertain situations. The aim of probabilistic reasoning is to combine the capacity of probability theory to handle uncertainity with the capacity of deductive logic to exploit structure of formal argument.

Probabilistic reasoning is used in AI:

• When we are unsure of the predicates
• When the possibilities of predicates become too large to list down
• When it is known that an error occurs during an experiment

One has to apply probabilistic reasoning in deciding about the next card to play in a game of cards or in diagnosing the illness from the symptoms.

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.

Inference: Inference is a process by which new sentences are derived from existing sentences in KB. E.g. Modus tollens is a rule of inference which derives new knowledge.

Reasoning: Reasoning is the act of deriving a conclusion from certain premise using given methodology.

 Probabilistic reasoning is using logic and probability to handle uncertain situations. The aim of probabilistic reasoning is to combine the capacity of probability theory to handle uncertainity with the capacity of deductive logic to exploit structure of formal argument.

Probabilistic reasoning is used in AI:

• When we are unsure of the predicates
• When the possibilities of predicates become too large to list down
• When it is known that an error occurs during an experiment

One has to apply probabilistic reasoning in deciding about the next card to play in a game of cards or in diagnosing the illness from the symptoms.

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

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.

Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing (i.e. drawing conclusions) from knowledge. A successful representation of some knowledge must, then, be in a form that is understandable by humans, and must cause the system using the knowledge to behave as if it knows it.

Semantic Network Representation

Semantic networks can

    - show natural relationships between objects/concepts

    - be used to represent declarative/descriptive knowledge

Knowledge is represented as a collection of concepts, represented by nodes. Thus, semantic networks are constructed using nodes linked by directional lines called arcs

A node can represent a fact description

    - physical object

    - concept

    - event

An arc (or link) represents relationships between nodes. There are some 'standard' relationship types

    - 'Is-a' (instance relationship): represent class/instance relationships

    - 'Has-a' (part-subpart relationship): identify property relationships

Example:

Causal Networks / Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Causal Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47

Applications of Causal / Bayesian Network:

1. Spam Filter: You must be lying if you say that you’ve never wondered how Gmail filters spam emails (unwanted and unsolicited emails. It uses Bayesian spam filter, which is the most robust filter.

2. Turbo Code: Bayesian Networks are used to create turbo codes that are high-performance forward error correction codes. These are used in 3G and 4G mobile networks.

3. Image Processing: Bayesian Networks use mathematical operations to convert images into digital format. It also allows image enhancement. 

4. Biomonitoring: Quantifying the concentration of chemicals couldn’t get any easier than with Bayesian Networks. In this, the amount of blood and tissue in humans is measured using indicators.

5. Gene Regulatory Network (GNR): A GNR contains various DNA segments of a cell that interact with other cell contents through protein and RNA expression products. The predictions of its behavior can be analyzed using Bayesian Networks.

Knowledge Representation is an area of AI whose fundamental goal is to represent knowledge in manner that facilitates inference i.e. drawing conclusion for knowledge. It analyzes how to think formally, how to use symbol to represent a domain of discourse along with function that allow inference about the objects.

Characteristics of knowledge representation:

i. Represential Adequacy:

    It is the ability to represent all kind of knowledge that are needed in the domain.

ii. Inferential Adequacy:

    It is the ability to manipulate the represential structure in such a way as to derive new structures corresponding to new knowledge inferred from old.

iii. Inferential Efficiency:

    It is the ability to incorporate into the knowledge structure additional information that can be used to focus the attention of the inference mechanism in the most efficient directions.

v. Acquisitional Efficiency:

    It is the ability to acquire new information easily.

Belief network / Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47

Inference with Belief / Bayesian Network:

        Using a Bayesian network to compute probabilities is called inference in Bayesian network.

The first task is to compute the posterior probability distribution for the query variable X, given some assignment of values e to the set of evidence variable E = E₁,......., E_n and the hidden variables are Y = Y₁,.....Y_n. From the full joint probability distribution we can answer the query P(X/e) by computing

        P(x/e) = αP(X, e) = αΣ_Y(X, e, Y)

A Bayesian network gives a complete representation of the full joint distribution, specifically, the terms P(X, e, Y) can be written as products of conditional probabilities from the network.

        Therefore, a query can be answered using a Bayesian network by computing sums of products of conditional probabilities from the network.

Causal Networks / Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Causal Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47

Applications of Causal / Bayesian Network:

1. Spam Filter: You must be lying if you say that you’ve never wondered how Gmail filters spam emails (unwanted and unsolicited emails. It uses Bayesian spam filter, which is the most robust filter.

2. Turbo Code: Bayesian Networks are used to create turbo codes that are high-performance forward error correction codes. These are used in 3G and 4G mobile networks.

3. Image Processing: Bayesian Networks use mathematical operations to convert images into digital format. It also allows image enhancement. 

4. Biomonitoring: Quantifying the concentration of chemicals couldn’t get any easier than with Bayesian Networks. In this, the amount of blood and tissue in humans is measured using indicators.

5. Gene Regulatory Network (GNR): A GNR contains various DNA segments of a cell that interact with other cell contents through protein and RNA expression products. The predictions of its behavior can be analyzed using Bayesian Networks.

Baye's theorem is a way to apply conditional probability for prediction. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events.

Mathematically, Baye's theorem is defined as:

Bayes theorem provides a way to resolve existing predictions or theories (update probabilities) given new or additional evidence. This, in turn, makes the prediction more accurate.

Applications of Bayes theorem:

1.  Medical science: Baye's rule is used for predicting a particular disease based on the symptoms and physical condition of the patient.

2. Weather forcasting: Baye's rule is a powerful algorithm for predictive modeling weather forcast.

3. Robotics: Baye's rule is used to calculate the probability of a robot's next steps given the steps the robot has already executed.

4. Finance: Baye's theorem can be used to rate the risk of lending money to potential borrowers.

    etc.

Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47

Inference with Bayesian Network:

        Using a Bayesian network to compute probabilities is called inference in Bayesian network.

The first task is to compute the posterior probability distribution for the query variable X, given some assignment of values e to the set of evidence variable E = E₁,......., E_n and the hidden variables are Y = Y₁,.....Y_n. From the full joint probability distribution we can answer the query P(X/e) by computing

        P(x/e) = αP(X, e) = αΣ_Y(X, e, Y)

A Bayesian network gives a complete representation of the full joint distribution, specifically, the terms P(X, e, Y) can be written as products of conditional probabilities from the network.

        Therefore, a query can be answered using a Bayesian network by computing sums of products of conditional probabilities from the network.

Model Based System

• A model-based system is based on a model of the structure and behavior of the device that the system is designed to simulate.
• It is used for well structured problems. E.g. Engineering problems: Diagnosing hardware or  a machine, Automobile diagnostics
• It is based on written document.
• Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do).

Case Based System

• A case-based system is a collection of a set of cases. It stores a case in the Case Base.
• It is based on human information processing (HIP) model in some problem areas: Thinking about how human processes information, Try to remember previous case/recall similar cases & modify to fit a new situation.
• E.g. Law, diagnosis, strategic planning
• It retrieves cases relevant to the present problem situation from the case base and decides on the solution to the current problem on the basis of the outcomes from the previous cases.

a. Electronic Circuit Testing --> Model based system is suitable.

b. Legal Reasoning --> Case based system is suitable.

c. Disease Recognition --> Case based system is suitable.

Conceptual graph is a graph representation for logic based on the semantic networks and the existential graphs of Charles sanders Peirce. 

A conceptual graph consists of concept nodes and relation nodes.

• The concept node represents entities, attributes, states and events.
• The relation node show how the concepts are interrelated.

Conceptual graph for given sentence:

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

The predicate in given KB are:

Conceptual graph is a graph representation for logic based on the semantic networks and the existential graphs of Charles sanders Peirce. 

A conceptual graph consists of concept nodes and relation nodes.

• The concept node represents entities, attributes, states and events.
• The relation node show how the concepts are interrelated.

Conceptual graph for given sentence:

Distribution over Y can be obtained by summing out all the other variables from any joint distribution containing Y. This process is called marginalization.

    P(Y) =ΣP(Y, Z)

Z --> No. of variables where Y seems to be true.

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:

Baye's theorem is a way to apply conditional probability for prediction. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events.

Mathematically, Baye's theorem is defined as:

Proof:

Bayes theorem provides a way to resolve existing predictions or theories (update probabilities) given new or additional evidence. This, in turn, makes the prediction more accurate.

Applications of Bayes theorem:

1.  Medical science: Baye's rule is used for predicting a particular disease based on the symptoms and physical condition of the patient.

2. Weather forcasting: Baye's rule is a powerful algorithm for predictive modeling weather forcast.

3. Robotics: Baye's rule is used to calculate the probability of a robot's next steps given the steps the robot has already executed.

4. Finance: Baye's theorem can be used to rate the risk of lending money to potential borrowers.

    etc.

Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47

Inference with Bayesian Network:

        Using a Bayesian network to compute probabilities is called inference in Bayesian network.

The first task is to compute the posterior probability distribution for the query variable X, given some assignment of values e to the set of evidence variable E = E₁,......., E_n and the hidden variables are Y = Y₁,.....Y_n. From the full joint probability distribution we can answer the query P(X/e) by computing

        P(x/e) = αP(X, e) = αΣ_Y(X, e, Y)

A Bayesian network gives a complete representation of the full joint distribution, specifically, the terms P(X, e, Y) can be written as products of conditional probabilities from the network.

        Therefore, a query can be answered using a Bayesian network by computing sums of products of conditional probabilities from the network.

Model Based System

• A model-based system is based on a model of the structure and behavior of the device that the system is designed to simulate.
• It is used for well structured problems. E.g. Engineering problems: Diagnosing hardware or  a machine, Automobile diagnostics
• It is based on written document.
• Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do).

Case Based System

• A case-based system is a collection of a set of cases. It stores a case in the Case Base.
• It is based on human information processing (HIP) model in some problem areas: Thinking about how human processes information, Try to remember previous case/recall similar cases & modify to fit a new situation.
• E.g. Law, diagnosis, strategic planning
• It retrieves cases relevant to the present problem situation from the case base and decides on the solution to the current problem on the basis of the outcomes from the previous cases.

a. Electronic Circuit Testing --> Model based system is suitable.

b. Legal Reasoning --> Case based system is suitable.

Facts in uncertain knowledge are represented using Bayesian network. Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

• Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
• Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
• The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
• Each nodes are labelled with probability.

Bayesian network for given statement:

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.

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.

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

We require machine learning because of the following reasons:

• Huge computational power is available
• To understand and improve efficiency of human learning.
• To discover new things or structure that is unknown to humans.
• To fill in sketal or incomplete specification about a domain.

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.

The most specific hypotheses (i.e., the specific boundary SB) are the hypotheses that cover observed positive training examples, and as little of the remaining feature space as possible. These are hypotheses which if reduced any further would exclude a positive training example, and hence become inconsistent. These minimal hypotheses essentially constitute a (pessimistic) claim that the true concept is defined just by the positive data already observed: Thus, if a novel (never-before-seen) data point is observed, it should be assumed to be negative. (I.e., if data has not previously been ruled in, then it's ruled out.)

The most general hypotheses (i.e., the general boundary GB) are those which cover the observed positive training examples, but also cover as much of the remaining feature space without including any negative training examples. These are hypotheses which if enlarged any further would include a negative training example, and hence become inconsistent.

Candidate Elimination Algorithm is used to construct generalization specialization tree.

• Nodes in the generalization tree are connected to a model that matches everything in its subtree.
• Nodes in the specialization tree are connected to a model that matches only one thing in its subtree.

Method:

• Neural Networks are networks of neurons, for example, as found in real (i.e. biological) brains.
• Artificial neurons are crude approximations of the neurons found in real brains. They may be physical devices, or purely mathematical constructs.
• Artificial Neural Networks (ANNs) are networks of Artificial Neurons and hence constitute crude approximations to parts of real brains. They maybe physical devices, or simulated on conventional.
• Computers point of view, an ANN is just a parallel computational system consisting of many simple processing elements connected together in a specific way in order to perform a particular task.

An artificial neural network is a composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problem. ANN has a number of input channels, a processing stages and output.

Feed-forward Neural Network

Feed-forward ANNs allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition.

Mathematical model of neural Network

Type of Neural Network: Feed-forward Neural Network

Feed-forward ANNs allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition.

Backpropagation is a supervised learning method, and is an implementation of the Delta rule. It requires a teacher that can calculate the desired output for any given input.

Steps / Algorithms

Example

After updating weights, we recompute y. Then we compute δ=t-y. If the value of y is converges to t then the values is fixed up otherwise we will iterate again.

Machine vision is the ability of a computer to "see." A machine-vision system employs one or more video cameras, analog-to-digital conversion ( ADC ), and digital signal processing ( DSP ). The resulting data goes to a computer or robot controller. Machine vision is similar in complexity to voice recognition . The machine vision systems use video cameras, robots or other devices, and computers to visually analyze an operation or activity. Typical uses include automated inspection, optical character recognition and other non-contact applications.

Two important specifications in any vision system are the sensitivity and the resolution. Sensitivity is the ability of a machine to see in dim light, or to detect weak impulses at invisible wavelengths. Resolution is the extent to which a machine can differentiate between objects. In general, the better the resolution, the more confined the field of vision.

A typical machine vision system will consist of most of the following components:

• One or more digital or analogue cameras (black-and-white or colour) with suitable optics for acquiring images, such as lenses to focus the desired field of view onto the image sensor and suitable, often very specialized, light sources
• Input/Output hardware (e.g. digital I/O) or communication links (e.g. network connection or RS-232) to report results
• A synchronizing sensor for part detection (often an optical or magnetic sensor) to trigger image acquisition and processing and some form of actuators to sort, route or reject defective parts
• A program to process images and detect relevant features.

Machine learning is a branch of AI that provides computer the ability to learn and improve its learning from experience without being written rules explicitly. It focuses on prediction based on known properties learned from the training data.

Learning by Analogy

• Learning by analogy means acquiring new knowledge about an input entity by transferring it from a known similar entity.

• This technique transforms the solutions of problems in one domain to the solutions of the problems in another domain by discovering analogous states and operators in the two domains.

E.g.  Infer by analogy the hydraulics laws that are similar to Kirchhoff's laws.

Instance Based Learning

• Instance based learning is a supervised classification learning algorithm that performs operation after comparing the current instances with the previously trained instances, which have been stored in memory.
• Time complexity of Instance based learning algorithm depends upon the size of training data. Time complexity of this algorithm in worst case is O (n), where n is the number of training items to be used to classify a single new instance.
• To improve the efficiency of instance based learning approach, preprocessing phase is required. Preprocessing phase is a data structure that enables efficient usage of run time modeling of test instance.
• Advantage of using Instance based learning over others is that it has the ability to adapt to previously unseen data, which means that one can store a new instance or drop the old instance.

An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.

We can construct expert system using given steps as below:

Knowledge Engineering

Knowledge engineering is a field of AI that creates rules to apply data in order to initiate the thought process of human expert. Knowledge engineering attempts to take challenges and solve problems that would usually require a high level of human expertise to solve.

In general, knowledge engineering is the process of understanding and representing a human knowledge in a computer as a program. Knowledge engineering includes:

1) Knowledge acquisition

2) Knowledge representation

3) Knowledge validation

4) Inferencing

5) Explanation and justification

            The interaction between these stages and source of knowledge is shown in the figure below:

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 parts: NLU and NLG.

Issues involved in the natural language processing are given below:

1. The same expression means different things in different context.

·         Where’s the water? ( Chemistry lab? Must be pure)

·         Where’s the water? ( Thirsty? Must be drinking water)

·         Where’s the water? ( Leaky roof? It can be dirty)

2. No natural language program can be complete because of new words, expression, and meaning can be generated quite freely.

·         I’ll fax it to you

3.  There are lots of ways to say the same thing.

·         Ram was born on October 11.

·         Ram’s birthday is October 11.

4.  Sentence and phrases might have hidden meanings

•      “Out of sight, out of mind”-> “ invisible idiot”

•      “The spirit was willing but the flesh was weak” - > “ the vodka was good, but the meat was bad”

5.  Problem due to syntax and semantics

6.  Problem due to extensive use of pronouns. (semantic issue)

•      Eg. Ravi went to the supermarket. He found his favorite brand of coffee in rack. He paid for it and left.

•      It denotes??

7.  Use of grammatically incorrect sentence

•      He rice eats. (syntax issue)

8.  Use of conjunctions to avoid repetition of phrases cause problem in NLP

•      Eg. Ram and Hari went to restaurant. While Ram had a cup of coffee, Hari had tea.

•      Hari had a cup of tea.

An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.

An expert system's knowledge is obtained from expert sources and code in a form suitable for the system to use in its inference process.

Architecture of expert system

Knowledge Base (KB): The component of an expert system that contains the system’s knowledge is called its knowledge base. It contains the rules of in IF-THEN rule. The KB contains the knowledge necessary for understanding, formulating & solving problems. The KB of an expert system contains both declarative knowledge and procedural knowledge.

Inference Engine: It carries out the reasoning by interplaying the information or facts obtained from the user with the knowledge stored in knowledge base whereby the expert system reaches a solution.

Working memory: It is a data structure which stores information about a specific problem.

User Interface: The component of an expert system that communicates with the user is known as the user interface. The communication performed by a user interface is bidirectional. At the simplest level, we must be able to describe our problem to the expert system, and the system must be able to respond with its recommendations. 

Features of expert system

1. It is useful program/software.

2. It should help users to accomplish their goals in shortest possible way.

3. It should be educational when appropriate.

4. It should be able to respond to simple questions.

5. It should be able to learn new knowledge.

6. It should be easily modified.

7. It should be goal oriented.

8. It should be adaptive and flexible.

Knowledge is a theoretical or practical understanding of a subject or a domain. Knowledge is also the sum of what is currently known. Knowledge consists of information that has been:

    – interpreted,

    – categorised,

    – applied, experienced and revised.

So knowledge consists of: facts, ideas, beliefs, heuristics, associations, rules, abstractions, relationships, customs.

An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.

Advantages of Expert System

• It provides consistent answer for repetitive decisions, processes and tasks.
• It holds and maintain levels of information.
• It can tackle very complex problems that are difficult for human expert to solve.
• It improves the decision quality.
• It reduces the cost of consulting experts for problem solving.
• It provides quick and efficient solutions to problem in narrow area of specialization.
• It can discover new knowledge.

Disadvantages of Expert System

• It lacks common sense needed in decision making.
• The knowledge base may not be complete.
• Expensive to build & maintain.
• Takes long time to develop.
• Errors in the knowledge base can lead to wrong decisions.
• Unable to make creative response in an extraordinary situations.

An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.

An expert system's knowledge is obtained from expert sources and code in a form suitable for the system to use in its inference process.

We require expert system due to the following reasons:

• It provides consistent answer for repetitive decisions, processes and tasks.
• It holds and maintain levels of information.
• It can tackle very complex problems that are difficult for human expert to solve.
• It can discover new knowledge.
• It can work steadily without getting emotional, tensed or fatigued.
• It provides quick and efficient solutions to problem in narrow area of specialization.

Architecture of expert system

Knowledge Base (KB): The component of an expert system that contains the system’s knowledge is called its knowledge base. It contains the rules of in IF-THEN rule. The KB contains the knowledge necessary for understanding, formulating & solving problems. The KB of an expert system contains both declarative knowledge and procedural knowledge.

Inference Engine: It carries out the reasoning by interplaying the information or facts obtained from the user with the knowledge stored in knowledge base whereby the expert system reaches a solution.

Working memory: It is a data structure which stores information about a specific problem.

User Interface: The component of an expert system that communicates with the user is known as the user interface. The communication performed by a user interface is bidirectional. At the simplest level, we must be able to describe our problem to the expert system, and the system must be able to respond with its recommendations. 

An expert system is a set of program that manipulate encoded knowledge to solve problem in a specialized domain that normally requires human expertise.

We can construct expert system using given steps as below:

Knowledge Engineering

Knowledge engineering is a field of AI that creates rules to apply data in order to initiate the thought process of human expert. Knowledge engineering attempts to take challenges and solve problems that would usually require a high level of human expertise to solve.

In general, knowledge engineering is the process of understanding and representing a human knowledge in a computer as a program. Knowledge engineering includes:

1) Knowledge acquisition

2) Knowledge representation

3) Knowledge validation

4) Inferencing

5) Explanation and justification

            The interaction between these stages and source of knowledge is shown in the figure below:

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 parts: NLU and NLG.

Issues involved in the natural language processing are given below:

1. The same expression means different things in different context.

·         Where’s the water? ( Chemistry lab? Must be pure)

·         Where’s the water? ( Thirsty? Must be drinking water)

·         Where’s the water? ( Leaky roof? It can be dirty)

2. No natural language program can be complete because of new words, expression, and meaning can be generated quite freely.

·         I’ll fax it to you

3.  There are lots of ways to say the same thing.

·         Ram was born on October 11.

·         Ram’s birthday is October 11.

4.  Sentence and phrases might have hidden meanings

•      “Out of sight, out of mind”-> “ invisible idiot”

•      “The spirit was willing but the flesh was weak” - > “ the vodka was good, but the meat was bad”

5.  Problem due to syntax and semantics

6.  Problem due to extensive use of pronouns. (semantic issue)

•      Eg. Ravi went to the supermarket. He found his favorite brand of coffee in rack. He paid for it and left.

•      It denotes??

7.  Use of grammatically incorrect sentence

•      He rice eats. (syntax issue)

8.  Use of conjunctions to avoid repetition of phrases cause problem in NLP

•      Eg. Ram and Hari went to restaurant. While Ram had a cup of coffee, Hari had tea.

•      Hari had a cup of tea.