AI

Actuators:
Actuators are the devices that are responsible for moving and controlling a system or machine.ctuators are the energy conversion device used inside a robot. The major function of actuators is to convert energy into movement.
Power Supply:
It is an electrical device that supplies electrical power to an electrical load.The working power to the robot is provided by batteries, hydraulic, solar power, or pneumatic power sources.
Sensor:
Sensors provide real time information on the task environment. Sensors are the devices or machines which help to detect the events or changes in the environment and send data to the computer processor. 
Electric Motors: 
These are the devices that convert electrical energy into mechanical energy and are required for the rotational motion of the machines.
Controller -
Controller is a part of robot that coordinates all motion of the mechanical system. It also receives an input from immediate environment through various sensors. The heart of robot's controller is a microprocessor linked with the input/output and monitoring device. 

AI Programs
Usually, we use to operate them in computer-simulated worlds.
Generally, input is given in the form of symbols and rules.
To operate this, we need general-purpose/Special-purpose computers.
Robots
Generally, we use robots to operate in the real physical world
Inputs are given in the form of the analogue signal or in the form of the speech waveform.
Also, to operate this, special hardware with sensors and effectors are needed.

Computer Vision
Robots can also see, and this is possible by one of the popular Artificial Intelligence technologies named Computer vision. Computer Vision plays a crucial role in all industries like health, entertainment, medical, military, mining, etc.
Computer Vision is an important domain of Artificial Intelligence that helps in extracting meaningful information from images, videos and visual inputs and take action accordingly.

Creating an expert system-
  
 Implementing human intelligence in machines-

Enhancing Efficiency and Automation-
One of the primary goals of AI is to streamline processes and enhance efficiency across industries. Automation powered by AI algorithms can handle repetitive tasks with precision and speed.

Decision-Making and Predictive Analytics-
 Through the power of machine learning, AI algorithms can analyze vast amounts of data to predict trends, identify patterns, and even foresee potential outcomes. 

Personalization and User Experience-
As AI systems become more sophisticated, they aim to provide personalized experiences for users. Recommendation engines and personalized content delivery are examples of AI’.

Natural Language Processing and Understanding-

Reactive Machines-
Purely reactive machines are the most basic types of Artificial Intelligence.
Such AI systems do not store memories or past experiences for future actions.
These machines only focus on current scenarios and react on it as per possible best action.
EXAMPLE-IBM's Deep Blue system,Google's AlphaGo

Limited Memory-
Limited memory machines can store past experiences or some data for a short period of time.
These machines can use stored data for a limited time period only.
Self-driving cars are one of the best examples of Limited Memory systems. These cars can store recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road.

Theory of Mind-
Theory of Mind AI should understand the human emotions, people, beliefs, and be able to interact socially like humans.
This type of AI machines are still not developed, but researchers are making lots of efforts and improvement for developing such AI machines.
FOR EXAMPLE-SOPHIA,it can identify animals and human beings and act act like a human but the research is not yet compketed.

Self-Awareness-
Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent, and will have their own consciousness, sentiments, and self-awareness.
These machines will be smarter than human mind.
Self-Awareness AI does not exist in reality still and it is a hypothetical concept.
for example-the feeling of i want to play and I know I want to play.first sentence is a charcterstic of theory of mind but second sentence shows the sense of consiousness which is a charcterstic of self awarness machines.

Searchingis a step by step procedure to solve a search-problem in a given search space. A search problem can have three main factors:
Search Space: Search space represents a set of possible solutions, which a system may have.
Start State: It is a state from where agent begins the search.
Goal test: It is a function which observe the current state and returns whether the goal state is achieved or not.

Properties of Search Algorithms:
Completeness: A search algorithm is said to be complete if it guarantees to return a solution if at least any solution exists for any random input.

Optimality: If a solution found for an algorithm is guaranteed to be the best solution (lowest path cost) among all other solutions, then such a solution for is said to be an optimal solution.

Time Complexity: Time complexity is a measure of time for an algorithm to complete its task.

Space Complexity: It is the maximum storage space required at any point during the search, as the complexity of the problem.

it is a class of search algorithm that  have no additional information on the goal node other than the one provided in the problem definition.  have no additional information on the goal node other than the one provided in the problem definition. Uninformed search is also called Blind search. These algorithms can only generate the successors and differentiate between the goal state and non goal state. 

The following uninformed search algorithms are discussed in this section.

Depth First Search
Breadth First Search
Uniform Cost Search

Depth-first search-
Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures.
 The algorithm starts at the root node and explores as far as possible along each branch before backtracking. 
It uses last in- first-out strategy and hence it is implemented using a stack.
example-link for image
https://media.geeksforgeeks.org/wp-content/uploads/question-3-e1547112515247.png
Path:   S -> A -> B -> C -> G 
d        = the depth of the search tree 
n^i        = number of nodes in level i        
Time complexity: Equivalent to the number of nodes traversed in DFS. 
T(n) = 1 + n^2 + n^3 + ... + n^d = O(n^d)        
Space complexity: 
maximum storage space required at any point of dfs.
 S(n) = O(n*d)
Completeness: DFS is complete if the search tree is finite, meaning for a given finite search tree, DFS will come up with a solution if it exists. 
Optimality: DFS is not optimal, meaning the number of steps in reaching the solution, or the cost spent in reaching it is high. 

Breadth-first search 
Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. It is implemented using a queue.

example-same image
Path: S -> D -> G 
 s        = the depth of the shallowest solution. 
n^i        = number of nodes in level i        . 
Time complexity: Equivalent to the number of nodes traversed in BFS until the shallowest solution. T(n) = 1 + n^2 + n^3 + ... + n^s = O(n^s)        
Space complexity: Equivalent to how large can the fringe get. S(n) = O(n^s)        
Completeness: BFS is complete, meaning for a given search tree, BFS will come up with a solution if it exists. 

Optimality: BFS is optimal as long as the costs of all edges are equal. 

Uniform Cost Search: 
UCS is different from BFS and DFS because here the costs come into play. In other words, traversing via different edges might not have the same cost. The goal is to find a path where the cumulative sum of costs is the least. 

Path: S -> A -> B -> G 
Cost: 5 
Let C        = cost of solution. 
 epsilon      = arcs cost. 

Then C /epsilon =        effective depth 

Time complexity:   T(n) = O(n^C\epsilon)        , Space complexity:   S(n) = O(n^C/repsilon)

UCS is complete only if states are finite and there should be no loop with zero weight.
UCS is optimal only if there is no negative cost.

First Order Logic in Artificial Intelligence is a technique used for knowledge representation. It is an extension of propositional logic and unlike propositional logic, it is sufficiently expressive in representing any natural language construct. First Order Logic in AI is also known as Predicate Logic or First Order Predicate Logic. It is a robust technique to represent objects as well as their relationships. Unlike propositional logic, First Order Logic in Artificial Intelligence doesn't only include facts but also different other entities as listed below.

Objects:
Objects can denote any real-world entity or any variable. E.g., A, B, colours, theories, circles etc.
Relations:
Relations represent the links between different objects. Relations can be unary(relations defined for a single term) and n-ary(relations defined for n terms). E.g., blue, round (unary); friends, siblings (binary); etc.
Functions:
Functions map their input object to the output object using their underlying relation. Eg: father_of(), mother_of() etc.

Parts of First Order Logic
First-order logic in Artificial Intelligence comprises two main components, which are as follows.

Syntax:
Syntax represents the rules to write expressions in First Order Logic in Artificial Intelligence.
Semantics:
Semantics refers to the techniques that we use to evaluate an expression of First Order Logic in AI. 

In First-Order Logic, inference is used to derive new facts or sentences from existing ones. 

Universal Generalization
Universal generalization is a valid inference rule that states that if premise P(c) is true for any arbitrary element c in the universe of discourse, we can arrive at the conclusion x P. (x).
It can be represented as:
https://tutorialforbeginner.com/images/tutorial/ai-inference-in-first-order-logic.png
Example: Let's represent, P(c): "A byte contains 8 bits", so "All bytes contain 8 bits."for ∀ x P(x) , it will also be true.

Universal Instantiation
A valid inference rule is universal instantiation, often known as universal elimination or UI. It can be used to add additional sentences many times.
The new knowledge base is logically equal to the existing knowledge base.
The UI rule say that we can infer any sentence P(c) by substituting a ground term c (a constant within domain x) from ∀ x P(x) for any object in the universe of discourse.'
It can be represented as
https://tutorialforbeginner.com/images/tutorial/ai-inference-in-first-order-logic.png
Example: 1 IF "Every person like ice-cream"=> ∀x P(x) so we can infer that
"John likes ice-cream" => P(c)

'Existential Instantiation:
Existential instantiation is also known as Existential Elimination, and it is a legitimate first-order logic inference rule.
It can only be used to replace the existential sentence once.
This rule states that for a new constant symbol c, one can deduce P(c) from the formula given in the form of x P(x).
https://tutorialforbeginner.com/images/tutorial/ai-inference-in-first-order-logic3.png
Example: 1

From the given sentence: ∃x Crown(x) ∧ OnHead(x, John),

So we can infer: Crown(K) ∧ OnHead( K, John), as long as K does not appear in the knowledge base.

Existential introduction
An existential generalization is a valid inference rule in first-order logic that is also known as an existential introduction.
This rule argues that if some element c in the universe of discourse has the property P, we can infer that something in the universe has the attribute P.
It's written like this:
https://tutorialforbeginner.com/images/tutorial/ai-inference-in-first-order-logic4.png
Example: Let's say that,
"Priyanka got good marks in English."
"Therefore, someone got good marks in English."
 

Machine learning: It deals with developing algorithms that can learn from data. ML algorithms are used in various applications, including image recognition, spam filtering, and natural language processing.
For example, to identify a simple object such as an apple or orange.

Deep learning: It is a branch of machine learning that harnesses artificial neural networks to acquire knowledge from data. Deep learning algorithms effectively solve various problems, including NLP, image recognition and speech recognition.

Natural language processing: It deals with the interaction between computers and human language. NLP techniques are used to understand and process human language and in various applications, including machine translation, speech recognition, and text analysis.

Robotics: It is a field of engineering that deals with robot design, construction, and operation. Robots can perform tasks automatically in various industries, including manufacturing, healthcare, and transportation.
For example, car assembly lines, in hospitals, office cleaner, serving 
foods and preparing foods in hotels etc. 

Expert systems: They are computer programs designed to mimic human experts' reasoning and decision-making abilities. Expert systems are used in various applications, including medical diagnosis, financial planning, and customer service.

Adversarial search is a search, where we examine the problem which arises when we try to plan ahead of the world and other agents are planning against us.


The environment with more than one agent is termed as multi-agent environment, in which each agent is an opponent of other agent and playing against each other. Each agent needs to consider the action of other agent and effect of that action on their performance.

So, Searches in which two or more players with conflicting goals are trying to explore the same search space for the solution, are called adversarial searches, often known as Games.

Chess, checkers, and tic-tac-toe are the three most popular games that can be solved via adversarial search. 

In many real-world applications, such as financial decision-making and military operations, adversarial search is used.

Formalization of the problem:
A game can be defined as a type of search in AI which can be formalized of the following elements:
Initial state: It specifies how the game is set up at the start.
Player(s): It specifies which player has moved in the state space.
Action(s): It returns the set of legal moves in state space.
Result(s, a): It is the transition model, which specifies the result of moves in the state space.
Terminal-Test(s): Terminal test is true if the game is over, else it is false at any case. The state where the game ends is called terminal states.
Utility(s, p): A utility function gives the final numeric value for a game that ends in terminal states s for player p. It is also called payoff function. For Chess, the outcomes are a win, loss, or draw and its payoff values are +1, 0, ½. And for tic-tac-toe, utility values are +1, -1, and 0.

Example: Tic-Tac-Toe game tree:
The following figure is showing part of the game-tree for tic-tac-toe game. Following are some key points of the game:

There are two players MAX and MIN.
Players have an alternate turn and start with MAX.
MAX maximizes the result of the game tree
MIN minimizes the result.

https://static.javatpoint.com/tutorial/ai/images/ai-adversarial-search.png

From the initial state, MAX has 9 possible moves as he starts first. MAX place x and MIN place o, and both player plays alternatively until we reach a leaf node where one player has three in a row or all squares are filled.
Both players will compute each node, minimax, the minimax value which is the best achievable utility against an optimal adversary.
Suppose both the players are well aware of the tic-tac-toe and playing the best play. Each player is doing his best to prevent another one from winning. MIN is acting against Max in the game.
So in the game tree, we have a layer of Max, a layer of MIN, and each layer is called as Ply. Max place x, then MIN puts o to prevent Max from winning, and this game continues until the terminal node.
In this either MIN wins, MAX wins, or it's a draw. This game-tree is the whole search space of possibilities that MIN and MAX are playing tic-tac-toe and taking turns alternately.

Intelligent Agents:
An intelligent agent is an autonomous entity which act upon an environment using sensors and actuators for achieving goals. An intelligent agent may learn from the environment to achieve their goals. A thermostat is an example of an intelligent agent.

Simple Reflex Agent
Model-based reflex agent
Goal-based agents
Utility-based agent
Learning agent

https://www.javatpoint.com/types-of-ai-agents

PEAS is an AI agent representation system that focuses on evaluating the performance of the environment, sensors, and actuators. We need to be aware of our job environment to create an agent. The PEAS system aids in defining the task environment. Performance, Environment, Actuators, and Sensors are abbreviated as PEAS. AI algorithms can be written more effectively by identifying PEAS.


Performance measures: These are the parameters used to measure the performance of the agent. How well the agent is carrying out a particular assigned task.
Environment: It is the task environment of the agent. The agent interacts with its environment. It takes perceptual input from the environment and acts on the environment using actuators.
Actuators: These are the means of performing calculated actions on the environment. For a human agent; hands and legs are the actuators.
Sensors: These are the means of taking the input from the environment. For a human agent; ears, eyes, and nose are the sensors.

Examples of PEAS Descriptors
1. PEAS Descriptor of Automated Car Driver
Performance

Safety − The automated system needs to be able to operate the vehicle securely without rushing.

Optimized Speed − Depending on the environment, automated systems should be able to maintain the ideal speed.

Journey − The end-user should have a comfortable journey thanks to automated systems.

Environment

Roads − Automated automobile drivers ought to be able to go on any type of route, from local streets to interstates.

Traffic Conditions − For various types of roadways, there are various traffic conditions to be found.

Actuators

Steering wheel − to point an automobile in the appropriate direction.

Gears and accelerators − adjusting the car's speed up or down.

Sensors

In-car driving tools like cameras, sonar systems, etc. are used to collect environmental data.

Automation and Efficiency-
same as goal
Decision making and predictive analysis-
same as goal
Personalization and User Experience-
same as goal
Healthcare and Medicine
Smart computers can help doctors by looking at medical images and suggesting treatments.. It also contributes to drug discovery and development.
Natural Language Processing (NLP)-
same as goal
Education and Learning
Smart computers can help you learn better by adjusting lessons to fit your style. It's like having a tutor that understands how you learn best. AI-powered educational tools provide tutoring, feedback, and assistance to learners, supplementing traditional teaching methods.
Content Creation: 
AI is involved in generating art, music, and other creative content. It can assist artists and creators in the production of new and innovative works.

In multiagent systems (MAS), agents interact with each other to achieve individual and collective goals. Arguments in this context refer to the communication and negotiation mechanisms used by agents to exchange information, reach agreements, and make decisions. These arguments can take various forms and play a crucial role in resolving conflicts, sharing knowledge, and coordinating actions among autonomous agents. 

Agents put forth claims or propositions during interactions. These claims represent a position or statement that an agent asserts as true or desirable.

There are types of arguments as described below,
(a) Informational arguments: (Beliefs->Belief format) e.g. If it is cloudy, it might rain.
(b) Motivational arguments: (Beliefs, desires->Desire format) e.g. If it is cloudy and you want
to get out then you don’t want to get wet.
(c) Practical arguments: (Belief, sub -goalsGoal format) e.g. If it is cloudy and you own a
raincoat then put the raincoat.
(d) Social arguments: (social commentsGoal, desire format) e.g. I will stop at the corner because
the law say so. e.g. I can’t do that, I promise to my mother that I won’t.
(e) Interactions (binary or collective) between arguments:
(1) Conflict (defeat) format e.g. attacks 
There are interactions of support-type that are used for collective binary arguments in multi
agents system

In multi-agent systems (MAS), negotiation and bargaining are essential mechanisms that enable autonomous agents to interact, make joint decisions, and coordinate their activities. These processes are crucial for resolving conflicts, reaching agreements, and achieving collective goals in a distributed and decentralized environment. 

Negotiation is the process by which agents communicate and exchange information to reach mutually acceptable agreements. It involves a series of interactions where agents propose, counter-propose, and modify their positions until an agreement is reached or the negotiation is terminated.

Types of Negotiation:

Distributive Negotiation: Agents engage in distributive negotiation when there is a fixed amount of resources to be divided among them. This type often involves zero-sum games, where one agent's gain is another agent's loss.

Integrative Negotiation: Integrative negotiation aims to create value by identifying opportunities for mutually beneficial agreements. Agents work together to find solutions that maximize joint benefits.

Bargaining:

Definition: Bargaining is a subset of negotiation that specifically focuses on the process of reaching agreements by making offers, counteroffers, and concessions. 

Any negotiation and bargaining mechanism should have the following attributes: 

Efficiency: Negotiating should be done without wasting a lot of time or resources.

Stability: Once an agreement is made, no one should want to back out because it benefits everyone involved.

Simplicity: The negotiation process should be easy to understand and not too complicated to follow.

Distribution: No single person should have all the power; decisions should be made by everyone involved.

Symmetry: Everyone should be treated fairly, and the process should not favor one person over another without good reason.

Information retrieval (IR) is the process of obtaining relevant information from a large repository or database. This retrieval is based on a user's query or information need. The goal of information retrieval is to efficiently and effectively locate and deliver information that is relevant to the user's request.
For example, information retrieval is widely used in popular search engine like Google, Yahoo, Bing etc. 

Here are the key characteristics of information retrieval:
Content Organization:
IR systems organize and index vast amounts of data or documents for efficient and effective retrieval.

Query Processing:
IR systems process user queries, which can be expressed in various formats, ranging from simple keyword queries to complex Boolean expressions or natural language queries.

Relevance Ranking:
Results are ranked based on their relevance to the user's query. Various algorithms and ranking models are employed to determine the order in which results are presented to the user.

Scalability:
IR systems are designed to handle large and diverse datasets. They should be scalable to accommodate growing volumes of data without compromising performance.

Information Retrieval Models:
Different IR models, such as Boolean model, Vector Space model, and Probabilistic model, define how documents are represented and how relevance is assessed.

Information Extraction (IE) is a natural language processing (NLP) technique that involves automatically extracting structured information from unstructured text.

Information extraction is the technique of creating database entries. 

The goal of information extraction is to transform raw textual data into useful information by identifying and extracting specific pieces of information, such as entities, relationships, and events.

The field such as street, city, state, pin-code are extracted from instances of addresses from web pages. 

The instance of weather report with temperature, wind, speed, humidity are extracted. 

Information retrieval systems and full text parser can be mapped by mid system called information extraction. 

Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and humans in natural language.

It involves the use of computational techniques to process and analyze natural language data, such as text and speech, with the goal of understanding the meaning behind the language.

NLP is used in a wide range of applications, including machine translation, sentiment analysis, speech recognition, chatbots, and text classification. 

The field is divided into  three different parts:

Speech Recognition — The translation of spoken language into text.
Natural Language Understanding (NLU)  — The computer’s ability to understand what we say.
Natural Language Generation  (NLG) — The generation of natural language by a computer.

NLU and NLG are the key aspects depicting the working of NLP devices

Spam Filters: One of the most irritating things about email is spam. Gmail uses natural language processing (NLP) to discern which emails are legitimate and which are spam.

Algorithmic Trading: Algorithmic trading is used for predicting stock market conditions. Using NLP, this technology examines news headlines about companies and stocks and attempts to comprehend their meaning in order to determine if you should buy, sell, or hold certain stocks.

Questions Answering: NLP can be seen in action by using Google Search or Siri Services. A major use of NLP is to make search engines understand the meaning of what we are asking and generate natural language in return to give us the answers.

Summarizing Information: On the internet, there is a lot of information, and a lot of it comes in the form of long documents or articles. NLP is used to decipher the meaning of the data and then provides shorter summaries of the data so that humans can comprehend it more quickly.

Chatbots
Chatbots are a form of artificial intelligence that are programmed to interact with humans in such a way that they sound like humans themselves. Chatbots are created using Natural Language Processing and Machine Learning.

Language Translator-
Google Translate and other translation tools as well as use Sequence to sequence modeling that is a technique in Natural Language Processing. It allows the algorithm to convert a sequence of words from one language to another which is translation.

Sentiment Analysis-
Companies use natural language processing,to understand the general sentiment of the users for their products and services and find out if the sentiment is good, bad, or neutral. 

Lexical Analysis and Morphological-
The first phase of NLP is the Lexical Analysis. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It divides the whole text into paragraphs, sentences, and words.

Syntactic Analysis (Parsing)
Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words.
Example: Agra goes to the Poonam
In the real world, Agra goes to the Poonam, does not make any sense, so this sentence is rejected by the Syntactic analyzer.

Semantic Analysis
Semantic analysis is concerned with the meaning representation. It mainly focuses on the literal meaning of words, phrases, and sentences.

Discourse integration
Discourse describes communication between 2 or more individuals. Discourse integration analyzes prior words and sentences to understand the meaning of ambiguous language.

Pragmatic analysis
Pragmatic analysis attempts to derive the intended—not literal—meaning of language.

Machine translation (MT) is the use of algorithms and artificial intelligence to automatically convert text or speech from one language to another.

In a machine translation task, the input already consists of a sequence of symbols in some language, and the computer program must convert this into a sequence of symbols in another language.

Direct MTApproach 
The Direct Translation approach works by translating the source language directly into the target language, without any intermediate representation. This method often operates at the word or phrase level, using dictionaries and rules to handle lexical, morphological, and syntactic differences between languages. While this approach can result in speedy translations, it can also lead to inaccuracies and difficulties in coping with complex language structures.

Transfer Approach
The Transfer-Based Translation approach involves converting the source language into an intermediate representation that captures its syntactic and semantic structure. This intermediate representation is then used to generate a translation in the target language, subsequently processed through linguistic rules and transformations. Although typically more computationally expensive than direct translation, transfer-based translation can produce higher-quality translations by preserving the structure and meaning of the source text.

Interlingua approach
Lastly, the Interlingua-Based Translation approach translates the source language into an abstract, language-independent representation called "interlingua." The target language translation is then generated from the interlingua. This approach is advantageous for multilingual translation scenarios, as only two translation steps are needed between any pair of languages. However, creating a comprehensive interlingua that can express different language structures accurately is a challenging task.

Forward chaining is a form of reasoning which start with atomic sentences in the knowledge base and applies inference rules (Modus Ponens) in the forward direction to extract more data until a goal is reached.

The Forward-chaining algorithm starts from known facts, triggers all rules whose premises are satisfied, and add their conclusion to the known facts. This process repeats until the problem is solved.

Properties of Forward-Chaining:

It is a down-up approach, as it moves from bottom to top.

It is a process of making a conclusion based on known facts or data, by starting from the initial state and reaches the goal state.

Forward-chaining approach is also called as data-driven as we reach to the goal using available data.

Forward -chaining approach is commonly used in the expert system, such as CLIPS, business, and production rule systems.

For example, suppose that the goal is to conclude the colour of my pet 
Bruno given that he croaks and eats flies, and that the rule base contains 
the following two rules :  
If X croaks and eats flies - Then X is a frog. 
If X is a frog - Then X is red. 





Backward Chaining
A backward chaining algorithm is a form of reasoning, which starts with the goal and works backward, chaining through rules to find known facts that support the goal.

Properties of backward chaining-
  
It is known as a top-down approach.

Backward-chaining is based on modus ponens inference rule.

In backward chaining, the goal is broken into sub-goal or sub-goals to prove the facts true.

It is called a goal-driven approach, as a list of goals decides which rules are selected and used.

Backward -chaining algorithm is used in game theory, automated theorem proving tools, inference engines, proof assistants, and various AI applications.

e backward-chaining method mostly used a depth-first search strategy for proof.

When based on available data a decision is taken then the process is called as Forward chaining.	Backward chaining starts from the goal and works backward to determine what facts must be asserted so that the goal can be achieved.
2.	Forward chaining is known as data-driven technique because we reaches to the goal using the available data.	Backward chaining is known as goal-driven technique because we start from the goal and reaches the initial state in order to extract the facts.
3.	It is a bottom-up approach.	It is a top-down approach.
4.	It applies the Breadth-First Strategy.	It applies the Depth-First Strategy.
5.	Its goal is to get the conclusion.	Its goal is to get the possible facts or the required data.
6.	Slow as it has to use all the rules.	Fast as it has to use only a few rules.
7.	It operates in forward direction i.e it works from initial state to final decision.	It operates in backward direction i.e it works from goal to reach initial state.
8.	Forward chaining is used for the planning, monitoring, control, and interpretation application.	It is used in automated inference engines, theorem proofs, proof assistants and other artificial intelligence applications.

Knowledge representation and reasoning (KR, KRR) is the part of Artificial intelligence for representing information about the real world so that a computer can understand and can utilize this knowledge to solve the complex real world problems such as diagnosis a medical condition or communicating with humans in natural language.It is also a way which describes how we can represent knowledge in artificial intelligence. Knowledge representation is not just storing data into some database, but it also enables an intelligent machine to learn from that knowledge and experiences so that it can behave intelligently like a human.

Knowledge representation has two entities : 
Facts: Facts are the truth in some relevant world. 
Representation : Representation is the presentation of facts in some chosen formalism. 
For example: 
Fact : Charlie is a dog. 
Representation of fact using mathematical logic : Dog (Charlie) 

Algorithm: Unify(Ψ1, Ψ2)

Step. 1: If Ψ1 or Ψ2 is a variable or constant, then:
	a) If Ψ1 or Ψ2 are identical, then return NIL. 
	b) Else if Ψ1is a variable, 
		a. then if Ψ1 occurs in Ψ2, then return FAILURE
		b. Else return { (Ψ2/ Ψ1)}.
	c) Else if Ψ2 is a variable, 
		a. If Ψ2 occurs in Ψ1 then return FAILURE,
		b. Else return {( Ψ1/ Ψ2)}. 
	d) Else return FAILURE. 
Step.2: If the initial Predicate symbol in Ψ1 and Ψ2 are not same, then return FAILURE.
Step. 3: IF Ψ1 and Ψ2 have a different number of arguments, then return FAILURE.
Step. 4: Set Substitution set(SUBST) to NIL. 
Step. 5: For i=1 to the number of elements in Ψ1. 
	a) Call Unify function with the ith element of Ψ1 and ith element of Ψ2, and put the result into S.
	b) If S = failure then returns Failure
	c) If S ≠ NIL then do,
		a. Apply S to the remainder of both L1 and L2.
		b. SUBST= APPEND(S, SUBST). 
Step.6: Return SUBST. 

State space search is a problem-solving technique used in Artificial Intelligence (AI) to find the solution path from the initial state to the goal state by exploring the various states. The state space search approach searches through all possible states of a problem to find a solution. It is an essential part of Artificial Intelligence and is used in various applications, from game-playing algorithms to natural language processing.

State space search has several features that make it an effective problem-solving technique in Artificial Intelligence. These features include:

Exhaustiveness:
State space search explores all possible states of a problem to find a solution.

Completeness:
If a solution exists, state space search will find it.

Optimality:
Searching through a state space results in an optimal solution.

Uninformed and Informed Search:
State space search in artificial intelligence can be classified as uninformed if it provides additional information about the problem.

In contrast, informed search uses additional information, such as heuristics, to guide the search process.

digram from nb

Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.

Algorithm for Simple Hill Climbing:
Step 1: Evaluate the initial state, if it is goal state then return success and Stop.
Step 2: Loop Until a solution is found or there is no new operator left to apply.
Step 3: Select and apply an operator to the current state.
Step 4: Check new state:
If it is goal state, then return success and quit.
Else if it is better than the current state then assign new state as a current state.
Else if not better than the current state, then return to step2.
Step 5: Exit.

Problems in Hill Climbing Algorithm:
1. Local Maximum: A local maximum is a peak state in the landscape which is better than each of its neighboring states, but there is another state also present which is higher than the local maximum.

Solution: Backtracking technique can be a solution of the local maximum in state space landscape. Create a list of the promising path so that the algorithm can backtrack the search space and explore other paths as well.

Hill Climbing Algorithm in AI
2. Plateau: A plateau is the flat area of the search space in which all the neighbor states of the current state contains the same value, because of this algorithm does not find any best direction to move. A hill-climbing search might be lost in the plateau area.

Solution: The solution for the plateau is to take big steps or very little steps while searching, to solve the problem. Randomly select a state which is far away from the current state so it is possible that the algorithm could find non-plateau region.

Hill Climbing Algorithm in AI
3. Ridges: A ridge is a special form of the local maximum. It has an area which is higher than its surrounding areas, but itself has a slope, and cannot be reached in a single move.

Solution: With the use of bidirectional search, or by moving in different directions, we can improve this problem.

Hill Climbing Algorithm in AI

Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic function is always positive.

h(n) <= h*(n)  
Here h(n) is heuristic cost, and h*(n) is the estimated cost. Hence heuristic cost should be less than or equal to the estimated cost.

Mini-max algorithm is a recursive or backtracking algorithm which is used in decision-making and game theory. It provides an optimal move for the player assuming that opponent is also playing optimally.
Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
In this algorithm two players play the game, one is called MAX and other is called MIN.
Both the players fight it as the opponent player gets the minimum benefit while they get the maximum benefit.
Both Players of the game are opponent of each other, where MAX will select the maximized value and MIN will select the minimized value.
The minimax algorithm performs a depth-first search algorithm for the exploration of the complete game tree.

Min-Max algorithm : 
Step 1: Set FINAL_VALUE to be minimum as possible. 
Step 2 : If limit of search has been reached, then FINAL_VALUE = 
GOOD_VALUE of the current position. 
Step 3: Else do. 
Step 3.1 : Generate the successors of the position. 
Step 3.2: Recursively call MIN-MAX again with the present position with 
depth incremented by unity. 
Step 4: Evaluate the GOOD_VALUE. 
Step 5 : If GOOD_VALUE > FINAL_VALUE then FINAL_VALUE = GOOD_VALUE.

Example: 
Consider a game which has 4 final states and paths to reach final state are from root to 4 leaves of a perfect binary tree as shown below. Assume you are the maximizing player and you get the first chance to move

https://media.geeksforgeeks.org/wp-content/uploads/minmax.png

Maximizer goes LEFT: It is now the minimizers turn. The minimizer now has a choice between 3 and 5. Being the minimizer it will definitely choose the least among both, that is 3
Maximizer goes RIGHT: It is now the minimizers turn. The minimizer now has a choice between 2 and 9. He will choose 2 as it is the least among the two values.
Being the maximizer you would choose the larger value that is 3. Hence the optimal move for the maximizer is to go LEFT and the optimal value is 3.

Alpha-beta pruning is a modified version of the minimax algorithm. It is an optimization technique for the minimax algorithm.
As we have seen in the minimax search algorithm that the number of game states it has to examine are exponential in depth of the tree. Since we cannot eliminate the exponent, but we can cut it to half. Hence there is a technique by which without checking each node of the game tree we can compute the correct minimax decision, and this technique is called pruning. This involves two threshold parameter Alpha and beta for future expansion, so it is called alpha-beta pruning. It is also called as Alpha-Beta Algorithm.
Alpha-beta pruning can be applied at any depth of a tree, and sometimes it not only prune the tree leaves but also entire sub-tree.
The two-parameter can be defined as:
Alpha: The best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞.
Beta: The best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞.
The Alpha-beta pruning to a standard minimax algorithm returns the same move as the standard algorithm does, but it removes all the nodes which are not really affecting the final decision but making algorithm slow. Hence by pruning these nodes, it makes the algorithm fast.

Modifications to min-max: There are some heuristic search methods other than alpha-beta pruning method which are used to improve the performance of min-max procedure. They are: 
Greedy hill climbing method 
Artificial immune algorithm