Artificial Intelligence & machine learning

Introduction

• Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans.
• It involves the development of algorithms and computer programs that can perform tasks that typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation.
• AI has the potential to revolutionize many industries and has a wide range of applications, from virtual personal assistants to self-driving cars.

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Uses of Artificial Intelligence

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• Artificial Intelligence has many practical applications across various industries and domains, including:
• Healthcare: AI is used for medical diagnosis, drug discovery, and predictive analysis of diseases.
• Finance: AI helps in credit scoring, fraud detection, and financial forecasting.
• Retail: AI is used for product recommendations, price optimization, and supply chain management.
• Manufacturing: AI helps in quality control, predictive maintenance, and production optimization.
• Transportation: AI is used for autonomous vehicles, traffic prediction, and route optimization.
• Customer service: AI-powered chatbots are used for customer support, answering frequently asked questions, and handling simple requests.
• Security: AI is used for facial recognition, intrusion detection, and cybersecurity threat analysis.
• Marketing: AI is used for targeted advertising, customer segmentation, and sentiment analysis.
• Education: AI is used for personalized learning, adaptive testing, and intelligent tutoring systems.

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�Technologies Based on Artificial Intelligence�

• Machine Learning: A subfield of AI that uses algorithms to enable systems to learn from data and make predictions or decisions without being explicitly programmed.
• Natural Language Processing (NLP): A branch of AI that focuses on enabling computers to understand, interpret, and generate human language.
• Computer Vision: A field of AI that deals with the processing and analysis of visual information using computer algorithms.
• Robotics: AI-powered robots and automation systems that can perform tasks in manufacturing, healthcare, retail, and other industries.
• Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain.
• Expert Systems: AI systems that mimic the decision-making ability of a human expert in a specific field.
• Chatbots: AI-powered virtual assistants that can interact with users through text-based or voice-based interfaces.

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INTELLIGENT AGENTS�

• In artificial intelligence, an agent is a computer program or system that is designed to perceive its environment, make decisions and take actions to achieve a specific goal or set of goals.
• Reactive agents are those that respond to immediate stimuli from their environment and take actions based on those stimuli. Proactive agents, on the other hand, take initiative and plan ahead to achieve their goals.
• Agents are used in a variety of applications, including robotics, gaming, and intelligent systems

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Types of Agents�

• Simple Reflex Agents
• Model-Based Reflex Agents
• Goal-Based Agents
• Utility-Based Agents
• Learning Agent
• Multi-agent systems
• Hierarchical agents

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Simple reflex agents�

• Simple reflex agents ignore the rest of the percept history and act only on the basis of the current percept.
• Percept history is the history of all that an agent has perceived to date. The agent function is based on the condition-action rule
• A condition-action rule is a rule that maps a state i.e, condition to an action. If the condition is true, then the action is taken, else not.

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Model-based reflex agents�

• It works by finding a rule whose condition matches the current situation.
• A model-based agent can handle partially observable environments by the use of a model about the world.
• The agent has to keep track of the internal state which is adjusted by each percept and that depends on the percept history

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Goal-based agents�

• These kinds of agents take decisions based on how far they are currently from their goal.
• Their every action is intended to reduce its distance from the goal.
• This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state.

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Utility-based agents�

• The agents which are developed having their end uses as building blocks are called utility-based agents.
• When there are multiple possible alternatives, then to decide which one is best, utility-based agents are used.

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Learning Agent

• A learning agent in AI is the type of agent that can learn from its past experiences or it has learning capabilities.
• It starts to act with basic knowledge and then is able to act and adapt automatically through learning

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Multi-agent systems�

• These agents interact with other agents to achieve a common goal.
• They may have to coordinate their actions and communicate with each other to achieve their objective.
• MAS can be used in a variety of applications, including transportation systems, robotics, and social networks.
• MAS can be implemented using different techniques, such as game theory, machine learning, and agent-based modeling.

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Hierarchical agents�

• These agents are organized into a hierarchy, with high-level agents overseeing the behavior of lower-level agents.
• The high-level agents provide goals and constraints, while the low-level agents carry out specific tasks.
• Hierarchical agents are useful in complex environments with many tasks and sub-tasks.
• Hierarchical agents can be implemented in a variety of applications, including robotics, manufacturing, and transportation systems.

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Environment�

• An environment in artificial intelligence is the surrounding of the agent. The agent takes input from the environment through sensors and delivers the output to the environment through actuators. There are several types of environments:
• Fully Observable vs Partially Observable
• Deterministic vs Stochastic
• Competitive vs Collaborative
• Single-agent vs Multi-agent
• Static vs Dynamic
• Discrete vs Continuous
• Episodic vs Sequential
• Known vs Unknown

What is Ideal Rational Agent?�

• An ideal rational agent is the one, which is capable of doing expected actions to maximize its performance measure, on the basis of −
• Its percept sequence
• Its built-in knowledge base
• Rationality of an agent depends on the following −
• The performance measures, which determine the degree of success.
• Agent’s Percept Sequence till now.
• The agent’s prior knowledge about the environment.
• The actions that the agent can carry out.

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SOLVING PROBLEMS BY SEARCH�

Problem-Solving Agents

• The reflex agent of AI directly maps states into action.
• Whenever these agents fail to operate in an environment where the state of mapping is too large and not easily performed by the agent, then the stated problem dissolves and sent to a problem-solving domain which breaks the large stored problem into the smaller storage area and resolves one by one.

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• Components to formulate the associated problem:
• Initial State: This state requires an initial state for the problem which starts the AI agent towards a specified goal. In this state new methods also initialize problem domain solving by a specific class.
• Action: This stage of problem formulation works with function with a specific class taken from the initial state and all possible actions done in this stage.
• Transition: This stage of problem formulation integrates the actual action done by the previous action stage and collects the final stage to forward it to their next stage.
• Goal test: This stage determines that the specified goal achieved by the integrated transition model or not, whenever the goal achieves stop the action and forward into the next stage to determines the cost to achieve the goal.
• Path costing: This component of problem-solving numerical assigned what will be the cost to achieve the goal. It requires all hardware software and human working cost.

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Uninformed Search Algorithms�

• Uninformed search is a class of general-purpose search algorithms which operates in brute force-way. Uninformed search algorithms do not have additional information about state or search space other than how to traverse the tree, so it is also called blind search.
• Following are the various types of uninformed search algorithms:
• Breadth-first Search
• Depth-first Search

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Breadth-first Search

Breadth-first search is the most common search strategy for traversing a tree or graph. This algorithm searches breadthwise in a tree or graph, so it is called breadth-first search.

• BFS algorithm starts searching from the root node of the tree and expands all successor node at the current level before moving to nodes of next level.
• The breadth-first search algorithm is an example of a general-graph search algorithm.
• Breadth-first search implemented using FIFO queue data structure.

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Depth-first Search�

• Depth-first search is a recursive algorithm for traversing a tree or graph data structure.
• It is called the depth-first search because it starts from the root node and follows each path to its greatest depth node before moving to the next path.
• DFS uses a stack data structure for its implementation.
• The process of the DFS algorithm is similar to the BFS algorithm.

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Heuristics function

• 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.
• Admissibility of the heuristic function is given as:
• 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.

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�Best-first Search Algorithm (Greedy Search)�

• Greedy best-first search algorithm always selects the path which appears best at that moment. It is the combination of depth-first search and breadth-first search algorithms.
• It uses the heuristic function and search. Best-first search allows us to take the advantages of both algorithms. With the help of best-first search, at each step, we can choose the most promising node.
• In the best first search algorithm, we expand the node which is closest to the goal node and the closest cost is estimated by heuristic function, i.e.
• f(n)= g(n).
• Were, h(n)= estimated cost from node n to the goal.
• The greedy best first algorithm is implemented by the priority queue

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A* Search Algorithm

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• A* search is the most commonly known form of best-first search. It uses heuristic function h(n), and cost to reach the node n from the start state g(n).
• It has combined features of UCS and greedy best-first search, by which it solve the problem efficiently. A* search algorithm finds the shortest path through the search space using the heuristic function. This search algorithm expands less search tree and provides optimal result faster.
• A* algorithm is similar to UCS except that it uses g(n)+h(n) instead of g(n).
• In A* search algorithm, we use search heuristic as well as the cost to reach the node. Hence we can combine both costs as following, and this sum is called as a fitness number.

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Adversarial Search�

• 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.
• Games are modeled as a Search problem and heuristic evaluation function, and these are the two main factors which help to model and solve games in AI.

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Mini-Max Algorithm�

• 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.

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Alpha-Beta Pruning�

• 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 +∞.

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Knowledge-Based Agent

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• An intelligent agent needs knowledge about the real world for taking decisions and reasoning to act efficiently.
• Knowledge-based agents are those agents who have the capability of maintaining an internal state of knowledge, reason over that knowledge, update their knowledge after observations and take actions. These agents can represent the world with some formal representation and act intelligently.

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Inference system�

• Inference means deriving new sentences from old. Inference system allows us to add a new sentence to the knowledge base. A sentence is a proposition about the world. Inference system applies logical rules to the KB to deduce new information.
• Inference system generates new facts so that an agent can update the KB. An inference system works mainly in two rules which are given as:
• Forward chaining
• Backward chaining

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Types of knowledge�

Propositional logic

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• Propositional logic (PL) is the simplest form of logic where all the statements are made by propositions.
• A proposition is a declarative statement which is either true or false. It is a technique of knowledge representation in logical and mathematical form.
• Propositional logic is also called Boolean logic as it works on 0 and 1.
• In propositional logic, we use symbolic variables to represent the logic, and we can use any symbol for a representing a proposition, such A, B, C, P, Q, R, etc.
• Propositions can be either true or false, but it cannot be both.

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Knowledge Engineering in First-order logic�

What is knowledge-engineering?

• The process of constructing a knowledge-base in first-order logic is called as knowledge- engineering. In knowledge-engineering, someone who investigates a particular domain, learns important concept of that domain, and generates a formal representation of the objects, is known as knowledge engineer.
• In this topic, we will understand the Knowledge engineering process in an electronic circuit domain, which is already familiar. This approach is mainly suitable for creating special-purpose knowledge base.

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• What is Unification?
• Unification is a process of making two different logical atomic expressions identical by finding a substitution. Unification depends on the substitution process.
• It takes two literals as input and makes them identical using substitution.

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�Bayesian Belief Network�

• Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty. We can define a Bayesian network as:
• "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph."
• It is also called a Bayes network, belief network, decision network, or Bayesian model.
• Bayesian networks are probabilistic, because these networks are built from a probability distribution, and also use probability theory for prediction and anomaly detection.

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Semantics of Bayesian Networks

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• There are two ways in which we can understand Semantics of Bayesian networks:
• 1. See the network as representation of the joint probability distribution. This is useful in understanding how to construct networks.
• 2. See the networks as an encoding of a collection of conditional independence statements. This is useful in designing inference procedures. However, the two ways are equivalent.
• If the Bayesian network is a representation of the joint distribution then it too can be used to answer any query, as earlier in the case of inference through probabilities.
• This does it really so and that too more efficiently. An extension of Baylsian net work is called Decision Net Work or Influence Diagram.

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Exact inference�

• In exact inference, we analytically compute the conditional probability distribution over the variables of interest.
• Exact inference algorithms calculate the exact value of probability P(X|Y ).
• Algorithms in this class include the elimination algorithm, the message-passing algorithm (sum-product, belief propagation), and the junction tree algorithms.

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Approximate Inference�

• Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics that can be used to estimate the posterior distributions of model parameters.
• In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models.
• For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate.

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LEARNING METHODS

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• Machine learning is a subset of AI, which enables the machine to automatically learn from data, improve performance from past experiences, and make predictions.
• Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and on the basis of training, they build the model & perform a specific task.

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Statistical Learning

• Statistical Learning is a set of tools for understanding data. These tools broadly come under two classes: supervised learning & unsupervised learning.
• Generally, supervised learning refers to predicting or estimating an output based on one or more inputs.
• Unsupervised learning, on the other hand, provides a relationship or finds a pattern within the given data without a supervised output.

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Learning with hidden variables

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• Hidden Variables
• The next simplest case is where the model is given, but not all variables are observed. A hidden variable or a latent variable is a variable in a belief network whose value is not observed for any of the examples. That is, there is no column in the data corresponding to that variable.

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ROTE LEARNING

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• Rote learning is the process of memorizing specific new items as they are encountered.
• The basic idea is simple and easy to realize within a computer program: Each time a new and useful piece of information is encountered, it is stored away for future use.
• For example, an AI system might be designed to recognize faces by extracting a variety of features (such as distance between the eyes) from an image and searching for a match within a database of 1000 stored feature sets.

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Explanation based Learning�

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• Explanation based learning has ability to learn from a single training instance. Instead of taking more examples the explanation based learning is emphasized to learn a single, specific example.
• Explanation based generalization (EBG) is an algorithm for explanation based learning, described in Mitchell at al. (1986). It has two steps first, explain method and secondly, generalize method.

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Formal Learning Theory�

• Formal learning theory is the mathematical embodiment of a normative epistemology.
• It deals with the question of how an agent should use observations about her environment to arrive at correct and informative conclusions.
• Philosophers such as Putnam, Glymour and Kelly have developed learning theory as a normative framework for scientific reasoning and inductive inference

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Neural Net Learning�

• A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain.
• It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
• It creates an adaptive system that computers use to learn from their mistakes and improve continuously.

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Genetic Learning�

• Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms.
• Genetic algorithms are based on the ideas of natural selection and genetics.
• These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space.
• They are commonly used to generate high-quality solutions for optimization problems and search problems.

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Expert System�

• An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert.
• It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries.
• It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base.

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• 1. User Interface
• With the help of a user interface, the expert system interacts with the user, takes queries as an input in a readable format, and passes it to the inference engine. After getting the response from the inference engine, it displays the output to the user. In other words, it is an interface that helps a non-expert user to communicate with the expert system to find a solution.
• 2. Inference Engine(Rules of Engine)
• The inference engine is known as the brain of the expert system as it is the main processing unit of the system. It applies inference rules to the knowledge base to derive a conclusion or deduce new information. It helps in deriving an error-free solution of queries asked by the user.
• With the help of an inference engine, the system extracts the knowledge from the knowledge base.
• There are two types of inference engine:
• Deterministic Inference engine: The conclusions drawn from this type of inference engine are assumed to be true. It is based on facts and rules.
• Probabilistic Inference engine: This type of inference engine contains uncertainty in conclusions, and based on the probability.

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Expert Systems/Shells�

• The E.S shell simplifies the process of creating a knowledge base.
• It is the shell that actually processes the information entered by a user relates it to the concepts contained in the knowledge base and provides an assessment or solution for a particular problem.
• Thus E.S shell provides a layer between the user interface and the computer O.S to manage the input and output of the data.
• It also manipulates the information provided by the user in conjunction with the knowledge base to arrive at a particular conclusion.

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