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UNIT-I
INTRODUCTION
Introduction
Artificial intelligence , or AI , is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities. According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently , in the similar manner the intelligent humans think. Foundations of Artificial Intelligence (AI) :
- Mathematics: Linear algebra, calculus, probability, and statistics.
- Computer Science: Programming languages, data structures, algorithms, and software engineering.
- Machine Learning: Supervised and unsupervised learning, neural networks, and deep learning.
- Natural Language Processing (NLP): Text processing, sentiment analysis, and language generation.
- Computer Vision: Image processing, object recognition, and image generation.
- Robotics: Robot control, sensor integration, and human- robot interaction.
- Philosophy: Ethics, logic, and epistemology.
- Cognitive Science: Human cognition, perception, and attention.
- Data Science: Data preprocessing, visualization, and analysis.
- Domain Expertise: Knowledge of a specific domain or industry, such as healthcare or finance.
The Evolution of Artificial Intelligence: Past, Present
and Future
Since the birth of artificial intelligence (AI), we have had an incredible journey, moving from fantastic ideas to real-world technologies that are changing our world today. But how did AI get to where it is today, and what are the prospects for the future? The past: the birth of Artificial Intelligence,The history of AI goes back in time, to a time when computers were huge machines capable of performing only the most basic tasks. The first attempts to create AI were limited to programs capable of solving a limited set of tasks. These were the years when the term “Artificial Intelligence” sounded like a part of sci-fi novels. The present: Conquests and Big Data. Over time, AI has undergone a revolution. At the end of the last century and the beginning of the present, with the development of computing power and big data, AI began to solve problems that seemed unbelievable before. We see it in everyday things, from voice assistants on our smartphones to automated driving systems in cars and data analysis in medical research.
Environment types
1. Fully Observable vs Partially Observable When an agent sensor is capable to sense or access the complete state of an agent at each point in time, it is said to be a fully observable environment else it is partially observable. Maintaining a fully observable environment is easy as there is no need to keep track of the history of the surrounding. An environment is called unobservable when the agent has no sensors in all environments. Examples: o Chess – the board is fully observable, and so are the opponent’s moves. o Driving – the environment is partially observable because what’s around the corner is not known. 2. Deterministic vs Stochastic When a uniqueness in the agent’s current state completely determines the next state of the agent, the environment is said to be deterministic.
The stochastic environment is random in nature which is not unique and cannot be completely determined by the agent. Examples: o Chess – there would be only a few possible moves for a chess piece at the current state and these moves can be determined. o Self-Driving Cars- the actions of a self-driving car are not unique, it varies time to time.
3. Competitive vs Collaborative An agent is said to be in a competitive environment when it competes against another agent to optimize the output. The game of chess is competitive as the agents compete with each other to win the game which is the output. An agent is said to be in a collaborative environment when multiple agents cooperate to produce the desired output. When multiple self-driving cars are found on the roads, they cooperate with each other to avoid collisions and reach their destination which is the output desired. 4. Single-agent vs Multi-agent An environment consisting of only one agent is said to be a single-agent environment. A person left alone in a maze is an example of the single- agent system. An environment involving more than one agent is a multi-agent environment.
the environment and then performs the corresponding action. Example: Consider an example of Pick and Place robot , which is used to detect defective parts from the conveyor belts. Here, every time robot(agent) will make the decision on the current part i.e. there is no dependency between current and previous decisions. In a Sequential environment , the previous decisions can affect all future decisions. The next action of the agent depends on what action he has taken previously and what action he is supposed to take in the future. Example: o Checkers- Where the previous move can affect all the following moves.
8. Known vs Unknown In a known environment, the output for all probable actions is given. Obviously, in case of unknown environment, for an agent to make a decision, it has to gain knowledge about how the environment works.
Agents in Artificial Intelligence:
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. The agent operates autonomously, meaning it is not directly controlled by a human operator. Perceiving its environment through sensors and Acting upon that environment through actuators
Structure of an AI Agent
To understand the structure of Intelligent Agents, we should be familiar with Architecture and Agent programs. Architecture is the machinery that the agent executes on. It is a device with sensors and actuators, for example, a robotic car, a camera, and a PC. An agent program is an implementation of an agent function. An agent function is a map from the percept sequence(history of all that an agent has perceived to date) to an action. Agent = Architecture + Agent Program There are many examples of agents in artificial intelligence. Here are a few: Intelligent personal assistants: These are agents that are designed to help users with various tasks, such as scheduling appointments, sending messages, and setting reminders. Examples of intelligent personal assistants include Siri, Alexa, and Google Assistant. Autonomous robots: These are agents that are designed to operate autonomously in the physical world. They can perform tasks such as cleaning, sorting, and delivering goods. Examples of autonomous robots include
TYPES OF AGENT
o Simple Reflex Agent o Model-based reflex agent o Goal-based agents o Utility-based agent o Learning agent
1. Simple Reflex agent: o The Simple reflex agents are the simplest agents. These agents take decisions on the basis of the current percepts and ignore the rest of the percept history. o These agents only succeed in the fully observable environment. o The Simple reflex agent does not consider any part of percepts history during their decision and action process. o The Simple reflex agent works on Condition-action rule, which means it maps the current state to action. Such as a Room Cleaner agent, it works only if there is dirt in the room. o Problems for the simple reflex agent design approach: o They have very limited intelligence o They do not have knowledge of non-perceptual parts of the current state o Mostly too big to generate and to store. o Not adaptive to changes in the environment.
2. Model-based reflex agent o The Model-based agent can work in a partially observable environment, and track the situation. o A model-based agent has two important factors: o Model: It is knowledge about "how things happen in the world," so it is called a Model-based agent. o Internal State: It is a representation of the current state based on percept history. o These agents have the model, "which is knowledge of the world" and based on the model they perform actions. o Updating the agent state requires information about: a. How the world evolves b. How the agent's action affects the world.
4. Utility-based agents o These agents are similar to the goal-based agent but provide an extra component of utility measurement which makes them different by providing a measure of success at a given state. o Utility-based agent act based not only goals but also the best way to achieve the goal. o The Utility-based agent is useful when there are multiple possible alternatives, and an agent has to choose in order to perform the best action. o The utility function maps each state to a real number to check how efficiently each action achieves the goals. 5. Learning Agents o A learning agent in AI is the type of agent which can learn from its past experiences, or it has learning capabilities. o It starts to act with basic knowledge and then able to act and adapt automatically through learning. o A learning agent has mainly four conceptual components, which are:
a. Learning element: It is responsible for making improvements by learning from environment b. Critic: Learning element takes feedback from critic which describes that how well the agent is doing with respect to a fixed performance standard. c. Performance element: It is responsible for selecting external action d. Problem generator: This component is responsible for suggesting actions that will lead to new and informative experiences. o Hence, learning agents are able to learn, analyze performance, and look for new ways to improve the performance.
Knowledge Base: These agents often possess some form of knowledge or representation of the problem domain. This knowledge can be encoded in various ways, such as rules, facts, or models, depending on the specific problem. Reasoning: Problem-solving agents employ reasoning mechanisms to make decisions and select actions based on their perception and knowledge. This involves processing information, making inferences, and selecting the best course of action. Planning: For many complex problems, problem-solving agents engage in planning. They consider different sequences of actions to achieve their goals and decide on the most suitable action plan. Actuation: After determining the best course of action, problem-solving agents take actions to interact with their environment. This can involve physical actions in the case of robotics or making decisions in more abstract problem-solving domains. Feedback: Problem-solving agents often receive feedback from their environment, which they use to adjust their actions and refine their problem-solving strategies. This feedback loop helps them adapt to changing conditions and improve their performance. Learning: Some problem-solving agents incorporate machine learning techniques to improve their performance over time. They can learn from experience, adapt their strategies, and become more efficient at solving similar problems in the future. Search Algorithm Terminologies: Search: Searching is 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.
Search tree: A tree representation of search problem is called Search tree. The root of the search tree is the root node which is corresponding to the initial state. Actions: It gives the description of all the available actions to the agent. Transition model: A description of what each action do, can be represented as a transition model. Path Cost: It is a function which assigns a numeric cost to each path. Solution: It is an action sequence which leads from the start node to the goal node. Optimal Solution: If a solution has the lowest cost among all solutions. Properties of Search Algorithms: Following are the four essential properties of search algorithms to compare the efficiency of these 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. Well-defined problems and solutions A problem can be divided into following components: Initial state: The initial state that the agent starts in.
Successor Function: from the state In (Arad),the successor function for the Romania problem would return {(Go(Sibzu),In(Sibiu)), (Go(Timisoara), In(Tzmisoara)), (Go(Zerznd), In(Zerind))) State Space: The above image gives the state space of Romanian city problem. Transition Model: A description of how the state changes when an action is taken, defining the result state given the current state and action (e.g., RESULT (In(Arad), Go(Zerind)) = In(Zerind)). Goal test: The agent's goal in Romania is the single ton set{In(Bucharest))}. Path cost: Finding distance in miles for all the paths between Arad to Bucharest. Example Problems:
Vacuum World: The problem can be formulated as follows: States: The agent is in one of two locations, each of which might or might not contain dirt. Thus, there are2x 22 =8 possible world states. Initial state: Any state can be designated as the initial state. Successor function: This generates the legal states that result from trying the three actions (Left, Right and Suck). The complete state space is shown in figure. Goal test : This checks whether all the squares are clean. Path cost: Each step costs 1, so the path cost is the number of steps in the path The 8 - puzzle Problem: What is 8 puzzle problem? The 8-puzzle problem is invented and popularized by Noyes Palmer Chapman in the year 1870. It is played on a 3 - by- 3 grid with 8 square blocks/tiles labeled 1 through 8 and a blank square. The goal of this 8-puzzle problem is to rearrange the given blocks
