unit-1 AI.pptx hddjlaajhshsjskskdhdhdbhdbd

unit-1 AI.pptx hddjlaajhshsjskskdhdhdbhdbd

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  Introduction of AI •Artificial Intelligence (AI) refers to the simulation of human intelligence in machines designed to think and act like humans. This technology encompasses a variety of techniques and applications, including machine learning, natural language processing, computer vision, and robotics. • Key Concepts: 1.Machine Learning: A subset of AI where algorithms learn from data to make predictions or decisions without being explicitly programmed. 2.Natural Language Processing (NLP): Enables machines to understand, interpret, and respond to human language, facilitating applications like chatbots and language translation. 3.Computer Vision: Allows computers to interpret and understand visual information from the world, used in applications like facial recognition and autonomous vehicles. 4.Robotics: Combines AI with physical machines to perform tasks autonomously, ranging from industrial automation to personal assistance.
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  Future of AI •The future of AI holds immense potential and is likely to shape various aspects of our lives. Here are some key trends and possibilities: • 1. Advanced Personalization • AI will enable highly personalized experiences in areas like healthcare, education, and entertainment. Tailored treatment plans, adaptive learning systems, and customized content recommendations will become more prevalent. • 2. Human-AI Collaboration • Rather than replacing humans, AI is expected to augment human capabilities. In fields such as medicine, design, and research, AI can assist professionals in decision-making, enhancing productivity and creativity. • 3. Autonomous Systems • The development of autonomous vehicles, drones, and robots will transform industries like transportation, logistics, and agriculture. These systems promise increased efficiency, safety, and reduced costs. • 4. Natural Language Understanding • Advancements in natural language processing will lead to more intuitive human-computer interactions. Virtual assistants and chatbots will become even more capable of understanding and responding to complex human queries. • 5. AI in Creativity • AI will increasingly play a role in creative fields, generating art, music, and literature. This could lead to new forms of collaboration between human artists and AI, expanding the boundaries of creativity.
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  • 6. Ethicsand Governance • As AI systems become more integrated into society, ethical considerations will take center stage. There will be a greater emphasis on transparency, fairness, accountability, and the development of regulations to mitigate risks associated with AI deployment. • 7. AI for Social Good • AI has the potential to address global challenges, such as climate change, poverty, and healthcare access. Predictive analytics and data-driven solutions can support better resource allocation and more effective interventions. • 8. Quantum Computing and AI • The convergence of AI and quantum computing could revolutionize data processing capabilities, enabling the solving of complex problems that are currently beyond our reach. • 9. Improved Cybersecurity • AI will play a crucial role in enhancing cybersecurity measures, predicting potential threats, and responding to breaches more effectively. • 10. Widespread Integration • AI will become ubiquitous across industries, from smart home devices to enterprise solutions, fundamentally changing how we interact with technology daily.
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  Characteristics of AIagent • AI agents possess several key characteristics that enable them to function effectively in various environments and tasks. Here are some of the most notable traits: • 1. Autonomy • AI agents can operate independently without human intervention. They can make decisions based on their programming and learned experiences. • 2. Adaptability • These agents can adjust their behavior in response to changes in their environment. They learn from new data and experiences, improving their performance over time. • 3. Intelligence • AI agents exhibit problem-solving capabilities and can process information to make informed decisions. This includes reasoning, learning, and understanding complex situations. • 4. Perception • AI agents often have the ability to perceive their environment through sensors or data inputs, such as cameras, microphones, and other sources. This helps them gather information and respond appropriately. • 5. Communication • Many AI agents can interact with humans and other agents using natural language or predefined protocols, facilitating effective collaboration and information exchange.
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  • 6. Goal-OrientedBehavior • AI agents are typically designed to achieve specific objectives. They use planning and decision-making strategies to pursue their goals efficiently. • 7. Learning • Machine learning capabilities allow AI agents to improve their performance over time by analyzing data and identifying patterns, enhancing their ability to make decisions. • 8. Persistence • AI agents can maintain their state and context over time, allowing them to perform tasks continuously or manage long-term projects. • 9. Multi-Functionality • Many AI agents can perform a variety of tasks, often across different domains, making them versatile tools for various applications. • 10. Ethical Considerations • Advanced AI agents are being designed with ethical considerations in mind, incorporating frameworks to ensure their actions align with societal norms and values.
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  Agents in ArtificialIntelligence An AI system can be defined as the study of the rational agent and its environment. The agents sense the environment through sensors and act on their environment through actuators. An AI agent can have mental properties such as knowledge, belief, intention, etc. What is an Agent? An agent can be anything that perceive its environment through sensors and act upon that environment through actuators. An Agent runs in the cycle of perceiving, thinking, and acting. An agent can be:
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  •Human-Agent: A humanagent has eyes, ears, and other organs which work for sensors and hand, legs, vocal tract work for actuators. •Robotic Agent: A robotic agent can have cameras, infrared range finder, NLP for sensors and various motors for actuators. •Software Agent: Software agent can have keystrokes, file contents as sensory input and act on those inputs and display output on the screen. •Hence the world around us is full of agents such as thermostat, cellphone, camera, and even we are also agents. •Before moving forward, we should first know about sensors, effectors, and actuators.
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  •Sensor: Sensor isa device which detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors. •Actuators: Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system. An actuator can be an electric motor, gears, rails, etc. •Effectors: Effectors are the devices which affect the environment. Effectors can be legs, wheels, arms, fingers, wings, fins, and display screen. •
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  •Intelligent Agents: •An intelligentagent 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. •Following are the main four rules for an AI agent: •Rule 1: An AI agent must have the ability to perceive the environment. •Rule 2: The observation must be used to make decisions. •Rule 3: Decision should result in an action. •Rule 4: The action taken by an AI agent must be a rational action.
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  •Structure of anAI Agent •The task of AI is to design an agent program which implements the agent function. •The structure of an intelligent agent is a combination of architecture and agent program. •It can be viewed as: •Agent = Architecture + Agent program •Following are the main three terms involved in the structure of an AI agent: •Architecture: Architecture is machinery that an AI agent executes on. •Agent Function: Agent function is used to map a percept to an action.
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  •Agent program: Agentprogram is an implementation of agent function. An agent program executes on the physical architecture to produce function f. •PEAS Representation •PEAS is a type of model on which an AI agent works upon. When we define an AI agent or rational agent, then we can group its properties under PEAS representation model. It is made up of four words: •P: Performance measure •E: Environment •A: Actuators •S: Sensors
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  PEAS for self- drivingcars: •Let's suppose a self-driving car then PEAS representation will be: •Performance: Safety, time, legal drive, comfort •Environment: Roads, other vehicles, road signs, pedestrian •Actuators: Steering, accelerator, brake, signal, horn •Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar.
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  Example of Agentswith their PEAS representation Agent Performance measure Environment Actuators Sensors 1. Medical Diagnose •Healthy patient •Minimized cost •Patient •Hospital •Staff •Tests •Treatments Keyboard (Entry of symptoms) 2. Vacuum Cleaner •Cleanness •Efficiency •Battery life •Security •Room •Table •Wood floor •Carpet •Various obstacles •Wheels •Brushes •Vacuum Extractor •Camera •Dirt detection sensor •Cliff sensor •Bump Sensor •Infrared Wall Sensor 3. Part -picking Robot •Percentage of parts in correct bins. •Conveyor belt with parts, •Bins •Jointed Arms •Hand •Camera •Joint angle sensors.
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  Types of AIAgents Simple Reflex Agent Model-based reflex agent Goal-based agents Utility-based agent Learning agent
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  •1. Simple Reflexagent: •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. •These agents only succeed in the fully observable environment. •The Simple reflex agent does not consider any part of percepts history during their decision and action process. •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. •Problems for the simple reflex agent design approach: • They have very limited intelligence • They do not have knowledge of non-perceptual parts of the current state • Mostly too big to generate and to store. • Not adaptive to changes in the environment.
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  •The Model-based agentcan work in a partially observable environment, and track the situation. •A model-based agent has two important factors: • Model: It is knowledge about "how things happen in the world," so it is called a Model- based agent. • Internal State: It is a representation of the current state based on percept history. •These agents have the model, "which is knowledge of the world" and based on the model they perform actions. •Updating the agent state requires information about: • How the world evolves • How the agent's action affects the world.
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  •3. Goal-based agents •Theknowledge of the current state environment is not always sufficient to decide for an agent to what to do. •The agent needs to know its goal which describes desirable situations. •Goal-based agents expand the capabilities of the model-based agent by having the "goal" information. •They choose an action, so that they can achieve the goal. •These agents may have to consider a long sequence of possible actions before deciding whether the goal is achieved or not. Such considerations of different scenario are called searching and planning, which makes an agent proactive. •
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  •4. Utility-based agents •Theseagents 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. •Utility-based agent act based not only goals but also the best way to achieve the goal. •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. •The utility function maps each state to a real number to check how efficiently each action achieves the goals. •
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  •5. Learning Agents •Alearning agent in AI is the type of agent which can learn from its past experiences, or it has learning capabilities. •It starts to act with basic knowledge and then able to act and adapt automatically through learning. •A learning agent has mainly four conceptual components, which are: • Learning element: It is responsible for making improvements by learning from environment • Critic: Learning element takes feedback from critic which describes that how well the agent is doing with respect to a fixed performance standard. • Performance element: It is responsible for selecting external action • Problem generator: This component is responsible for suggesting actions that will lead to new and informative experiences. •Hence, learning agents are able to learn, analyze performance, and look for new ways to improve the performance. •
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  •A problem-solving agentin AI is an intelligent entity that can perceive its environment, reason about it, and take actions to achieve specific goals. These agents are designed to handle various types of problems by following a structured process. Here’s an overview of the characteristics, components, and functioning of a problem-solving agent: •Characteristics of a Problem-Solving Agent •Autonomy: Operates independently based on its reasoning and decision-making capabilities. •Goal-Oriented: Aims to achieve specific objectives or solve defined problems. •Adaptive: Can adjust its strategies based on feedback and changes in the environment. •Rationality: Makes decisions that maximize its chances of achieving its goals based on available information.
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  Component of aProblem Solving Agent • Perceptual System: Gathers data from the environment through sensors, allowing the agent to understand its current state. • Knowledge Base: Contains information about the environment, rules, and previously solved problems, which helps the agent in reasoning. • Reasoning Mechanism: Processes the information and knowledge to draw conclusions or make decisions. This can include algorithms for search, optimization, or logical reasoning. • Action Selection: Determines the best course of action based on the reasoning process, often considering multiple potential actions and their outcomes. • Learning Component: In more advanced agents, this allows them to improve over time by learning from past experiences and data.