Intelligent Agents PDF

CHAPTER FIVE Intelligent Agents What is an agent? – An agent is anything that perceiving its environment through sensors and acting upon that environment through actuators – Example: Human is an agent A robot is al...

CHAPTER FIVE Intelligent Agents What is an agent? – An agent is anything that perceiving its environment through sensors and acting upon that environment through actuators – Example: Human is an agent A robot is also an agent with cameras and motors A thermostat detecting room temperature. 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. Agents can be classified into different types based on their characteristics, such as whether they are reactive or proactive, whether they have a fixed or dynamic environment, and whether they are single or multi-agent systems.  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. The environment in which an agent operates can also be fixed or dynamic. Fixed environments have a static set of rules that do not change, while dynamic environments are constantly changing and require agents to adapt to new situations.  Multi-agent systems involve multiple agents working together to achieve a common goal. These agents may have to coordinate their actions and communicate with each other to achieve their objectives. Agents are used in a variety of applications, including robotics, gaming, and intelligent systems. They can be implemented using different programming languages and techniques, including machine learning and natural language processing. Artificial intelligence is defined as the study of rational agents. A rational agent could be anything that makes decisions, such as a person, firm, machine, or software. It carries out an action with the best outcome after considering past and current percepts (agent’s perceptual inputs at a given instance). An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents. An agent is anything that can be viewed as:  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. Percept (Vision) – Agent’s perceptual inputs at any given instant. Percept sequence – Complete history of everything that the agent has ever perceived. Agent = Architecture + Agent Program Types of Agents Agents can be grouped into five classes based on their degree of perceived intelligence and capability:  Simple Reflex Agents  Model-Based Reflex Agents  Goal-Based Agents  Utility-Based Agents  Learning Agent  Multi-agent systems  Hierarchical agents 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., a condition to an action. If the condition is true, then the action is taken, else not. Problems with Simple reflex agents are :  Very limited intelligence.  No knowledge of non-perceptual parts of the state.  Usually too big to generate and store. 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. Goal-Based Agents These kinds of agents take decisions based on how far they are currently from their goal(description of desirable situations). Their every action is intended to reduce their distance from the goal. This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. 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. They choose actions based on a preference (utility) for each state. Sometimes achieving the desired goal is not enough. We may look for a quicker, safer, cheaper trip to reach a destination. Agent happiness should be taken into consideration. Utility describes how “happy” the agent is. 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. A learning agent has mainly four conceptual components, which are: 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. A multi-agent system (MAS) is a system composed of multiple interacting agents that are designed to work together to achieve a common goal. These agents may be autonomous or semi-autonomous and are capable of perceiving their environment, making decisions, and taking action to achieve the common objective. MAS can be used in a variety of applications, including transportation systems, robotics, and social networks. They can help improve efficiency, reduce costs, and increase flexibility in complex systems. 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. Agent function & program Agent’s behavior is mathematically described by – Agent function – A function mapping any given percept sequence to an action Practically it is described by – An agent program – The real implementation Vacuum-cleaner world Perception: Clean or Dirty? where it is in? Actions: Move left, Move right, suck, do nothing Program implements the agent function tabulated in Fig. 2.3 Function Reflex-Vacuum-Agent([location,statuse (condition)]) return an action If status = Dirty then return Suck else if location = A then return Right else if location = B then return left Performance measure Performance measure – An objective function that determines How the agent does successfully E.g., 90% or 30% ? An agent, based on its percepts –  action sequence : if desirable, it is said to be performing well. – No universal performance measure for all agents – A general rule: – Design performance measures according to – What one actually wants in the environment – Rather than how one thinks the agent should behave – E.g., in vacuum-cleaner world – We want the floor clean, no matter how the agent behave – We don’t restrict how the agent behaves  CHAPTER SIX Game Artificial Intelligence Game Artificial Intelligence: What is considered Game AI? Is it any NPC behavior? – A single “if” statement? – Scripted behavior? Pathfinding? Animation selection? Automatically generated environment? Best shot at a definition of game AI? Possible Game AI Definition Inclusive view of game AI: “Game AI is anything that contributes to the perceived intelligence of an entity, regardless of what’s under the hood.” Game Agents May act as an – Opponent – Ally – Neutral character Continually loops through the sense-Think-Act cycle – Optional learning or remembering step What Is AI in Gaming? AI in gaming refers to artificial intelligence powering responsive and adaptive behavior within video games. A common example is for AI to control non-player characters (NPCs), which are often sidekicks, allies or enemies of human users that tweak their behavior to appropriately respond to human players’ actions. By learning from interactions and changing their behavior, NPCs increase the variety of conversations and actions that human gamers encounter. Artificial intelligence is also used to develop game landscapes, reshaping the terrain in response to a human player’s decisions and actions. As a result, AI in gaming immerses human users in worlds with intricate environments, malleable narratives and life-like characters. The importance and impact of AI in games Game developers strive to deliver valuable interactive experiences to players. These experiences derive from the cumulative effects of a number of orchestrated game elements, including graphics, sounds, gameplay timing, narratives, challenges, and content that directly interacts with the players (allies, opponents, or other objects). Because of this, AI is becoming an unmatched tool that can help designers coordinate the ever-growing complexity of game dynamics. The growing popularity of AI in games also has significant business benefits for enterprises. The gaming industry is becoming one of the most profitable sectors, with a market value predicted to reach around 314 billion USD by 2026. As a result, funding of the development of AI-based games worldwide has been steadily rising. Today, many startups are emerging in this area. For example, latitude, a startup that develops games using AI-generated infinity storylines, raised 3.3 million USD in seed funding in January 2021. Osmo, an interactive play company, has raised 32.5 million USD in funding so far. Gosu Data Lab, another AI gaming startup based in Lithuania, has raised 5.1 million USD in funding. Gosu mainly focuses on exploring gaming data for AI purposes, helping gamers get better at playing. The History of Game AI As early as 1950, computer scientists were using electronic machines to construct relatively simple game systems, such as Bertie the Brain in 1950 to play tic tac toe, or Nimrod in 1951 for playing Nim. These systems used either electronic light displays and mainly as demonstration systems at large exhibitions to showcase the power of computers at the time. Another early demonstration was Tennis for Two, a game created by William Higinbotham at Brookhaven National Laboratory in 1958 for three-day exhibition, using an analog computer and an oscilloscope for a display. Spacewar! is considered one of the first recognized video games that enjoyed wider distribution behind a single exhibition system. Developed in 1961 for the PDP-1 mainframe computer at MIT, it allowed two players to simulate a space combat fight on the PDP-1's relatively simplistic monitor. The game's source code was shared with other institutions with a PDP-1 across the country as the MIT students themselves moved about, allowing the game to gain popularity. Spacewar! is credited as the first widely available and influential computer game.  CHAPTER SEVEN The AI Technology Used in Business Sector Artificial intelligence is transforming the business sector, driving innovation and efficiency. It automates tasks, enhances decision-making, and creates personalized customer experiences. What is Business Intelligence? Collecting and refining information from many sources (internal and external) Analyzing and presenting the information in useful ways (dashboards, visualizations) So that people can make better decisions That help build and retain competitive advantage. BI Tools and techniques to turn data into meaningful information. – Process: Methods used by the organization to turn data into knowledge. – Product: Information that allows businesses to make decisions. Artificial intelligence in business is the use of AI tools such as machine learning, natural language processing, and computer vision to optimize business functions, boost employee productivity, and drive business value. Understanding the Role of AI in Business Automated Processes AI streamlines repetitive tasks, freeing up human resources for more complex and strategic work. Data Analysis AI algorithms analyze vast amounts of data, identifying patterns and trends to improve decision-making. Customer Insights AI helps companies understand customer behavior, preferences, and needs, leading to better products and services. AI-Powered Process Automation Task Automation AI automates routine tasks, increasing efficiency and reducing errors. Workflow Optimization AI analyzes and improves workflows, making them more efficient and streamlined. Process Integration AI seamlessly integrates different processes, creating a more cohesive and automated system. Predictive Analytics and Decision-Making Data Forecasting AI predicts future trends and patterns, enabling better planning and decision-making. Risk Assessment AI identifies potential risks and vulnerabilities, allowing for proactive mitigation strategies. Optimization Strategies AI suggests optimal solutions based on data analysis, improving efficiency and performance. The Future of AI in Business Advanced Automation AI will automate even more complex tasks, freeing up human creativity. Hyper-Personalization AI will tailor products and services to individual customer preferences. Data-Driven Insights AI will generate deeper insights from data, driving better business decisions.  CHAPTER EIGHT The AI Technology Used in Agriculture Artificial intelligence (AI) technology has shown immense potential in transforming the agricultural sector. In this presentation, we will explore the various benefits, applications, challenges, and future trends in AI for agriculture. The Benefits of AI in Agriculture Increased Productivity and Efficiency AI-powered sensors and tools can help farmers monitor crop growth, soil conditions, and analyze large amounts of data to streamline farming processes and improve overall productivity. Improved Crop Yield and Quality AI algorithms can precisely analyze soil and climate data to optimize crop growth and yield while minimizing waste and reducing environmental impact, leading to high-quality produce. Enhanced Decision-making and Precise Farming AI-powered precision farming can help farmers make data-driven decisions about crop management, fertilizer and pesticide usage, and irrigation scheduling, among others. Applications of AI in Agriculture Crop and Soil Monitoring AI-powered sensors and drones can help monitor crop health, soil moisture, and nutrient levels, enabling farmers to make data-driven decisions for maximizing yield and minimizing waste. Pest and Disease Detection AI-powered image recognition algorithms can help detect and diagnose pest infestations and plant diseases early, reducing yield losses and the usage of harmful pesticides. Automation and Robotics in Farming Operations AI-powered robots and machines can perform various tasks such as planting, weeding, and harvesting, reducing the need for human labor and increasing efficiency and accuracy. Challenges and Limitations of AI in Agriculture 1 Data Privacy and Security Concerns As more data is collected about farming operations, there is a risk of data breaches and misuse. Farmers need to ensure that their data is secure and protected. 2 Lack of Technology Infrastructure in Rural Areas Many areas do not have access to the necessary technology infrastructure to support AI-powered farming, creating a digital divide that must be addressed. 3 Initial Investment and Training Requirements AI-powered tools and machines require significant investment, and there is also a need for training and education to ensure that farmers can effectively use these technologies. Future Trends and Developments in AI for Agriculture Adoption of Machine Learning and Predictive Analytics Machine learning algorithms can be used to analyze large amounts of data and make predictions about crop growth, pest infestation, and environmental changes. Integration of AI with Internet of Things (IoT) Devices AI-powered sensors and devices can be used to collect and analyze data from farming equipment, allowing farmers to optimize their operations and reduce costs. Use of Drones and Satellite Imaging for Farm Monitoring Drones and satellites can provide high-resolution images of crops, soil, and weather conditions, allowing for precise monitoring and analysis of farm operations. Conclusion AI technology has great potential to transform the agricultural sector By leveraging AI-powered tools and machines, farmers can optimize their operations, increase productivity, and improve the quality of their crops. Challenges such as data privacy, infrastructure limitations, and training requirements must be addressed Investment in infrastructure and education can help bridge the digital divide and ensure that all farmers can benefit from AI technology in the future.  CHAPTER NINE The AI Technology Used in Livestock Keeping Welcome to the world of AI technology in livestock keeping. Discover how artificial intelligence is transforming the industry and addressing current challenges. Current Challenges in Livestock Keeping  Disease Control 🦠 Learn how AI technology helps monitor livestock health and prevent disease outbreaks.  Feed Optimization 🌾 Explore how AI algorithms are used to create optimal feeding strategies, reducing costs and improving animal welfare.  Reproductive Management 🐄 Discover how AI technology assists in reproductive management, from estrus detection to genetic selection. Types of AI Technology Used in Livestock Keeping Automated Monitoring 📷 Discover how AI-powered cameras and sensors monitor animal behavior, health, and welfare in real-time. Predictive Analytics 📈 Learn how AI algorithms analyze data to predict crucial outcomes, such as market trends and disease outbreaks. Robotic Systems 🦠 Explore the world of AI-driven robots that automate tasks like feeding, cleaning, and milking. Machine Learning 🦠 Unlock the potential of machine learning algorithms that categorize data, identify patterns, and optimize farm processes. Benefits of Using AI Technology in Livestock Keeping  Improved Efficiency ⚡️ Discover how AI reduces labor requirements and streamlines farm operations, maximizing productivity.  Enhanced Animal Welfare 🐮 Learn how AI technology promotes better animal health, behavior monitoring, and overall well-being.  Increased Profitability 💰 Explore how AI-driven data analysis optimizes resource allocation and drives better profit margins. Examples of Successful AI Technology Implementations in Livestock Keeping Robotic Milking See how robotic milking systems revolutionize dairy farming, ensuring efficient milk production and cow comfort. Live Monitoring Witness the power of AI monitoring systems that analyze behavior, detect abnormalities, and enhance operational control. Automated Feeding Experience the precision and optimized feeding strategies facilitated by AI technology, ensuring healthier herds. Conclusion Embrace the future of livestock keeping with AI technology. It's time to revolutionize your farm and unlock new levels of productivity, efficiency, and animal welfare.  CHAPTER TEN The Use Of AI Technology in Farming Discover how AI is revolutionizing the agriculture industry, improving efficiency, reducing waste, and enhancing crop yields. Benefits of AI in Farming  Increased Efficiency AI-powered technologies optimize resource utilization, automate farming operations, and save time for farmers.  Improved Crop Yield Precision farming techniques enable AI systems to monitor and manage crop growth, resulting in higher yields.  Sustainable Practices AI-driven analytics and predictions help optimize irrigation, pesticide usage, and fertilization for environmentally-friendly farming.  Reduced Labor Costs Automated processes minimize the need for manual labor, reducing expenses for farmers. Applications of AI in Farming Robotics & Automation AI-powered robots perform tasks like planting, harvesting, and weeding, increasing productivity and reducing labor requirements. Weather & Irrigation Optimization AI algorithms leverage weather data to optimize irrigation schedules, reducing water waste and maintaining soil health. Crop Disease Detection Computer vision and machine learning detect early signs of crop diseases, allowing farmers to implement timely interventions. Challenges and Limitations of AI in Farming Data Dependence AI systems require large volumes of accurate and diverse data for optimal performance, posing challenges for implementation. Cost of Adoption Initial investment in AI technology can be expensive, limiting accessibility for small-scale farmers. Technological Limitations AI models may struggle with complex agricultural contexts, such as diverse crop varieties and unpredictable weather patterns. Case Studies of Successful AI Implementation in Farming  Smart Dairy Farming AI-enabled sensors monitor cow health, detect diseases, and autonomously manage feeding schedules, improving overall herd productivity.  Precision Agriculture By analyzing soil data and plant health indicators, AI algorithms optimize fertilizer and pesticide application, leading to increased crop yields.  Aquaponics Management AI optimizes the aquaponics system by monitoring water quality, feeding fish, and ensuring optimal conditions for plant growth, boosting overall efficiency. Future of AI in Farming Explore how AI is expected to continue evolving, with advancements in robotics, autonomous machinery, and predictive analytics poised to transform farming practices globally.