Intelligent Agents PDF

Intelligent Agents  Exploring the fundamental concepts and characteristics of intelligent agents.  Analyzing their components.  How it work.  Agent types.  Environment.  Advantages of AI agent. Introduction An autonomous AI agent is an advanced software program that can operate i...

Intelligent Agents  Exploring the fundamental concepts and characteristics of intelligent agents.  Analyzing their components.  How it work.  Agent types.  Environment.  Advantages of AI agent. Introduction An autonomous AI agent is an advanced software program that can operate independently without human control. It can think, act, and learn on its own, without needing constant input from humans. These agents are widely used in different industries, like healthcare, finance, and banking, to make things run smoother and more efficiently. They can adjust to new situations, learn from their experiences, and make decisions using their own internal systems. What is an intelligent agent? An intelligent agent is a system capable of autonomous interaction with its environment, taking actions that maximize its success in achieving its goals. AI program is an intelligent agent. AI agent is a software program designed to interact with its environment, perceive the data it receives through sensors, and take actions based on that data through actuators to achieve specific goals. AI agents simulate intelligent behavior. AI agents can be as simple as rule-based systems or as complex as advanced machine learning models. They use predetermined rules or trained models to make decisions. The components of an intelligent agent: The building blocks of an intelligent agent are:  Performance measure: An objective evaluation of the agent's success.  Environment: The dynamic world the agent operates within.  Actuators: Physical mechanisms enabling the agent to act on its environment.  Sensors: Sensory organs facilitating perception of the surrounding environment.  Decision-making mechanism: is the brain of an AI agent. It processes the information gathered by the sensors and decides what action to take using the actuators. The components of an Intelligent Agent How does AI agents work ?  Perceiving the environment: An autonomous AI agent first needs to gather information about its environment. It can do so using sensors or collecting data from various sources.  Processing input data: The agent takes the knowledge gathered in Step 1 and prepares it for processing. This may include organizing the data, creating a knowledge base, or making internal representations that the agent can understand and work with.  How does AI agents work ?  Decision-making: The agent uses reasoning techniques like logic or statistical analysis to make an informed decision based on its knowledge base and goals.  Executing an action: The agent makes a step or a series of steps to reach its goals.  Learning and Improvement: After taking action, the agent can learn from its own experiences. This feedback loop allows the agent to improve performance and adapt to new situations and environments.  Examples of Agents Human  eyes, ear, skin, taste buds, etc. for sensors.  hands, fingers, legs, mouth for effectors. Robots  Camera, infrared, etc. for sensors.  grippers, wheels, lights, speakers, etc. for actuators. Software agent  functions as sensors.  functions as actuators. Key characteristics of intelligent agents include:  Autonomy: Ability to act independently without human intervention.  Reactivity: Real-time perception (senses and interprets the environment) and response to environmental changes to achieve its goals.  Pro-activeness: planning for future scenarios (Gaming).  Socialness: Interaction with other agents or humans using different methods, like understanding and responding to natural language, recognizing speech.  Learning: They can learn and enhance their performance through machine, deep, and reinforcement learning elements and techniques evolving based on experiences and environmental shifts. Rational Agent What is rational at any given time depends on four things:  The performance measure that defines the criterion of success.  The agent’s prior knowledge of the environment.  The actions that the agent ca n perform.  The agent’s percept sequence to date. This leads to a definition of a rational agent: Rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.  Environment The environment plays a crucial role in shaping how an intelligent agent behaves. The nature of the task environment directly affects the appropriate design for the agent program. Environmental characteristics like: Fully Observable VS. Partial Observable: If an agent’s sensors give it access to the complete state of the environment at each point in time, then we say that the task environment is fully observable. Fully observable environments are convenient because the agent need not maintain any internal state to keep track of the world. An environment might be partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data. If the agent has no sensors at all then the environment is unobservable.  Environmental Characteristics SINGLE-AGENT VS. MULTIAGENT: The distinction between single-agent and multi-agent environments may seem simple enough. For example, an agent solving a crossword puzzle by itself is clearly in a single-agent environment, whereas an agent playing chess is in a two agent environment (chess is a competitive multi-agent environment where as taxi-driving is a cooperative multi-agent environment). Deterministic vs. nondeterministic: If the next state of the environment is completely determined by the current state and the action exec uted by the agent(s), then we say the environment is deterministic; otherwise, it is nondeterministic.  Environmental Characteristics An agent need not worry about uncertainty in a fully observable, deterministic environment. Ifthe environment is partially observable, however, then it could appear to be nondeterministic. Taxi driving is clearly nondeterministic in this sense, because one can never predict the behavior of traffic, moreover, one’s tires may blow out unexpectedly. The vacuum world as we described it is deterministic, but variations can include nondeterministic elements such as randomly appearing dirt.  Environmental Characteristics EPISODIC VS. SEQUENTIAL: In an episodic task environment, the agent’s experience is divided into atomic episodes. In each episode the agent receives a percept and then performs a single action. The next episode does not depend on the actions taken in previous episodes (Arm Robot). In sequential environments, the current decision could affect all future decisions such as Chess and taxi driving are sequential. Episodic environments are much simpler than sequential environments because the agent does not need to think ahead.  Environmental Characteristics DISCRETE VS. CONTINUOUS: The discrete/continuous distinction applies to the state of the environment, to the way time is handled, and to the percepts and actions of the agent. For example, the chess environment has a finite number of distinct states (excluding the clock). Chess also has a discrete set of percepts and actions. Taxi driving is a continuous-state and continuous-time problem: the speed and location of the taxi and of the other vehicles sweep through a range of continuous values and do so smoothly over time. Taxi-driving actions are also continuous.  Environmental Characteristics STATIC VS. DYNAMIC: Static environments are easy to deal with because the agent need not keep looking at the world while it is deciding on an action, nor need it worry about the passage of time. Dynamic environments, are continuously asking the agent what it wants to do. Taxi driving is clearly dynamic where Crossword puzzles are static.  The Structure of Agents Agent =architecture +program. Agent program take the current percept as input from the sensors and return an action to the actuators. The key challenge for AI is to find out how to write programs that, to the extent possible, produce rational behavior.  What are the types of AI agents?  Simple reflex agents These agents select actions on the basis of the current percept, ignoring the rest of the percept history. For example, the vacuum agent. we have written the agent program using if-then-else statements, it is simple enough that it can also be implemented as a Boolean circuit.  The types of AI agents  Model-based reflex agents First, we need some information about how the world changes over time. Second, we need some information about how the state of the world is reflected in the agent’s percepts. For example, when the car in front initiates braking.  The types of AI agents  Goal-based agents Knowing something about the current state of the environment is not always enough to decide what to do, as well as a current state description, the agent needs some sort of goal information that describes situations that are desirable being at a particular destination. For example, at a road junction, the taxi can turn left, turn right, or go straight on. The correct decision depends on where the taxi is trying to get to.  The types of AI agents  Utility-based agents (Happy-based agents): Goals alone are not enough to generate high-quality behavior in most environments. when there are conflicting goals, only some of which can be achieved (for example, speed and safety), the utility function specifies the appropriate (rational) decision. The goals are inadequate but a utility-based agent can still make rational decisions.  The types of AI agents  Learning agents execute machine learning techniques to improve their decision-making over time. What are the advantages of using AI agents?  Increased efficiency AI agents c an automate repetitive tasks, allowing businesses to complete them faster and more accurately.  Better decision-making AI agents can analyze large amounts of data and provide valuable insights (pattern, correlation) to support decision-making processes. What are the advantages of using AI agents?  Timely customer interaction AI agents can provide personalized and timely interactions with customers, enhancing their experience (provide recommendations, leading to increased customer satisfaction).  Cost savings and avoid human errors By automating tasks, AI agents can reduce the need for human resources and manual labor, resulting in cost savings for businesses. They can handle high-volume, repetitive tasks without fatigue or errors.  Examples of AI Applications Healthcare: AI agents can assist in diagnosing, treating, and monitoring patients (IBM Watson Health). Finance: They can analyze financial data, detect fraud, and make investment recommendations (Intelligent Portfolio). Agriculture: AI agents can optimize crop production, monitor soil quality, and predict weather patterns. John Deere uses an AI agent called (See & Spray) to detect and target weeds without affecting crops. Education: They can provide personalized learning experiences, automate administrative tasks, and analyze student performance. Pearson’s AI agent, Aida, can provide feedback to students and suggest personalized learning paths.  Intelligent Agents  Must be sense.  Must be act.  Must be autonomous (to some extent).  Must be rational.