2 Intelligent Agent articial intellligence.pptx
- 1. Artificial Intelligence Prepared by: Ataklti Nguse Chapter Two: Intelligent Agent 01/08/2025 1
- 2. 2.1. Intelligent Agent • An intelligent agent 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. • The 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. • AI is the science of building machines (agents) that act rationally with respect to a goal. • A thermostat is an example of an intelligent agent. 01/08/2025 2
- 3. 2.2. Agent and Environment • An agent can be anything that perceive its environment through sensors and act upon that environment through actuators. • percept to refer to the agent’s perceptual inputs at any given instant. • Sensor: is a device which detects the change in the environment and sends the information to other electronic devices. • Actuators The actuators are only responsible for moving and controlling a system • Effectors: Effectors are the devices which affect the environment. • Agent has a Goal. 01/08/2025 3
- 4. …Continued 01/08/2025 4 • An AI system is composed of an agent and its environment. • The agents act in their environment. • The environment is not the part of agent and may contain other agents.
- 5. Human Agent and Robot Agent Human Agents Robot Agents Sensors Eyes, Ears, Nose Camera, microphone, Scanners, recorder, infrared (range finders) Effectors Hands, Legs, Mouth Various Motors (Artificial Legs, Artificial Hands,… )Speaker, light 01/08/2025 5
- 6. Exercise • List down the sensors and effectors of the following agents ? 1. software agent 2. Vacuum cleaner agent 01/08/2025 6
- 7. 2.2.1. Rational Agent: • A rational agent is an agent which has clear preference, models uncertainty, and acts in a way to maximize its performance measure with all possible actions. • on the basis of :- • percept sequence • built-in knowledge base • Rationality can be judged on the basis of following points by(PEAS)− – Performance measure which defines the success criterion. – Agent prior knowledge of its environment. – Best possible actions that an agent can perform. – The sequence of percepts. • Here performance measure is the objective for the success of an agent's behavior. 01/08/2025 7
- 8. PEAS for self-driving cars: 01/08/2025 8
- 9. …Continued • 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, microphone, keyboard ,.. 01/08/2025 9
- 10. Develop PEAS description for the following task environment: 1. Vacuum cleaner agent 2. Medical diagnosis system 3. Robot soccer player 4. Shopping for used AI books on the Internet 01/08/2025 10
- 11. 2.2.2.Agent Environment in AI •An environment is everything in the world which surrounds the agent. •The environment is where agent lives, operate and provide the agent with something to sense and act upon it. •Agent perceives and acts in an environment. •Properties of Environments: –Fully observable vs. partially observable –Deterministic vs. stochastic –Episodic vs. non-episodic –Static vs. Dynamic –Discrete vs. continuous 01/08/2025 11
- 12. Fully observable vs. partially observable • If an agent sensor can sense or access the complete state of an environment at each point of time then it is a fully observable environment, else it is partially observable. • A fully observable environment is easy as there is no need to maintain the internal state to keep track history of the world. • Chess ,checker are fully observable • Automatic taxi driving, poker… partially observable 01/08/2025 12
- 13. Deterministic vs. Stochastic • If an agent's current state and selected action can completely determine the next state of the environment, then such environment is called a deterministic environment. • A stochastic environment is random in nature and cannot be determined completely by an agent. • In a deterministic, fully observable environment, agent does not need to worry about uncertainty. • Taxi driving is non-deterministic (i.e. stochastic) 01/08/2025 13
- 14. Episodic vs. Sequential • In an episodic environment, there is a series of one-shot actions, and only the current percept is required for the action. • However, in Sequential environment, an agent requires memory of past actions to determine the next best actions. • Taxi driving is sequential, while mushroom-picking robot is episodic 01/08/2025 14
- 15. Static vs. Dynamic • If the environment can change itself while an agent is deliberating then such environment is called a dynamic environment else it is called a static environment. • Static environments are easy to deal because an agent does not need to continue looking at the world while deciding for an action. • However for dynamic environment, agents need to keep looking at the world at each action. • Taxi driving is an example of a dynamic environment whereas Crossword puzzles are an example of a static environment. 01/08/2025 15
- 16. Discrete vs. Continuous • If in an environment there are a finite number of percepts and actions that can be performed within it, then such an environment is called a discrete environment else it is called continuous environment. • A chess game comes under discrete environment as there is a finite number of moves that can be performed. • A self-driving car is an example of a continuous environment. • Taxi driving is continuous - speed location are in a range of continuous values. • Chess is discrete - there are a fixed number of possible moves on each item 01/08/2025 16
- 17. Examples for Environment Types Problems Observable Deterministic Episodic Static Discrete Crossword Puzzle Yes Yes No Yes Yes mushroom- picking robot No No Yes No No Web shopping program No No No No Yes Tutor No No No Yes Yes Medical Diagnosis No No No No No Taxi driving No No No No No Below are lists of properties of a number of familiar environments 01/08/2025 17 The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous,…
- 18. 2.3. Rationality vs. Omniscience •Rationality maximizes expected performance, while perfection maximizes actual performance. •Rational agent acts so as to achieve one's goals, given one's beliefs (one that does the right thing). –What does right thing mean? one that will cause the agent to be most successful and is expected to maximize goal achievement, given the available information •An Omniscient agent knows the actual outcome of its actions, and can act accordingly, but in reality omniscience is impossible. •A rational agent is autonomous if it can learn to compensate for partial or incorrect prior knowledge 01/08/2025 18
- 19. 2.4.The Structure of Intelligent Agents • Agent’s structure can be viewed as − • Agent = Architecture + Agent Program • Architecture = the machinery that an agent executes on. • Agent Program = an implementation of an agent function. – Feeds the program’s action choices to the effectors. • An agent program executes on the physical architecture to produce function f. • Agent Function − It is a map from the precept sequence to an action. 01/08/2025 19
- 20. 2.5. Types of AI Agents • Agents can be grouped into five classes based on their degree of perceived intelligence and capability. • These are given below: – Simple Reflex Agent – Model-based reflex agent – Goal-based agents – Utility-based agent – Learning agent 01/08/2025 20
- 21. 2.5.1. SIMPLE REFLEX AGENT • These agents take decisions on the basis of the current percepts and ignore the rest of the percept history. • They choose actions only based on the current percept. • They are rational only if a correct decision is made only on the basis of current precept. • Their environment is completely observable. • The Simple reflex agent works on Condition-action rule. • Such as a Room Cleaner agent, it works only if there is dirt in the room. • They have very limited intelligence and not adaptive to changes in the environment. 21
- 22. Structure of simple reflex agent 01/08/2025 22
- 23. 2.5.2. Model-Based Reflex Agent 01/08/2025 23 •The Model-based agent can 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. • Updating the agent state requires information about: How the world evolves • How the agent's action affects the world.
- 24. Structure of model-based reflex agent 01/08/2025 24
- 25. 2.5.3. Goal based agents 01/08/2025 25 •The knowledge 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. •Uses knowledge about a goal to guide its actions • E.g., Search, planning
- 26. Structure of goal-based agent 01/08/2025 26
- 27. 2.5.4. Utility based agents 01/08/2025 27 •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. •Goals alone are not enough to generate high-quality behavior in most environments. •Utility-based agent act based not only goals but also the best way to achieve the goal. •It depends efficiently action achieves the goals. •A utility function maps a state onto a real number which describes the associated degree of happiness.
- 28. Structure of utility-based agent 01/08/2025 28
- 29. 2.5.5.Learning Agents • All agents can improve their performance through learning. 29
- 30. Structure of learning agent 01/08/2025 30