Environments of Agents for their effective working
- 1. Environment
- 2. • INTRODUCTION – Must decide and think about “Task environment” – Task environments are essentially the “problems” to which rational agents are the “solution” • SPECIFYING THE TASK ENVIRONMENT – It involves grouping the following measuring factors together under the heading of the “Task environment” – PEAS (Performance, Environment, Actuator, Sensors) NATURE OF ENVIRONMENT 2
- 3. PEAS • 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
- 4. PEAS • PEAS: Performance measure, Environment, Actuators, Sensors • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi driver: Performance measure – Environment – Actuators – Sensors 4
- 5. Example
- 6. PEAS • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi driver: – Performance measure: Safe, fast, legal, comfortable trip, maximize profits – Environment: Roads, other traffic, pedestrians, customers – Actuators: Steering wheel, accelerator, brake, signal, horn – Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard 6
- 7. PEAS description of the task environment for an automated Taxi 7
- 8. PEAS • Agent: Medical diagnosis system • Performance measure: Healthy patient, minimize costs • Environment: Patient, hospital, staff • Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) • Sensors: Keyboard (entry of symptoms, findings, patient's answers) 8
- 9. PEAS • Agent: Part-picking robot • Performance measure: Percentage of parts in correct bins • Environment: Conveyor belt with parts, bins • Actuators: Jointed arm and hand • Sensors: Camera, joint angle sensors 9
- 11. PEAS • Agent: Interactive English tutor • Performance measure: Maximize student's score on test • Environment: Set of students • Actuators: Screen display (exercises, suggestions, corrections) • Sensors: Keyboard 11
- 13. Examples of agent types and their PEAS descriptions 13
- 15. Environment Types • Fully observable vs Partially Observable • Static vs Dynamic • Discrete vs Continuous • Deterministic vs Stochastic • Single-agent vs Multi-agent • Episodic vs sequential
- 16. Environment types (or) Properties of Task environment • Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic) • Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself. 16
- 17. Environment types • Static (vs. dynamic): The environment is unchanged while an agent is thinking. (The environment is semi- dynamic if the environment itself does not change with the passage of time but the agent's performance score does) • Discrete (Vs. continuous): A limited number of distinct, clearly defined percepts and actions. • Single agent (Vs. multi-agent): An agent operating by itself in an environment. 17
- 18. 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. • Image Recognition • Driving Cars
- 21. Static vs Dynamic • If the environment can change itself while an agent is thinking 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. • Taxi driving is an example of a dynamic environment whereas Crossword puzzles are an example of a static environment. • Empty House • Playing football
- 23. 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.
- 24. 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. (Next state is completely predictable) • A stochastic environment is random in nature and cannot be determined completely by an agent. (Next step has some uncertainty) • In a deterministic, fully observable environment, agent does not need to worry about uncertainty. • Tic Tac Toe game, Physical world
- 25. Single-agent vs Multi-agent • If only one agent is involved in an environment, and operating by itself then such an environment is called single agent environment. • However, if multiple agents are operating in an environment, then such an environment is called a multi-agent environment. • Maze game • Football that includes 10 players
- 26. 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. • Mail Sorting System • However, in Sequential environment, an agent requires memory of past actions to determine the next best actions. • Chess game
- 28. Chess game
- 29. Cont…
- 30. Environment types Chess with Chess without Taxi driving a clock a clock Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes No Single agent No No No • The environment type largely determines the agent design • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent 30