1. peas

1. peas

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  PEAS By Md. Fazle Rabbi 16CSE057
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  4.2 Presentation Outlines • Specifyingthe task environment (PEAS ) • Example Of PEAS • Environment Types
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  4.3 PEAS
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  4.4 PEAS • P: Performancemeasure • E: Environment • A: Actuators • S: Sensors
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  4.5 • Performance measure Safe,fast, legal, comfortable trip, maximize profits • Environment Roads, other traffic, pedestrians, customers • Actuators Steering wheel, accelerator, brake, signal, horn • Sensors Cameras, LIDAR, speedometer, GPS, odometer, engine sensors, keyboard PEAS Example: Autonomous taxi
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  4.6 • Performance measure Minimizingfalse positives, false negatives • Environment A user’s email account, email server • Actuators Mark as spam, delete, etc. • Sensors Incoming messages, other information about user’s account PEAS example: Spam filter
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  4.7 PEAS example Agent: Medicaldiagnosis system • Performance measure: Healthy patient, minimize costs, fewer lawsuits. • Environment: Patient, hospital, staff. • Actuators: Screen display (questions, tests, diagnoses, treatments, referrals). • Sensors: Keyboard (entry of symptoms, findings, patient's answers).
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  4.8 PEAS example Agent: Part-pickingrobot • Performance measure: Percentage of parts in correct bins • Environment: Conveyor belt with parts, bins • Actuators: Jointed arm and hand • Sensors: Camera, joint angle sensors
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  4.9 PEAS example Agent: InteractiveEnglish tutor • Performance measure: Maximize student's score on test • Environment: Set of students • Actuators: Screen display (exercises, suggestions, corrections) • Sensors: Keyboard
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  4.10 Environment Types
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  4.11 Environment Types 1.Fully observablevs Partially Observable 2.Static vs Dynamic 3.Discrete vs Continuous 4.Deterministic vs Stochastic 5.Single-agent vs Multi-agent 6.Episodic vs sequential 7.Known vs Unknown 8.Accessible vs Inaccessible
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  4.12 Fully observable vsPartially 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.
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  4.13 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.
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  4.14 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.
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  4.15 • If onlyone 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. Single-agent vs Multi-agent
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  4.16 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. Taxi driving is an example of a dynamic environment whereas Crossword puzzles are an example of a static environment.
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  4.17 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 A self-driving car is an example of a continuous environment.
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  4.18 Thank you