PEAS Framework in AI

PEAS

• PEAS stands for Performance, Environment, Actuators, Sensors
• Used to define task environments
• Performance Measure: Evaluates the agent's performance
• Environment: Where the agent operates
• Actuators: How the agent interacts
• Sensors: How the agent perceives

Example PEAS - Part-Picking Robot

• Performance measure: Percentage of parts correctly placed
• Environment: Conveyor belt with parts and bins
• Actuators: Jointed arm and hand
• Sensors: Camera and joint angle sensors

Example PEAS - Interactive English Tutor

• Performance measure: Maximize student's test score
• Environment: Set of students
• Actuators: Screen displaying exercises, suggestions, and corrections
• Sensors: Keyboard

Environment Types

• Fully Observable vs Partially Observable: Fully observable if sensors get all information about the environment
• Single Agent vs Multiagent: Single agent operates alone, multiagent operates in a competitive or cooperative environment
  • Multiagent challenges: Communication and randomized behavior
• Deterministic vs Stochastic: Deterministic if the next state is fully determined by the current state and agent action, otherwise stochastic
• Episodic vs Sequential: Episodic has independent experiences, sequential actions affect the future
• Static vs Dynamic: Static environments don't change while the agent decides, dynamic environments constantly change
  • Semidynamic environments do not change, but agent's score changes over time

Environment Types (continued)

• Discrete vs Continuous: Applies to environment state, time, percepts, and actions
• Known vs Unknown: Refers to the agent's knowledge about the environment's laws

Hardest Environment

• Partially Observable, Multiagent, Stochastic, Sequential, Dynamic, Continuous, and Unknown is the most difficult type
• Taxi Driving is hard in all these aspects, except it's generally known

Agent Structure

• Agent = Architecture + Program
• An Agent Program implements the agent function
• Should be designed based on the environment

Agent Programs

• Table-Driven Agent: Stores every possible percept sequence with a corresponding action
• Problems: Table size, creation time, learning time, guidance for entries

Agent Types

• Four basic types in order of increasing generality:
  • Simple Reflex Agents
  • Model-Based Reflex Agents
  • Goal-Based Agents
  • Utility-Based Agents
  • Learning Agents
  • All can be turned into learning agents

Simple Reflex Agents

• Select actions based on the current percept, ignoring history
• Examples: Vacuum cleaner agent
• Use condition-action rules: If car in front is braking, initiate braking

Problems with Simple Reflex Agents

• Only work if the correct decision can be made based on current percept
• Infinite loops are common in partially observable environments
• Randomization can help escape infinite loops

Model-Based Reflex Agents

• Maintain an internal state to represent unseen parts of the environment
• Use knowledge about how the world evolves and how actions affect the world
• Example: Driving requires tracking other cars that may not be in view

Goal-Based Agents

• Have a goal information defining desirable situations
• Example: Taxi needs to take in to account where it is trying to go
• Goal-based decision making can be straightforward or complex

Problems with Goal-Based Agents

• Less efficient than reflex agents, but more flexible
• Knowledge is explicitly represented and can be modified

Utility-Based Agents

• Have a utility function that measures how happy a state would make the agent
• Allows for comparison of different world states
• Example: Taxi could take multiple routes to its destination
• Rational decisions: utility function balances conflicting goals and likelihood of success against importance of goals

Learning Agents

• Can improve performance through learning
• Can operate in unknown environments
• Have a learning element to adjust performance based on feedback
• Learning element uses critic feedback and decides how to modify the performance component

Summary (continued)

• Agents interact with environments through actuators and sensors
• The agent function defines the agent's behavior
• The performance measure evaluates the agent's actions
• A rational agent maximizes expected performance
• Several agent architectures: reflex, reflex with state, goal-based, utility-based.

Test your understanding of the PEAS framework, which stands for Performance, Environment, Actuators, and Sensors. This quiz covers its application in various scenarios such as robots and interactive tutors, helping you grasp how agents interact with their environments. Dive in to see how well you can define task environments using PEAS!