Agent Types and Environments

Agent Types and Environments

• There are 9 types of agents: Table driven Agent, Simple reflex agents, Model-based reflex agents, Goal-based agents, Utility-based agents, Learning Agent, Intelligent Agents, Mobile Agent, and Multi-Agent Systems (MAS)

Simple Reflex Agents

• Operate based on a simple "if-then" rule format
• Take actions based on the current percept or input without considering past states or future consequences

Model-based Reflex Agents

• Maintain an internal model or representation of the world
• Use this model to make decisions by considering past states, current percepts, and anticipated future states

Goal-based Agents

• Have predefined goals or objectives that guide their decision-making process
• Take actions that are expected to move them closer to achieving their goals

Utility-based Agents

• Make decisions by evaluating the utility or desirability of different actions
• Choose actions that maximize their expected utility or reward

Learning Agents

• Can adapt and improve their behavior over time through learning mechanisms
• Acquire knowledge and skills from experience, feedback, and training data

Agents and Environments

• An agent perceives its environment through sensors and acts upon it through actuators
• The agent function maps from percept histories to actions
• The agent program implements the agent function

Rational Agent

• Does the right thing based on the performance measure
• Chooses actions that maximize the expected value of the performance measure
• Limited by the available percepts and lacks knowledge of the environment dynamics

PEAS (Performance measure, Environment, Actuators, Sensors)

• A framework for specifying the task environment
• Used to design a rational agent
• Consists of Performance measure, Environment, Actuators, and Sensors

Examples of PEAS

• Automated taxi system
• Medical diagnosis system
• Pac-man game