Agents and Rationality in AI

Agents in AI

• An agent can be anything that perceives its environment through sensors and acts upon it through effectors/actuators.
• Humans have organs like eyes and ears for sensors, and hands, legs, and mouth for effectors.
• Robots use cameras, infrared range finders, and motors for sensors and effectors respectively.
• Software agents use encoded bit strings for percepts and actions.

Rational Agents

• A rational agent performs actions that lead to the most successful outcome.
• Success is determined by the agent's performance measure.
• It is important to select the right performance measure to avoid unintended consequences (like an agent cleaning up dirt it brought in itself).
• Performance should be evaluated over a long period to avoid rewarding agents for short-term gains.

Ideal Rational Agent

• An ideal rational agent will always choose the action that maximizes its performance measure based on:
  • Its performance measure
  • Its complete perceptual history (percept sequence)
  • Its knowledge about the environment
  • The actions it can perform

Intelligent Agent Requirements

• Intelligent agents should use a combination of experience and built-in knowledge.
• Autonomy is a key aspect of intelligent agents, meaning they should be able to learn and adapt from their experiences.

Environment Properties

• Observable:
  • Fully Observable: The agent has access to the complete state of the environment.
  • Partially Observable: The agent's sensors do not provide complete information about the environment.
• Deterministic: The next state of the environment is completely determined by the current state and actions selected by the agent.
• Stochastic: The environment's state depends on factors beyond the agent's control, making it unpredictable.
• Episodic: The agent's experience is divided into independent episodes, where the outcome of one episode does not affect the next.
• Sequential: The agent's actions in one episode can affect future episodes.
• Static: The environment remains unchanged while the agent is deliberating.
• Dynamic: The environment can change while the agent makes decisions.
• Discrete: The environment has a limited number of distinct percepts and actions.
• Continuous: The environment has a continuous range of possible states, percepts, and actions.

Agent Program Design

• The goal of AI is to create agent programs that map percepts to actions.
• To design an agent program, you need to understand:
  • The agent's percepts and actions
  • The agent's goals or performance measures
  • The environment in which the agent operates

Agent Program Types

• Simple Reflex Agents: They react to the current percept, using pre-computed actions.
  • Limitations: Vulnerable to changes in the environment, and incapable of handling complex situations.
• Goal-Based Agents: Agents have goals and use these goals to guide their actions, taking into account the future consequences of their choices.

Example: The Automated Taxi Driver

• Percepts: Includes visual information from cameras, speedometer readings, GPS signals, and data from engine and electrical system sensors.
• Actions: Steering, acceleration, braking, communication with passengers, and communication with other vehicles.
• Performance Measures: Safety, speed, legality, comfort of the ride for passengers, and maximizing profits.
• Environment: Roads, other traffic vehicles, pedestrians, customers, and the dynamic changes within this environment.

Agent Program Design for the Taxi Driver

• Simple Reflex Agents: Could be used for basic actions like stopping at red lights.
• Goal-Based Agents: Needed for tasks requiring planning, like finding the most efficient route to a destination considering traffic and road conditions.

Key Points about AI Agents and their Design

• Designing intelligent agents requires deep understanding of the environment and its properties.
• The type of agent program needed depends on the complexity of the task and the environment.
• Combining knowledge with experience is crucial for creating robust and adaptable AI agents.

Explore the concepts of agents and rationality in artificial intelligence. This quiz covers the functionality of agents, their performance measures, and the characteristics of ideal rational agents. Test your understanding of how these concepts apply in real-world scenarios.