Lecture on Intelligent Agents in AI

Introduction

Agent: Entity perceiving environment via sensors and acting via actuators.
- Examples: NPCs, Siri, Roomba, Robots on Mars.
Environment: Everything external to the agent.
- Examples: Game board in Pac-Man, Mars surface, and atmosphere.
Interaction Model: Agent interacts with the environment (e.g., Pac-Man & ghosts, robot on Mars).
Agent's Requirements: Must receive input from the environment and act on it.

Sensors: Devices through which an agent perceives its environment (e.g., cameras, microphones).
Actuators: Devices through which an agent acts on the environment (e.g., motors in a Roomba).

Environmental State: A snapshot of the environment including all agent perceptions and characteristics (e.g., location, time, temperature).
Percept Sequence: Entire history of what an agent has perceived (sequence of environmental states).

Rational Agent: Seeks to do the right thing based on predefined goals and performance measures.
Performance Measure: Evaluates actions and states to determine if the agent is doing the right thing.
Challenges: Defining 'right' actions can be subjective; thus, performance measures are essential.
Example: Vacuum cleaner robot assessing cleanliness and action efficiency.

Goal-Oriented Design: Focus on the overall objective (goal) rather than specific actions.
Performance Measures: Should be aligned with desired outcomes and objective goals.
Gaming AI: Example of Mario’s Goombas and their interaction with Mario.
- Intelligent Goombas should predict Mario’s movement and adjust speed accordingly.
- Rational actions are defined to maximize in-game objectives (e.g., moving toward Mario).
Exam Tips: Understand and explain concepts in your own words; application over memorization.

Conflicting Objectives: Ensuring the agent balances multiple goals effectively (e.g., cleaning efficiency vs. power usage in a vacuum robot).
Dynamic Environments: Agents must adapt and respond effectively to changing environments.
Performance Measure Design: Crafting effective and robust performance measures is crucial for rational agent behavior.

Rational Agent Programming: In assignments like Bomberman, define clear, goal-oriented performance measures for agent behaviors.
Evaluation: Consistently re-evaluate agent behaviors to ensure alignment with desired goals; adjust performance measures as necessary.

Key Takeaways: Rational agents aim to do the right thing based on performance measures; practical application requires understanding of sensors, actuators, and environment interactions.
Looking Ahead: Future topics will build on this foundation and involve more complex agent behaviors.

Important for Exam: Definitions and understanding of agents, environments, performance measures, and rationality concepts.