Lecture 02.1: Intelligent Agents and Environments

What is an Agent?

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

• Human Agent: Sensors include eyes and ears. Actuators include hands, legs, and mouth.
• Robotic Agent: Sensors include cameras and infrared range finders. Actuators include motors.
• Software Agent: Sensors receive keystrokes, file contents, and network packets. Actuators display on the screen, write to files, and send network packets.

Agents & Environments

The agent takes sensory input from its environment and produces output actions that affect it.

• Percept: The term used to refer to the content an agent's sensors are perceiving.
• Percept Sequence: The complete history of everything the agent has ever perceived.

An agent's behavior is described by the agent function, which maps any given percept sequence to an action: [f: P* → A]. The agent program runs on the physical architecture (computing device) to produce this function.

Agent = Architecture + Program

What makes an Agent "Intelligent" or "Rational"?

A rational agent is one that acts so as to achieve the best outcome, or when there is uncertainty, the best expected outcome.

What is rational at any given time depends on four things:

1. The performance measure that defines the criterion of success.
2. The agent's prior knowledge of the environment.
3. The actions that the agent can perform.
4. The agent's percept sequence to date.

For example, the performance measure of a vacuum-cleaner agent could be the amount of dirt cleaned up, time taken, electricity consumed, or noise generated.

An intelligent agent is capable of flexible autonomous action. According to Wooldridge & Jennings, "flexible" means three things:

• Reactivity: Agents perceive their environment and respond in a timely fashion.
• Pro-activeness: Agents exhibit goal-directed behavior by taking the initiative.
• Social ability: Agents are capable of interacting with other agents or humans.

Furthermore, an agent is autonomous if its behavior is determined by its own experience (the ability to learn and adapt).

Task Environment (PEAS)

When designing an intelligent agent, we must first specify the setting. We use the PEAS description:

• Performance measure
• Environment
• Actuators
• Sensors

PEAS Examples

1. Medical Diagnosis System:
   • Performance: 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.
2. Part-Picking Robot:
   • Performance: Percentage of parts in correct bins.
   • Environment: Conveyor belt with parts, bins.
   • Actuators: Jointed arm and hand.
   • Sensors: Camera, joint angle sensors.

Environment Types

Environments can vary drastically in their characteristics, which influences the agent design:

• Fully Observable vs. Partially Observable: Does the agent's sensors give it access to the complete state of the environment at all times?
• Deterministic vs. Stochastic: Is the next state of the environment completely determined by the current state and the agent's action?
• Episodic vs. Sequential: Is the agent's experience divided into atomic "episodes" that do not depend on past actions?
• Static vs. Dynamic: Can the environment change while the agent is deliberating? (If only the agent's performance score changes, it is semidynamic).
• Discrete vs. Continuous: Are there a limited number of distinct percepts and actions?
• Single Agent vs. Multiagent: Is the agent operating by itself or alongside others? (e.g. Crossword puzzle vs. Chess).