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PEAS: Agents and Environments

CSC 411: AI

Fall 2013

What’s an agent?

Agents include humans, robots, softbots, thermostats, etc. The agent function maps from percept histories to actions: f : P∗^ → A. The agent program runs on the physical architecture to produce f.

PEAS

We first specify the setting. Let’s design an automated taxi:

• (^) Performance measure: Be safe, reach destination, maximize profits, obey laws,...
• (^) Environment: Urban streets, freeways, traffic, pedestrians, weather, customers,...
• (^) Actuators: Steering wheel, accelerator, brake, horn ...
• Sensors: Video, accelerometers, gauges, engine sensors, keyboard, GPS,...

An aside on actuators and sensors

PEAS

A medical diagnosis system?

• (^) Performance measure: Healthy patient, minimal costs, no lawsuits,...
• (^) Environment: Patient, hospital, pharmacy, doctors, nurses, equipment,...
• (^) Actuators: Screen display (questions, tests, diagnoses, treatments, referrals)...
• (^) Sensors: Keyboard (entry of symptoms, findings, patient’s answers)...

PEAS

A medical diagnosis system?

• (^) Performance measure: Healthy patient, minimal costs, no lawsuits,...
• (^) Environment: Patient, hospital, pharmacy, doctors, nurses, equipment,...
• (^) Actuators: Screen display (questions, tests, diagnoses, treatments, referrals,.. .)
• (^) Sensors: Keyboard (entry of symptoms, findings, patient’s answers,.. .)

PEAS

How about an Internet shopping agent?

• (^) Performance measure: Price, quality, appropriateness, efficiency,...
• (^) Environment: Current and future Web sites, vendors, shippers,...
• (^) Actuators: Display to user, follow URL, fill in form ...
• (^) Sensors: Web pages (text, graphics, scripts.. .)

Rational agents

Without loss of generality, “goals” are specifiable by a performance measure defining a numerical value for any environment history. Rational action: whichever action maximizes the expected value of the performance measure given the percept sequence to date. Rational 6 = omniscient Rational 6 = clairvoyant Rational 6 = successful

Environment properties

• (^) Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent’s performance score does.)
• Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions.
• Single agent (vs. multiagent): An agent operating by itself in an environment.

Environment example

Crossword puzzle:

Observable: Agents: Deterministic: Episodic: Static: Discrete:

Environment example

Taxi driving:

Observable: Agents: Deterministic: Episodic: Static: Discrete:

Environment example

Taxi driving:

Observable: Partially Agents: Multi Deterministic: Stochastic Episodic: Sequential Static: Dynamic Discrete: Continuous

Environment example

English tutor:

Observable: Partially Agents: Multi (why?) Deterministic: Stochastic Episodic: Sequential Static: Dynamic Discrete: Discrete

Environment example

Image analysis:

Observable: Agents: Deterministic: Episodic: Static: Discrete: