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A rational agent is a system that chooses the action expected to perform best according to a defined success measure, using the information it has received and any built-in knowledge. Rationality is therefore relative to the task: it does not mean the agent knows everything, always succeeds, or makes the choice a person would prefer.
What makes an agent rational?
In artificial intelligence, an agent receives information from its surroundings and takes actions that affect them. It is rational when, given its percept history and built-in knowledge, it selects the action expected to maximize its performance measure. Chalmers University’s course slides explain the decision rule in those terms; UC Berkeley’s CS 188 text describes agents as using sensors to perceive and actuators to act.
- Rationality is not omniscience. An agent can make the best choice available without having all relevant information.
- Rationality is not clairvoyance. Outcomes may be uncertain, so a sensible choice can still lead to an unfavorable result.
- Rationality is not guaranteed success. Judge the decision by the evidence and expected performance at the time, not only by what happened afterward.
The performance measure matters. If a system is rewarded for the wrong outcome, it may optimize that measure while behaving in a way people consider unreasonable. To assess whether an action is rational, first establish what counts as success.
How PEAS describes an agent’s task
PEAS is a framework for specifying the task environment: what the agent should achieve, where it operates, how it can act, and how it receives information. Berkeley’s CS 188 text uses PEAS to define a task environment.
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- Performance measure: the criterion used to evaluate success or utility.
- Environment: the external world and conditions in which the agent operates.
- Actuators: the means through which it takes actions.
- Sensors: the means through which it gathers information.
PEAS describes the task setting, not a universal set of internal software modules. A robot may use physical sensors and actuators; a software agent may receive inputs and produce outputs through interfaces or API calls.
Common types of AI agents
Introductory AI courses commonly describe five agent designs. They differ in how they choose or improve actions; learning can be combined with the other designs rather than treated as a mutually exclusive category.
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| Type | How it selects or improves actions | Useful distinction |
|---|---|---|
| Simple reflex | Chooses an action based on the current percept. | Does not use percept history. |
| Model-based reflex | Uses an internal state informed by percept history. | Can help when the current percept does not reveal the whole situation. |
| Goal-based | Considers whether actions move the agent toward a goal. | Evaluates actions in relation to desirable situations. |
| Utility-based | Uses a utility function to compare possible outcomes. | Supports trade-offs among outcomes. |
| Learning | Improves from experience or other learning, online or offline. | Learning can be added to other agent designs. |
Simple reflex behavior responds to the present situation. More deliberative, planning-oriented behavior can model the world and consider possible consequences before acting, as described by Berkeley CS 188.
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Examples: applying rationality and PEAS
Vacuum-cleaner agent
A basic vacuum agent may sense its location and whether the current square is dirty. It can move, clean, or do nothing. The rational choice depends on the performance measure: prioritizing cleaned squares may favor different actions than minimizing movement or conserving energy. If the measure balances these aims, the agent must weigh them rather than optimize just one.
Checkers agent
The board is the environment and moving a piece is an action. A reflex agent can respond to the current board position; a planning agent can consider possible moves and their consequences. Because an opponent also chooses actions, checkers is a multi-agent environment.
Autonomous car
For an illustrative self-driving task, a performance measure might account for reaching a destination, obeying traffic laws, safety, time, and fuel use. The environment includes roads, traffic, pedestrians, signs, and passengers. Steering, acceleration, braking, and signaling are possible actuator functions; cameras, sonar, GPS, and vehicle sensors are examples of information sources. These are general course illustrations, not specifications for a particular car.
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Agent design depends on the conditions of the task, not just on the desired outcome. The following dimensions help describe those conditions:
- Observability: fully observable or partially observable, depending on how much of the relevant state the agent can perceive.
- Outcome uncertainty: deterministic or stochastic, depending on whether actions reliably produce a known result.
- Temporal structure: episodic when decisions are separate, or sequential when one action affects later situations.
- Change during action or deliberation: static or dynamic; some frameworks also distinguish semidynamic environments.
- Representation: discrete or continuous states and actions.
- Other decision-makers: single-agent or multi-agent, with other agents potentially cooperating or competing.
These labels describe the problem setting, not agent architectures. For example, the course materials characterize real driving as partially observable, stochastic, sequential, dynamic, continuous, and multi-agent. Chalmers’ slides discuss these environment dimensions alongside the agent types.
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Why information-gathering can be rational
An agent need not act immediately on incomplete information. If gathering information can improve a later decision, an information-seeking action may be worthwhile. Whether it is rational depends on the expected benefit under the performance measure and the cost or consequences of gathering that information.
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