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What makes an agent rational?
In artificial intelligence, an agent receives information from its environment and takes actions that affect it. Its sensors provide percepts; its actuators enable actions. A rational agent selects the action expected to maximize its performance measure in light of its percept history and built-in knowledge. The performance measure is the specification of what counts as doing well.
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This definition is conditional. The same action can be rational under one measure and poor under another. A vacuum agent rewarded only for cleaning may keep moving until every reachable square is clean; one also penalized for energy use may stop sooner. To judge an agent, first identify its success criteria. Chalmers University of Technology’s introductory AI slides describe rational choice in terms of expected performance given percept history and knowledge; UC Berkeley CS 188’s agents text connects goals, sensing, and action.
Rationality is not perfect knowledge or guaranteed success
- Not omniscient: An agent may not have access to information that would change its decision.
- Not clairvoyant: Outcomes may be uncertain even when the agent makes a sound choice.
- Not judged by one result alone: A decision can be rational based on the available evidence even if an unlucky outcome follows.
Information gathering can itself be a rational action when it is likely to improve a later choice. The agent decides using what it knows now, rather than information it could not have had.
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What PEAS describes
PEAS is a way to specify an agent’s task environment: Performance measure, Environment, Actuators, Sensors. It clarifies what success means, where the agent operates, how it can act, and how it receives information. Berkeley’s course text uses PEAS to define a task environment.
| Element | Question it answers | Example meaning |
|---|---|---|
| Performance measure | What counts as success? | Clean squares, while accounting for movement or energy if those matter. |
| Environment | Where does the agent operate? | The world, conditions, and other entities it encounters. |
| Actuators | How can it affect that world? | Motors or other mechanisms that move, manipulate, or signal. |
| Sensors | How does it receive information? | Devices or inputs that provide observations of the environment. |
PEAS describes the task setting, not a universal inventory of internal software modules. In a robot, sensors and actuators may be physical hardware; in a software agent, inputs, outputs, and API calls can serve analogous roles.
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Common types of agents
The introductory taxonomy distinguishes agents by how they select or improve actions. These designs need not be mutually exclusive: learning, for example, can be added to other approaches.
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|---|---|---|
| Simple reflex | Maps the current percept to an action. | Does not use percept history. |
| Model-based reflex | Maintains an internal state based on percept history. | Can cope better when the current percept does not reveal the full situation. |
| Goal-based | Uses a goal describing a desirable situation. | Can consider whether actions move toward the goal. |
| Utility-based | Uses a utility function to compare possible outcomes. | Supports trade-offs when outcomes differ in value. |
| Learning | Improves through learning, online or offline. | A learning capability can complement the other designs rather than replace them. |
Reflex agents respond to percepts or internal state; planning agents can model the world and consider the consequences of possible actions before selecting one. The appropriate design depends on the task, not on a universal ranking of agent types.
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Examples: connect the agent to its task
Vacuum-cleaner agent
A basic vacuum agent may sense its location and whether the current square is dirty, then move, suck, or do nothing. Its rational action depends on the performance measure: maximizing cleaned squares, minimizing movement, saving energy, or balancing those goals can produce different choices.
Checkers agent
The board is the environment and a piece move is an action. A reflex policy might react to the current board; a planning agent can model legal moves and their consequences. Because an opponent also chooses actions, checkers is a multi-agent setting.
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Autonomous car
As an illustrative PEAS description, the performance measure might include reaching a destination, obeying traffic laws, safety, travel time, and fuel use. The environment includes roads, traffic, pedestrians, signs, and passengers. Steering, acceleration, braking, and signaling are possible actuator functions; cameras, GPS, speed sensors, and vehicle sensors are possible information sources. This is a general course example, not a description of any one commercial vehicle.
How task-environment properties affect design
Before comparing agent designs, describe the environment they must handle. These properties characterize the problem setting; they are not additional agent architectures.
- Observability: Is the environment fully observable, or can relevant information be hidden?
- Transition uncertainty: Are outcomes deterministic, or stochastic?
- Temporal structure: Are decisions episodic, or do earlier actions affect later ones in a sequential task?
- Change over time: Is the environment static while the agent deliberates, or dynamic? Some frameworks also distinguish semidynamic environments.
- Representation: Are states and actions discrete or continuous?
- Other decision-makers: Is the task single-agent or multi-agent, with cooperative or competitive interactions?
For example, the Chalmers course material characterizes real driving as partially observable, stochastic, sequential, dynamic, continuous, and multi-agent. Those conditions help explain why an agent for driving needs more than a rule that reacts to one isolated observation.
Quick Recap
A practical way to assess an agent
- State the performance measure. Write down what the system is meant to maximize or achieve, including meaningful trade-offs.
- Describe the environment. Note what can be observed, how actions may turn out, whether the world changes, and whether other agents act.
- Identify sensors and actuators. Establish what information is available and which actions the system can take.
- Evaluate the choice using the evidence available at decision time. Ask whether the selected action had the best expected performance under the measure and knowledge, rather than treating one success or failure as decisive.
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