Rational agent
From Wikipedia, the free encyclopedia
A
rational agent takes actions which, given his or her knowledge of its environment, maximizes its chances of success.
The action a rational agent takes depends on:
the agent's past experiences
the agent's information of his
environment
the actions available to the agent
the estimated benefits and the chances of success of the actions.
Rational agents are researched in ethics, the study of practical
reason, and artificial intelligence.
In game theory its usually assumed that the actors are rational.
An example of rational agents is BDI software agents.
See also:
bounded rationality
software agent
intelligent
agent
belief revision
game theory
Further reading
Artificial Intelligence: A Modern Approach (2nd
Edition) by Stuart J. Russell & Peter Norvig, (2002) Prentice Hall, ISBN 0-13-790395-2
Competence Levels
Aristotle's hierarchy resembles the competence levels that Rodney
Brooks (1986) defined for mobile robots. A robot is an AI system that receives signals from the environment and acts on the
environment in a way that helps it to achieve some preestablished goals. In what he called the subsumption architecture for
mobile robots, Brooks distinguished eight levels of competence, each with increasingly more sophisticated goals and means
for achieving them:
Avoiding. Avoid contact with other objects, either moving or
stationary.
Wandering. Wander around aimlessly without hitting things.
Exploring. Look for places in the world that seem reachable and
head for them.
Mapping. Build a map of the environment and record the routes from
one place to another.
Noticing. Recognize changes in the environment that require updates
to the mental maps.
Reasoning. Identify objects, reason about them, and perform actions
on them.
Planning. Formulate and execute plans that involve changing the
environment in some desirable way.
Anticipating. Reason about the behavior of other objects, anticipate
their actions, and modify plans accordingly.
A rational agent must be able to perceive relevant aspects of a situation, evaluate their desirability, and determine
plans for transforming the current situation into a more desirable one.
Ideal Rational Agent
“For each possible percept sequence, an ideal rational agent
should:
do whatever action is expected to maximize its performance measure,
on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has."
Russell & Norvig, Artificial Intelligence: A Modern Approach,
p. 33
