An implementation of agent function. Maps percepts to actions. Can be considered as the brain of the agent.
Reflex agents
Section titled “Reflex agents”Takes actions based on the current percept, and not the percept history.
Simple reflex agents
Section titled “Simple reflex agents”Actions depends only on the current percept, not the history. Simplest type. A predefined set of conditions-actions rules are used to select an action for the current percept. Has limited intelligence. Works best in a fully observable environment. Does not work in many situations, especially dynamic ones.
Model-based reflex agents
Section titled “Model-based reflex agents”Maintains an internal state which depends on the percept history. A simple reflex agent with memory. Plans the immediate action based on the current state and action.
Requires 2 kind of models:
- Transition model
how the environment changes based on the current state and action; both independent and dependent on the action. - Sensor model
how the environment is reflected in percepts.
Indirectly uses the percept history to take actions. The model is like a compressed internal state.
Goal-based agents
Section titled “Goal-based agents”Takes actions to achieve a goal, by considering the future consequences. A model-based agent that work towards a goal. Plans ahead of the immediate action. Boolean evaluation of the goal.
More flexible than reflex agents. Among all the possible actions, the agent will take the action which is most likely help itself to achieve the goal. Uses searching or planning to decide.
Utility-based agents
Section titled “Utility-based agents”Takes actions to achieve a goal in the best way possible. Numeric evaluation of the path.
A generalization and improvement over goal-based agents. Used where goals are inadequate. Can handle scenarios where there are conflicting goals or multiple goals with different priorities. Instead of just determining whether a state achieves a goal or not, utility-based agents calculate the utility of each possible action, and takes the best one.
Utility function
Section titled “Utility function”Maps a state or a sequence of states onto a real number.
Happiness
Section titled “Happiness”Aka. satisfiction. The returned value of the utility function. Means how good the action is.
Environmental state representation
Section titled “Environmental state representation”Atomic
Section titled “Atomic”State is represented as a single, indivisible entity. Similar to a black box. No internal state.
Factored
Section titled “Factored”State is represented as a set of variables. Each variable can be in one of several states.
Structured
Section titled “Structured”State is represented as a set of objects, and relationships.