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Classification of Intelligent Agents

COMP111 Lectures

Intelligent agents are classified depending on how they map their precepts to their actions. They can be classed under:

  • Simple reflex agents
    • Select actions to execute based upon the current percept
    • Don’t take the percept history into account.
    • Very simple IFTTT actions.
    • Very simple to implement but have limited intelligence.
    • They have no memory.
graph TB
    A(Environment)--> |Sensors| B
    B[What the world is like now] --> C
    C[What action I should do now]--> |Actuators| A
    D[Condition-action rules]-->C
  • Model-based reflex agents
    • Maintain an internal state that depends upon the percept history - memory.
    • Helps deal with partial observability.
    • Knows how the world evolves and how its actions affect the world.
graph TB
    A(Environment)--> |Sensors| B
    B[What the world is like now] --> C
    C[What action I should do now]--> |Actuators| A
    D[Condition-action rules]-->C
    E-->B
    B.->E(State)
    F(How the world evolves)-->B
    G(What my actions do)-->B
  • Goal-based agents
    • Select appropriate actions to achieve desirable state of the environment.
    • Knowledge of the current state does not automatically mean that the agent knows what to do.
    • It will have to plan what to do over a long sequence of actions.
graph TB
    A(Environment)  --> |Sensors| B
    B[What the world is like now] --> H
    H[What it will be like if I do action A] --> C
    C[What action I should do now]--> |Actuators| A
    D[Condition-action rules]-->C
    B.->E(State)
    E-->B
    F(How the world evolves)-->B
    F --> H
    G(What my actions do)-->B
    G --> H
  • Utility-based agents
    • Make use of a utility function to compare the desirability of different states that result from actions.
    • Many actions may satisfy the goal but which one is the most desirable?
    • Utility function maps a state, or sequence of states, onto a number to give the degree of usefulness of the state to the agents.
    • Agent maximises the value of its utility function.
graph TB
    A(Environment)--> |Sensors| B
    B[What the world is like now] --> H
    H[What it will be like if I do action A] --> I
    I[How happy I will be in such a state] --> C
    J(Utility)-->I
    C[What action I should do now]--> |Actuators| A
    D[Condition-action rules]-->C
    B.->E(State)
    E-->B
    F(How the world evolves)-->B
    F --> H
    G(What my actions do)-->B
    G --> H
  • Learning agents
    • Learning agents find ways of maximising their utility function based on previous experience
    • The performance element is the same choice mechanism from the previous agents. This is now informed by the learning element.
    • The performance standard informs the learning element how well it’s actions have worked.
graph TB
	A(Environment) --> |Sensors| B[Performance element]
	B --> |Actuators| A
	D[Performance standard] --> C[Critic]
	C --> |Feedback| L[Learning element]
	L --> |Changes| B
	B --> |Knowledge| L
	L --> |Learning Goals| P[Problem Generator]
	P --> B