Intelligent Agents

An agent can be viewed as:

  • perceiving its environment through sensors,
  • and acting upon that environment through its effectors
  • in order to maximize progress towards its goals.

Rational Agents

A rational agent is one that does the right thing.

  • Need to be able to assess agent's performance
  • Should be independent of internal measures
  • Not just dependent on how well it completes a task
  • First consideration: evaluation of rationality

Rational Actions

  • The action that maximizes the expected value of the performance measure given the percept sequence to date

  • Rational Best Yes, to the best of its knowledge

  • Rational Optimal Yes, to the best of its abilities

  • Rational Omniscience

  • Rational Clairvoyant

  • Rational Successful

Autonomy in Agents

The autonomy of an agent is the extent to which its behavior is determined by its own experience.

Extremes:

  • No autonomy – ignores environment/data
  • Complete autonomy – must act randomly / no program

Ideal: design agents to have some autonomy

  • Possibly good to become more autonomous in time

What is an Intelligent Agent?

  • PAGE (Percepts, Actions, Goals, Environment)

  • Task-specific & specialized: well-defined goals and environment

  • The notion of an agent is meant to be a tool for analyzing systems

    • It is not a different hardware or new programming languages
Example

Human mind as network of thousands or millions of agents working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also contain many agents and systems for arbitrating among the agents' competing results.

Session 2 - Agent Diagram.png#invert

Structure of Intelligent Agents

  • Agent program: the implementation of , the agent’s perception-action mapping
function Skeleton-Agent(Percept) return Action
	memory <- UpdateMemory(memory, Percept)
	Action <- ChooseBestAction(memory)
	memory <- UpdateMemory(memory, Action)
return Action
  • Architecture: a device that can execute the agent program
    • Ex. general-purpose computer, specialized device, etc...

Self Driving Example

Collision Avoidance Agent (CAA)

  • Goals: Avoid running into obstacles
  • Percepts: Obstacle distance, velocity, trajectory
  • Sensors: Vision, proximity sensing
  • Effectors: Steering Wheel, Accelerator, Brakes, Horn, Headlights
  • Actions: Steer, speed up, brake, blow horn, signal (headlights)
  • Environment: Freeway

Lane Keeping Agent (LKA)

  • Goals: Stay in current lane
  • Percepts: Lane center, lane boundaries
  • Sensors: Vision
  • Effectors: Steering Wheel, Accelerator, Brakes
  • Actions: Steer, speed up, brake
  • Environment: Freeway

Driving Look Up Table

Session 2 - Self Driving LUT.png#invert

Behavior and Performance

  • Perception (sequence) to Action Mapping:

    • Ideal mapping: specifies which actions an agent ought to take at any point in time

  • Ex. Look-Up-Table
    Session 2 - Look Up Table Example.png#invert

  • Performance measure: a subjective measure to characterize how successful an agent is

    • Ex. speed, power usage, accuracy, money, etc...

  • (degree of) Autonomy: to what extent is the agent able to make decisions and take actions on its own?

Agent Types

Utility-based Agents

  • How well can the goal be achieved (degree of happiness)
  • What to do if there are conflicting goals
    • Ex. Speed and safety
  • What goal should be selected if several can be achieved?

Reactive Agents

  • Reactive agents do not have internal symbolic models.

  • Act by stimulus-response to the current state of the environment.

  • Each reactive agent is simple and interacts with others in a basic way.

  • Complex patterns of behavior emerge from their interaction.

  • Benefits: robustness, fast response time

  • Challenges: scalability, how intelligent? and how do you debug them?

Mobile Agents

  • Programs that can migrate from one machine to another.

  • Execute in a platform-independent execution environment.

  • Mobility not necessary or sufficient condition for agenthood.

  • Practical but non-functional advantages:

    • Reduced communication cost (e.g. from PDA)
    • Asynchronous computing (when you are not connected)

  • Two types:

    • One-hop mobile agents (migrate to one other place)
    • Multi-hop mobile agents (roam the network from place to place)

Information Agents

  • Manage the explosive growth of information.

  • Manipulate or collate information from many distributed sources.

  • Information agents can be mobile or static.

  • Ex. BargainFinder comparison shops among internet stores.