Intelligent Agents

1. Intelligent Agents

2. Software agents • Monday: • Overview video (Introduction to software agents) • Agents and environments • Rationality • Wednesday: • PEAS (Performance measure, Environment, Actuators, Sensors) • Example of simple and not-so-simple software agents • Friday: • Environment types • Agent types • Group discussion

3. Very short software agents overview • http://labcast.media.mit.edu/?p=23 • What is an agent?: A computer program that is • Embodied (i.e local) • Situated (ie. has knowledge of / responds to its situation / environment) • Autonomous (i.edecides what action to take based on its own situation) • Cooperating (i.e can communicate with other agents to achieve tasks)

4. Agent and environments

5. Agent and environments • Agents include humans, robots, softbots, thermostats, etc. • The agent function maps from percept histories to actions: f: P -> A • The agent program runs on the physical architecture to produce f

6. Vacuum-cleaner world • Percepts: location and contents, e.g., [A;Dirty] • Actions: Left, Right, Suck, NoOp

7. A vacuum-cleaner agent

8. Pseudocode What is the right function? Can it be implemented in a small agent program?

9. Rationality • Fixed performance measure evaluates the environment sequence • one point per square cleaned up in time T? • one point per clean square per time step, minus one per move? • penalize for > k dirty squares? • A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date • Rational is different from omniscient • percepts may not supply all relevant information • Rational is different from clairvoyant • action outcomes may not be as expected • Rational is different from successful • Rational exploration, learning, autonomy

10. PEAS • To design a rational agent, we must specify the task environment. Example: consider, e.g., the task of designing an automated taxi: • Performance measure: • Environment: • Actuators: • Sensors:

11. Another example • Internet shopping agents: • Performance measure?? • Environment?? • Actuators?? • Sensors??

12. Environment types • The environment type largely determines the agent design • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

13. Agent types • Four basic types in order of increasing generality: • Simple reflex agents • Reflex agents with state • Goal-based agents • Utility-based agents

14. Simple reflex agents

15. Reflex agents with state

16. Goal-based agents

17. Utility-based agents

18. Learning agents

19. Example • Simple example • Demonstration of software agent on Linux • Complex example: • http://web.media.mit.edu/~hugo/demos/commonsense-aria.html

20. Summary • Agents interact with environments through actuators and sensors • The agent function describes what the agent does in all circumstances • The performance measure evaluates the environment sequence • A perfectly rational agent maximizes expected performance • Agent programs implement (some) agent functions • PEAS descriptions definetaskenvironments • Environments are categorized along several dimensions: • observable? deterministic? episodic? static? discrete? single-agent? • Several basic agent architectures exist: • Reflex, reflex with state, goal-based, utility-based