![Agents and environments
• The agent function maps from percept histories
to actions:
[f: P* A]
• The agent program runs on the physical
architecture to produce f
• agent = architecture + program](https://image.slidesharecdn.com/3-241110020309-13d83749/85/3-agents-2-ppt-real-world-Artificial-Intelligence-4-320.jpg)
![Vacuum-cleaner world
• Percepts: location and contents, e.g.,
[A,Dirty]
• Actions: Left, Right, Suck, NoOp](https://image.slidesharecdn.com/3-241110020309-13d83749/85/3-agents-2-ppt-real-world-Artificial-Intelligence-5-320.jpg)
3.agents-2.ppt real world Artificial Intelligence
- 2. Outline • Agents and environments • Rationality • PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types • Agent types
- 3. Agents • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
- 4. Agents and environments • The agent function maps from percept histories to actions: [f: P* A] • The agent program runs on the physical architecture to produce f • agent = architecture + program
- 5. Vacuum-cleaner world • Percepts: location and contents, e.g., [A,Dirty] • Actions: Left, Right, Suck, NoOp
- 6. Rational agents • An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful • Performance measure: An objective criterion for success of an agent's behavior • E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.
- 7. Rational agents • Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.
- 8. Rational agents • Rationality is distinct from omniscience • Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration) • An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)
- 9. PEAS • PEAS: Performance measure, Environment, Actuators, Sensors • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi driver: – Performance measure – Environment – Actuators – Sensors
- 10. PEAS • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi driver: – Performance measure: Safe, fast, legal, comfortable trip, maximize profits – Environment: Roads, other traffic, pedestrians, customers – Actuators: Steering wheel, accelerator, brake, signal, horn – Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard
- 11. PEAS • Agent: Medical diagnosis system • Performance measure: Healthy patient, minimize costs, lawsuits • Environment: Patient, hospital, staff • Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) • Sensors: Keyboard (entry of symptoms, findings, patient's answers)
- 12. PEAS • Agent: Part-picking robot • Performance measure: Percentage of parts in correct bins • Environment: Conveyor belt with parts, bins • Actuators: Jointed arm and hand • Sensors: Camera, joint angle sensors
- 13. PEAS • Agent: Interactive English tutor • Performance measure: Maximize student's score on test • Environment: Set of students • Actuators: Screen display (exercises, suggestions, corrections) • Sensors: Keyboard
- 14. Environment types • Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic) • Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.
- 15. Environment types • Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does) • Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. • Single agent (vs. multiagent): An agent operating by itself in an environment.
- 16. Environment types • Uncertain We say an environment is uncertain if it is not fully observable or not deterministic.
- 17. • Fully observable For example, a checkers game can be classed as fully observable, because the agent can observe the full state of the game how many pieces the opponent has, how many pieces we have etc.
- 18. • Partially observable An example of this could be a Poker game. The Agent may not know what cards the opponent has and will have to make best decision based on what cards the opponent has played.
- 19. • Deterministic Deterministic environments are where your agent's actions uniquely determine the outcome. So for example, if we had a pawn while playing chess and we moved that piece from A2 to A3, that would always work. There is no uncertainty in the outcome of that move
- 20. • Episodic Many classification tasks are episodic. For example, an agent that has to spot defective parts on an assembly line bases each decision on the current part, regardless of previous decisions; moreover, the current decision doesn’t affect whether the next part is defective.
- 21. • Sequential In sequential environments, on the other hand, the current decision could affect all future decisions. Chess and taxi driving are sequential: in both cases, short-term actions can have long-term consequences. Episodic environments are much simpler than sequential environments because the agent does not need to think ahead.
- 22. • Static environments easy to deal with because the agent need not keep looking at the world while it is deciding on an action, nor need it worry about the passage of time. Crossword puzzles are static.
- 23. • Dynamic environments continuously asking the agent what it wants to do, if it hasn’t decided yet, that counts as deciding to do nothing. Taxi driving is clearly dynamic: the other cars and the taxi itself keep moving
- 24. • SEMIDYNAMIC Chess, when played with a clock, is semidynamic.
- 25. • Discrete In discrete environments, we have a finite amount of action choices and a finite amount of things that we can sense. Using our checkers example again, there are a finite amount of board positions and a finite amount of things we can do within the checkers environment.
- 26. • Continuous In continuous environments, many actions can be sensed by our agents. To apply this to a medical context, a patient's temperature and blood pressure are continuous variables, and can be sensed by medical agents designed to capture vital signs from patients and then recommend diagnostic action to healthcare professionals.
- 27. • Discrete vs. continuous: The discrete/continuous distinction applies to the state of the environment, to the way time is handled, and to the percepts and actions of the agent. Taxi driving is a continuous-state and continuous- time problem: the speed and location of the taxi and of the other vehicles sweep through a range of continuous values and do so smoothly over time. Taxi-driving actions are also continuous (steering angles, etc.).
- 28. • Single vs. Multi agent example, an agent solving a crossword puzzle by itself is clearly in a single-agent environment, whereas an agent playing chess is in a two agent environment • Multi-agents environment – Competitive : chess – Cooperative : taxi
- 29. Environment types Chess with Chess without Taxi driving a clock a clock Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes No Single agent No No No • The environment type largely determines the agent design • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent • •
- 30. Class exercise • Internet book shopping agent
- 31. Write PEAS and discuss all environment types as well for following: Robot Soccer Player
- 32. Agent functions and programs • An agent is completely specified by the agent function mapping percept sequences to actions • One agent function (or a small equivalence class) is rational • Aim: find a way to implement the rational agent function concisely • •
- 33. Table-lookup agent • Drawbacks: – Huge table – Take a long time to build the table – No autonomy – Even with learning, need a long time to learn the table entries
- 34. Agent types • Four basic types in order of increasing generality: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents
- 39. Learning agents
Editor's Notes
- #8: (all-knowing with infinite knowledge)