![Agents and environments
ƒ The agent function maps from percept
histories to actions: [f: P* Æ A]
ƒ The agent program runs on the physical
architecture to produce f
ƒ agent = architecture + program
4](https://image.slidesharecdn.com/ai-slides-1233566181695672-21-220810045330-261b1b96/85/ai-slides-1233566181695672-2-1-pdf-4-320.jpg)
![Vacuum-cleaner world
ƒ Percepts: location and contents, e.g.,
[A, Dirty]
ƒ Actions: Left, Right, Suck, NoOp
5](https://image.slidesharecdn.com/ai-slides-1233566181695672-21-220810045330-261b1b96/85/ai-slides-1233566181695672-2-1-pdf-5-320.jpg)
![A vacuum-cleaner agent
6
ƒ What is the right function?
ƒ Can it be implemented in a small agent program?
ƒ Partial tabulation of a simple agent function:
Percept sequence Action
[A;Clean] Right
[A;Dirty] Suck
[B;Clean] Left
[B;Dirty] Suck
[A;Clean], [A;Clean] Right
[A;Clean], [A;Dirty] Suck
…. ….](https://image.slidesharecdn.com/ai-slides-1233566181695672-21-220810045330-261b1b96/85/ai-slides-1233566181695672-2-1-pdf-6-320.jpg)
![Simple reflex agents
24
function Reflex-Vacuum-Agent( [location,status]) re-
turns an action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left](https://image.slidesharecdn.com/ai-slides-1233566181695672-21-220810045330-261b1b96/85/ai-slides-1233566181695672-2-1-pdf-21-320.jpg)
ai-slides-1233566181695672-2 (1).pdf
- 1. Intelligent Agents (more concrete study) R&N: Chap. 2
- 2. Outline ƒ Agents and environments ƒ Rationality ƒ PEAS (Performance measure, Environment, Actuators, Sensors) ƒ Environment types ƒ Agent types 2
- 3. Agents ƒ An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators ƒ Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators ƒ Robotic agent: cameras and infrared range finders for sensors; various motors for actuators 3
- 4. Agents and environments ƒ The agent function maps from percept histories to actions: [f: P* Æ A] ƒ The agent program runs on the physical architecture to produce f ƒ agent = architecture + program 4
- 5. Vacuum-cleaner world ƒ Percepts: location and contents, e.g., [A, Dirty] ƒ Actions: Left, Right, Suck, NoOp 5
- 6. A vacuum-cleaner agent 6 ƒ What is the right function? ƒ Can it be implemented in a small agent program? ƒ Partial tabulation of a simple agent function: Percept sequence Action [A;Clean] Right [A;Dirty] Suck [B;Clean] Left [B;Dirty] Suck [A;Clean], [A;Clean] Right [A;Clean], [A;Dirty] Suck …. ….
- 7. Rational agents ƒ 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 ƒ Performance measure: An objective criterion for success of an agent's behavior ƒ E.g., the performance measure of the vacuum-cleaner agent example could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc. 7
- 8. Rational agents ƒ 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
- 9. Rational agents ƒ Rationality is distinct from omniscience (all-knowing with infinite knowledge) ƒ Does rationality mean perfection? ƒ Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration) ƒ An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt) 9
- 10. Task Environment ƒ PEAS: Performance measure, Environment, Actuators, and Sensors ƒ Must first specify the task environment for intelligent agent design ƒ Consider a more complex example: the task of designing an automated taxi driver: ƒ Performance measure ƒ Environment ƒ Actuators ƒ Sensors 10
- 11. PEAS ƒ Must first specify the setting for intelligent agent design ƒ Performance measure: Safe, fast, legal, comfortable trip, maximize profits ƒ Environment: Roads, other traffic, pedestrians, customers, stray animals, road works, … ƒ Actuators: Steering wheel, accelerator, brake, signal, horn, voice synthesizer or display screen, … ƒ Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard or microphone 11
- 12. PEAS Agent: Internet shopping agent ƒ Performance measure?? price, quality, appropriateness, efficiency ƒ Environment?? current and future WWW sites, vendors, shippers ƒ Actuators?? display to user, follow URL, fill in form ƒ Sensors?? HTML pages (text, graphics, scripts) 12
- 13. PEAS Agent: Internet shopping agent ƒ Performance measure?? price, quality, appropriateness, efficiency ƒ Environment?? current and future WWW sites, vendors, shippers ƒ Actuators?? display to user, follow URL, fill in form ƒ Sensors?? HTML pages (text, graphics, scripts) 13
- 14. Types of Task Environments ƒ Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time. ƒ 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) ƒ 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. 17
- 15. Environment types ƒ 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) ƒ Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. ƒ Single agent (vs. multiagent): An agent operating by itself in an environment. 18
- 16. Environment types: Examples 19 Crossword puzzle Chess with clock Internet shopping Taxi Driver Observable Yes Yes! No No Deterministic Yes Strategic Partly No Episodic No No No No Static Yes Semi Semi No Discrete Yes Yes Yes No Single-agent Yes No Yes!! No ƒ Are the answers exact? ƒ The environment type largely determines the agent design ƒ The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent
- 17. Agent functions and programs ƒ An agent is completely specified by the agent function mapping percept sequences to actions ƒ One agent function (or a small equivalence class) is rational ƒ The aim is to design the agent program that implement the rational agent function concisely ƒ This program shall run on a computing device with sensors and actuators: architecture 20
- 19. Table-driven to agent construction ƒ Is it practical? ƒ Consider the automated taxi ƒ 30 frames/sec; 640x480 pixels with 24 bits for color ƒ The size of the lookup table > 10250B entries!!! ƒ Drawbacks: ƒ Huge table ƒ Take a long time to build the table ƒ No autonomy ƒ Even with learning, need a long time to learn the table entries 22
- 20. Agent types ƒ Four basic types in order of increasing generality: ƒ Simple reflex agents ƒ Model-based reflex agents ƒ Goal-based agents ƒ Utility-based agents All these can be turned into learning agents 23
- 21. Simple reflex agents 24 function Reflex-Vacuum-Agent( [location,status]) re- turns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left
- 22. Model-based reflex agents 25 It keeps track of the current state of the world using an internal model
- 23. Goal-based agents 26 It keeps track of the current state of the world and a set of goals to achieve.
- 26. Summary ƒ Agents interact with environments through actuators and sensors ƒ The agent function describes what the agent does in all circumstances ƒ The performance measure evaluates the environment sequence ƒ A perfectly rational agent maximizes expected performance ƒ Agent programs implement (some) agent functions ƒ PEAS descriptions define task environments ƒ Environments are categorized along several dimensions: ƒ observable? deterministic? episodic? static? discrete? single-agent? ƒ Several basic agent architectures exist: ƒ reflex, model-based, goal-based, utility-based 29