1. Artificial Intelligence
Chapter 2 Agents
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2. What is an (Intelligent) Agent?
An over-used, over-loaded, and misused term. Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals.
Perception
Sensors receive input from environment
Keyboard clicks
Camera data
Action
Actuators impact the environment
Move a robotic arm
Generate output for computer display
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3. Perceptual inputs at an instant
Perception
Percept
Perceptual inputs at an instant
May include perception of internal state
Percept Sequence
Complete history of all prior percepts
Do you need a percept sequence to play Chess?
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4. 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
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5. Intelligent Agents and Artificial Intelligence
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.
Distributed decision-making and control
Challenges:
Action selection: What next action to choose
Conflict resolution
sensors
effectors
Agency
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6. We can split agent research into two main strands:
Agent Types
We can split agent research into two main strands:
Distributed Artificial Intelligence (DAI) – Multi-Agent Systems (MAS) (1980 – 1990)
Much broader notion of "agent" (1990’s – present)
interface, reactive, mobile, information
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7. Intelligent Agents
Chapter 2

8. Outline Agents and environments Rationality
PEAS (Performance measure, Environment, Actuators, Sensors)
Environment types
Agent types
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9. Agents
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Human agent: eyes, ears, and other organs for sensors; hands,
legs, mouth, and other body parts for actuators
Robotic agent: cameras and infrared range finders for sensors;
various motors for actuators
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10. 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
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11. Vacuum-cleaner world Percepts: location and contents, e.g., [A,Dirty]
Actions: Left, Right, Suck, NoOp
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12. A vacuum-cleaner agent
\input{tables/vacuum-agent-function-table}
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13. 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.
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14. 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.
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15. Rational agents
Rationality is distinct from omniscience (all-knowing with infinite knowledge)
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)
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16. 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
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17. 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
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18. 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)
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19. 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
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20. 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
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21. 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.
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22. 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.
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23. 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
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24. 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
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25. Table-lookup agent \input{algorithms/table-agent-algorithm} 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
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26. Agent program for a vacuum-cleaner agent
\input{algorithms/reflex-vacuum-agent-algorithm}
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27. Agent types Four basic types in order of increasing generality:
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
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28. Simple reflex agents
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29. Simple reflex agents
\input{algorithms/d-agent-algorithm}
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30. Model-based reflex agents
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31. Model-based reflex agents
\input{algorithms/d+-agent-algorithm}
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32. Goal-based agents
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33. Utility-based agents
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34. Learning agents
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35. Rational Agents How to design this? Sensors percepts Environment ?
actions
Effectors
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36. Interacting Agents Collision Avoidance Agent (CAA)
Goals: Avoid running into obstacles
Percepts ?
Sensors?
Effectors ?
Actions ?
Environment: Freeway
Lane Keeping Agent (LKA)
Goals: Stay in current lane
Percepts ?
Sensors?
Effectors ?
Actions ?
Environment: Freeway
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37. Interacting Agents 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
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38. Conflict Resolution by Action Selection Agents
Override: CAA overrides LKA
Arbitrate: if Obstacle is Close then CAA else LKA
Compromise: Choose action that satisfies both agents
Any combination of the above
Challenges: Doing the right thing
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39. The Right Thing = The Rational Action
Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date
Rational = Best ?
Rational = Optimal ?
Rational = Omniscience ?
Rational = Clairvoyant ?
Rational = Successful ?
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40. The Right Thing = The Rational Action
Rational Action: 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 (incl.
Rational  Omniscience(全知) its constraints)
Rational  Clairvoyant(有超人力)
Rational  Successful
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41. Behavior and performance of IAs
Perception (sequence) to Action Mapping: f : P*  A
Ideal mapping: specifies which actions an agent ought to take at any point in time
Description: Look-Up-Table, Closed Form, etc.
Performance measure: a subjective measure to characterize how successful an agent is (e.g., 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?
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42. Distance Action Look up table 10 No action 5 Turn left 30 degrees 2
Stop
agent
obstacle
sensor
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43. Output (degree of rotation) = F(distance)
Closed form
Output (degree of rotation) = F(distance)
E.g., F(d) = 10/d (distance cannot be less than 1/10)
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44. How is an Agent different from other software?
Agents are autonomous, that is, they act on behalf of the user
Agents contain some level of intelligence, from fixed rules to learning engines that allow them to adapt to changes in the environment
Agents don't only act reactively, but sometimes also proactively
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45. How is an Agent different from other software?
Agents have social ability, that is, they communicate with the user, the system, and other agents as required
Agents may also cooperate with other agents to carry out more complex tasks than they themselves can handle
Agents may migrate from one system to another to access remote resources or even to meet other agents
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46. Characteristics Environment Types Accessible vs. inaccessible
Deterministic vs. nondeterministic
Episodic vs. nonepisodic
Hostile vs. friendly
Static vs. dynamic
Discrete vs. continuous
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47. Characteristics Environment Types Accessible vs. inaccessible
Sensors give access to complete state of the environment.
Deterministic vs. nondeterministic
The next state can be determined based on the current state and the action.
Episodic vs. nonepisodic (Sequential)
Episode: each perceive and action pairs
The quality of action does not depend on the previous episode.
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48. Characteristics Environment Types Hostile vs. friendly
Static vs. dynamic
Dynamic if the environment changes during deliberation
Discrete vs. continuous
Chess vs. driving
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49. Environment types Environment Accessible Deterministic Episodic Static
Discrete
Operating System
Virtual
Reality
Office Environment
Mars
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50. Environment types Environment Accessible Deterministic Episodic Static
Discrete
Operating System
Yes No
Virtual
Reality
Office Environment
Mars
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51. Environment types Environment Accessible Deterministic Episodic Static
Discrete
Operating System
Yes No
Virtual
Reality
Yes/no
Office Environment
Mars
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52. Environment types Environment Accessible Deterministic Episodic Static
Discrete
Operating System
Yes No
Virtual
Reality
Yes/no
Office Environment
Mars
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53. Environment types Environment Accessible Deterministic Episodic Static
Discrete
Operating System
Yes No
Virtual
Reality
Yes/no
Office Environment
Mars
Semi
The environment types largely determine the agent design.
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54. Structure of Intelligent Agents
Agent = architecture + program
Agent program: the implementation of f : P*  A, the agent’s perception-action mapping function Skeleton-Agent(Percept) returns Action memory  UpdateMemory(memory, Percept) Action  ChooseBestAction(memory) memory  UpdateMemory(memory, Action) return Action
Architecture: a device that can execute the agent program (e.g., general-purpose computer, specialized device, beobot, etc.)
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55. Using a look-up-table to encode f : P*  A
Example: Collision Avoidance
Sensors: 3 proximity sensors
Effectors: Steering Wheel, Brakes
How to generate?
How large?
How to select action?
obstacle
sensors
agent
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56. Using a look-up-table to encode f : P*  A
Example: Collision Avoidance
Sensors: 3 proximity sensors
Effectors: Steering Wheel, Brakes
How to generate: for each p  Pl  Pm  Pr generate an appropriate action, a  S  B
How large: size of table = #possible percepts times # possible actions = |Pl | |Pm| |Pr| |S| |B| E.g., P = {close, medium, far}3 A = {left, straight, right}  {on, off} then size of table = 27*3*2 = 162
How to select action? Search.
obstacle
sensors
agent
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57. Reflex agents with internal states Goal-based agents
Agent types
Reflex agents
Reflex agents with internal states
Goal-based agents
Utility-based agents
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58. Reflex agents with internal states
Agent types
Reflex agents
Reactive: No memory
Reflex agents with internal states
W/o previous state, may not be able to make decision
E.g. brake lights at night.
Goal-based agents
Goal information needed to make decision
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59. Utility-based agents Agent types
How well can the goal be achieved (degree of happiness)
What to do if there are conflicting goals?
Speed and safety
Which goal should be selected if several can be achieved?
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60. Reflex agents
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61. 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?
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62. Reflex agents w/ state
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63. Goal-based agents
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64. Utility-based agents
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65. Mobile agents Programs that can migrate from one machine to another.
Execute in a platform-independent execution environment.
Require agent execution environment (places).
Mobility not necessary or sufficient condition for agenthood.
Practical but non-functional advantages:
Reduced communication cost (eg, 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)
Applications:
Distributed information retrieval.
Telecommunication network routing.
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66. Programs that can migrate from one machine to another.
Mobile agents
Programs that can migrate from one machine to another.
Execute in a platform-independent execution environment.
Require agent execution environment (places).
Mobility not necessary or sufficient condition for agenthood.
A mail agent
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67. Practical but non-functional advantages:
Mobile agents
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)
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68. Applications: Mobile agents Distributed information retrieval.
Telecommunication network routing.
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69. Information agents Manage the explosive growth of information.
Manipulate or collate information from many distributed sources.
Information agents can be mobile or static.
Examples:
BargainFinder comparison shops among Internet stores for CDs
FIDO the Shopping Doggie (out of service)
Internet Softbot infers which internet facilities (finger, ftp, gopher) to use and when from high-level search requests.
Challenge: ontologies for annotating Web pages (eg, SHOE).
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70. Agent Types: Reflex, state-based, goal-based, utility-based
Summary
Intelligent Agents:
Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals.
PAGE (Percepts, Actions, Goals, Environment)
Described as a Perception (sequence) to Action Mapping: f : P*  A
Using look-up-table, closed form, etc.
Agent Types: Reflex, state-based, goal-based, utility-based
Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date
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