comp 4640 intelligent and interactive systems intelligent agents
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COMP 4640Intelligent and Interactive Systems
Intelligent Agents
Chapter 2
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.
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)
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
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
PEAS
Agent: Medical diagnosis systemPerformance measure: Healthy patient,
minimize costs, lawsuitsEnvironment: Patient, hospital, staffActuators: Screen display (questions,
tests, diagnoses, treatments, referrals)Sensors: Keyboard (entry of symptoms,
findings, patient's answers)
PEAS
Agent: Part-picking robotPerformance measure: Percentage of
parts in correct binsEnvironment: Conveyor belt with parts,
binsActuators: Jointed arm and handSensors: Camera, joint angle sensors
PEAS
Agent: Interactive English tutorPerformance measure: Maximize student's
score on testEnvironment: Set of studentsActuators: Screen display (exercises,
suggestions, corrections)Sensors: Keyboard
Structure of Intelligent Agents
The objective of AI is the design and application of agent programs that implement mappings between percepts and actions
An agent can be viewed as a program that is developed to run on a particular architecture (some computing device).
The architecture: makes percepts available, runs the agent program, sends actions to the effectors
Types of Agent Programs
Intelligent systems are typically composed of a number of intelligent agents.
Each agent interacts (directly or indirectly) with one or more aspects of an environment.
This type of agent interaction is similar to what we see in sports, business, and other organizations that are composed of a number of different agents with different responsibilities working together for the common good.
Environment types
There are a number of different agent programs; however, many can be classified as one of the following:
Agent Environments Fully vs. Partially Observable (Accessible vs.
inaccessible) Deterministic vs. Stochastic (non-deterministic) Episodic vs. Sequential (non-episodic) Static vs. dynamic Discrete vs. continuous
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.
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.
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 NoSingle 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
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
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
Agent types
Four basic types in order of increasing generality:
Simple reflex agentsModel-based reflex agentsGoal-based agentsUtility-based agents
Simple reflex agents
COMP-4640 Intelligent & Interactive SystemsSimple Reflex Agent I
Performance Measure:Get to the bowl
Percept Sequence:(x,y) coordinates
Agent’s Knowledge of the Environment:No Knowledge
Set of Actions:0 1 27 c 36 5 4
Rule Base
R01: If at(5,0) action(2)R02: If at(6,1) action(2)R03: If at(7,2) action(2)R04: If at(8,3) action(1)R05: If at(8,4) action(1)R06: If at(8,5) action(1) R07: If at(8,6) action(0)R08: If at(7,7) action(0)R09: If at(6,8) action(0)R10: If at(5,9) action(0)
COMP-4640 Intelligent & Interactive SystemsSimple Reflex Agent II
Performance Measure:Get to the bowl
Percept Sequence:(x,y) coordinates
Agent’s Knowledge of the Environment:No Knowledge
Set of Actions:0 1 27 c 36 5 4,
MTB (Move Towards Bowl)
COMP-4640 Intelligent & Interactive SystemsSimple Reflex Agent II
Rule Base
R01: If MTB(x) clear(x) action(x) remove(lastMove(y)) assert(lastMove(x))R02: If MTB(x) clear(x) assert(escape_phase)R03: If MTB(x) clear(x) escape_phase action(x) remove(lastMove(y)) remove(escape_phase)
assert(lastMove(x))
R04: If escape_phase lastMove(5) clear(1) action(1) retract(lastMove(_)) assert(lastMove(1))R05: If escape_phase lastMove(7) clear(3) action(3) retract(lastMove(_)) assert(lastMove(3))R06: If escape_phase lastMove(1) clear(5) action(5) retract(lastMove(_)) assert(lastMove(5))R07: If escape_phase lastMove(3) clear(7) action(7) retract(lastMove(_)) assert(lastMove(7))
COMP-4640 Intelligent & Interactive SystemsSimple Reflex Agent II
Rule Base
R01: If MTB(x) clear(x) action(x) remove(lastMove(y)) assert(lastMove(x))R02: If MTB(x) clear(x) assert(escape_phase)R03: If MTB(x) clear(x) escape_phase action(x) remove(lastMove(y)) remove(escape_phase)
assert(lastMove(x))
R04: If escape_phase lastMove(5) clear(1) action(1) retract(lastMove(_)) assert(lastMove(1))R05: If escape_phase lastMove(7) clear(3) action(3) retract(lastMove(_)) assert(lastMove(3))R06: If escape_phase lastMove(1) clear(5) action(5) retract(lastMove(_)) assert(lastMove(5))R07: If escape_phase lastMove(3) clear(7) action(7) retract(lastMove(_)) assert(lastMove(7))
Model-based reflex agents
Goal-based agents
COMP-4640 Intelligent & Interactive Systems
Goal-Based Agent
Performance Measure:Get to the Agent’s Knowledge bowl
Percept Sequence:(x,y) coordinates
Agent’s Knowledge of the Environment:No Knowledge
Set of Actions:0 1 27 c 36 5 4,
GeneratePath
Rule Base
R01: If path_ok GeneratePath assert(path_ok)R02: If path_ok getMove(x,y,a) clear(a) action(a)R03: If path_ok getMove(x,y,a) clear(a) retract(path_ok) retract(getMove(c,d,e))
Utility-based agents
COMP-4640 Intelligent & Interactive Systems
Utility-Based Agent
Performance Measure:Get to the bowl Using the Shortest Path
Percept Sequence:(x,y) coordinates
Agent’s Knowledge of the Environment:No Knowledge
Set of Actions:0 1 27 c 36 5 4,
GeneratePath
Rule Base
R01: If path_ok GeneratePath(UF) assert(path_ok)R02: If path_ok getMove(x,y,a) clear(a) action(a)R03: If path_ok getMove(x,y,a) clear(a) retract(path_ok) retract(getMove(c,d,e))
Learning agents
Learning Agent Examples
•Interactive Animated Pedagogical Agents
Microsoft agent
Why people hate clippy?
Intelligent tutoring systems
Agents on Websites with characters to guide usersmySimon.combuy.comextempo.comananova.comIkea.com
Collaborative Agents
0
5000
Collaborative Agents
10000
Collaborative Agents
0
Collaborative Agents
500
Collaborative Agents
3000
Collaborative Agents
Collaborative Agent Examples
Collaborative AgentsCMUAdvanced Mechantronics Lab
More Agent Examples Virtual Humans & Conversational Agents
Conversational Agent Applications
Virtual Patient Audio
Virtual Pediatric Patient Video
Video Just Talk
Sample Animations Video
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