comp 4640 intelligent and interactive systems intelligent agents

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



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))

Model-based reflex agents

Goal-based agents

COMP-4640 Intelligent & Interactive Systems

Goal-Based Agent

Performance Measure:Get to the Agent’s Knowledge bowl




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




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

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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

comp 4640 intelligent and interactive systems intelligent agents

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