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Intelligent Agent Informatics Engineering Study Program School of Electrical Engineering and Informatics Institute of Technology Bandung
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43467316 Contoh SKPL Sistem Penjual Minuman Otomatis

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Page 1: 43467316 Contoh SKPL Sistem Penjual Minuman Otomatis

Intelligent Agent

Informatics Engineering Study ProgramSchool of Electrical Engineering and Informatics

Institute of Technology Bandung

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Outline

Review

Agents & Environment

Rationality

PEAS (Performance Measures, Environment, Actuators, Sensors)

Environment types

Agent types

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Review• Berdasarkan keempat pendekatan IB, tentukan pendekatan yang

digunakan pada aplikasi/ teknologi berikut ini, ataukah aplikasi

tersebut tidak menggunakan pendekatan inteligensi buatan.

Jelaskan dengan singkat jawaban anda.

– NuPIC, platform perangkat lunak yang berbasiskan pada model

struktur dan operasi pada neocortex (bagian pada otak

mamalia).

– PXDES, aplikasi yang melakukan diagnosis X-ray untuk

penentuan pneumoconiosis (penyakit paru-paru yang

disebabkan oleh penghisapan debu).

– Pc-Nqthm, aplikasi „proof-checker‟ yang berlandaskan pada teori

automated reasoning, berdasarkan aturan formal logika.

– AceMoney, aplikasi yang membantu mengorganisasikan dan

mengatur keuangan individu.

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Agent & Environment

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Environment

Agent

Percepts

Actions

sensors

actuators

?

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Agents

[1]: Software that gathers information about an environment and takes actions based on that information.

A robot

A factory

A web shopping program

… [2]: computer system that is situated in some

environment, and that is capable of autonomous action in this environment in order to meet its design objectives

Computational agents that behave autonomously

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Agents• Anything that can be viewed as perceiving its

environment through sensors and acting upon that

environment through effectors.

– A robot

– A factory

– A web shopping program

– …

Computational agents that behave autonomously

• 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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The Agent & the Environment

• How do we begin to formalize the problem of building an

agent?

– Make a dichotomy between the agent and the environment

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Vacuum-cleaner world

• Percepts: location and contents, e.g.,

[A,Dirty]

• Actions: Left, Right, Suck, NoOp

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World Model

• A – the action space

• P – the percept space

• Define:

– S – internal state [may not be visible to agent]

– Perception function: S P

– World dynamics: S x A S

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Perception Function

World Dynamics

ap

s

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Agent Design

• U – utility function: S real (or S* real)

• The agent design problem: Find P* A

– mapping of sequences of percepts to actions

– maximize the utility of the resulting sequences of

states (each action maps from one state to next state)

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Planning Agent Policy

• Planning is explicitly considering future consequences of actions in order to choose the best one.

• So, planning is the process of generating possible sequences of actions, simulating their consequences, picking which is the best and committing to one of these actions.

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ai that leadss

a1

aa2

a3

u

u

u

u

u

u to max U

s1

s2

s3

a4

a5

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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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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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Rationality• A rational agent takes actions it believes will achieve its goals.

– Assume I don‟t like to get wet, so I bring an umbrella. Is that rational?

– Depends on the weather forecast and whether I‟ve heard it. If I‟ve heard the forecast for rain (and I believe it) then bringing the umbrella is rational.

• Rationality ≠ omniscience

– Assume the most recent forecast is for rain but I did not listen to it and I did not bring my umbrella. Is that rational?

– Yes, since I did not know about the recent forecast!

• Rationality ≠ success

– Suppose the forecast is for no rain but I bring my umbrella and I use it to defend myself against an attack. Is that rational?

– No, although successful, it was done for the wrong reason.

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Limited Rationality• There is a big problem with our definition

of rationality

• The agent might not be able to compute

the best action (subject to its beliefs and

goals).

• So, we want to use limited rationality:

“acting in the best way you can subject to

the computational constraints that you

have”.

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Limited Rationality (2)• The (limited rational) agent design problem:

Find P* A

– mapping of sequences of percepts to actions

– maximizes the utility of the resulting sequences of

states

– subject to our computational constraints

• To design an agent specify PEAS

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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: 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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Another Example of PEAS

Medical diagnosis system Agent

• 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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Another Example of PEAS

Part-picking robot Agent

• 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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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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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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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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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

• We can use table lookup that maps sequence of

percepts to action

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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

• Alternatives reflex agent: agent respond very

flexible to a very broad range of stimuli

– Drawback: coulds not store the response

• Next: Types of agent based

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Agent types

Four basic types :

• Simple reflex agents

• Model-based reflex agents

• Goal-based agents

• Utility-based agents

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Simple reflex agents

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Model-based reflex agents

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Goal-based agents

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Utility-based agents

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Learning agents (later on…)

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THANK YOU