Lecture 1 - Intelligent Agent

7/28/2019 Lecture 1 - Intelligent Agent

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Ning Xiong

Mlardalen University

Lecture 1: Intelligent Agents

Outline

What is the concept of agent?

Rationality of agent behavior Task environments for agents

Types of intelligent agents


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What is Agent

An agent is anything that can be viewed as

perceiving its environment through sensors andacting upon that environment through actuators

to change the states of the environment

Agent Examples

Human agent

Sensors: eyes, ears, and other organs for feeling;Actuators: hands, legs, mouth, and other body parts

Robotic agentSensors: cameras, sonar, and infrared range finders

Actuators: robotic arm, various motors


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Agent Function and Program

The agentfunction maps from percept histories

to actions:

[f: P*A]

Agent behavior decided by the agent function

The agentprogram runs on the physicalarchitecture (computing device) to implement

the agent function f

Rational agents

An rational agent should strive to "do the rightthing", based on what it can perceive and theactions it can perform. The right action is the one

that will cause the agent to be most successful


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Why Not Saying Optimal Agents

The inherent uncertainty in real-world makes the optimal

solution not exist or hardly to judge

1. The imperfect or incomplete knowledge about the

world makes it impossible to judge optimality of acts

2. The chance factor in real-world makes outcome

stochastic and uncertain.

3. The consequence of an action by an agent is affected

by actions of other agents

Rational Choice

O11

O12

O21

O22

A1

A2

p11

p12

p21

p22

Decisionnode

EU(A1)=u11p11+u12p22EU(A2)=u21p21+u22p22

A rational agent will prefer act A1 to act A2 provided that

EU(A1)>EU(A2)


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Agent Environment Types

Fully observable (vs. partially observable): An agent'ssensors give it access to the complete state of theenvironment at each point in time.

Deterministic (vs. stochastic): The next state of theenvironment is completely determined by the currentstate and the action executed by the agent.

Environment types

Static (vs. dynamic): The environment is

unchanged while an agent is deliberating. (The

environment is semidynamic if the environmentitself 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 Deterministic Deterministic No

Static Semi Yes No

Discrete Yes Yes No

Single agent No No No

Intrinsically the real world is partially observable,stochastic,, dynamic, continuous, multi-agent

Agent types

Four types of agents in order of increasing

generality:

Simple reflex agents

Model-based reflex agents

Goal-based agents

Learning agents

The course covers key techniques for designing

various types of intelligent agents


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Simple reflex agents

Use if-then rules to define mapping from percepts to actions

Behavior-based intelligence without reasoning (in robotics)

Lecture 3: Fuzzy rule-based control for decision making

Model-based reflex agents

World model

When the world state is not fully observable, use the model of

the environment to estimate the current state given the sensor

observations


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Model-based state estimation

Consider a process monitoring problem where we want to estimate the state

of process given sensor measurements. The available model for the

process is as follows

where x denotes the process state, y the sensor measurements.

Bayesian-based filtering methods such as Kalman filtering and particle

filtering to update and refine state estimates.

)1()1()( kwkFxkx

)()()( kvkxHky

One lecture in the course, which will discuss

how agents can utilize multi-sensor data andother information to better estimate the

hidden states of the environment and to

acquire better situation awareness

Multi-sensor data fusion


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Goal-based agents

Analyze and predict resulting outcomes of possible actions,

choose the most promising action to satisfy the goal

Goal-Based Agents

Lecture 5: decision theory and analysis for

helping selection of rational actions

Lecture 6: How to make decisions by

exploiting previous experiences

Lecture 8: How to find a set of interesting,

nondominated solutions in the continuous

space with multiple conflicting objectives


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Learning agents

Learning element: modify agent functions in decision making

Decision making: agent function to select external actions

Critic: evaluate how well agent is doing

Environment

Sensors

Actuators

Decision

making

Critic

Learning

element

feedback

Learning Mechanisms in DVA406

Fuzzy adaptive control: on-line modification of fuzzy

decision rules based on performance feedback.

Lecture 4

Other agent learning approaches like reinforcement learning are

given in the Learning Systems course (CDT407).


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Recommended Reference on Agents

Chapter 2: Intelligent agents, in: Artificial

Intelligence: A modern approach, by Stuart

Russel and Peter Norvig, Prentice Hall, 2002.

Documents

Lecture 1 - Intelligent Agent