©intelligent agent technology and application, 2006, ai lab nju intelligent agent technology and...

©Intelligent Agent Technology and Application, 2006, Ai Lab NJU

Intelligent Agent

Technology and Application

Course overview

and

what is intelligent agent

Sept. 2006©Gao Yang, Ai Lab NJU2

What is intelligent agent

Field that inspired the agent fields?– Artificial Intelligence

Agent intelligence and micro-agent

– Software Engineering Agent as an abstracted entity

– Distributed System and Computer Network Agent architecture, MAS, Coordination

– Game Theory and Economics Agent Negotiation

There are two kinds definition of agent– Often quite narrow– Extremely general Agent

?


General definitions

American Heritage Dictionary– ”... One that acts or has the power or authority to act ... or

represent another”

Russel and Norvig– ”An agent is anything that can be viewed as perceiving its

environment through sensors and acting upon that

environment through effectors.”

Maes, Parrie– ”Autonomous agents are computational systems that

inhabit some complex dynamic environment, sense and act

autonomously in this environment, and by doing so realize

a set of goals or tasks for which they are designed”.


Agent: more specific definitions

Smith, Cypher and Spohrer– ”Let us define an agent as a persistent software entity

dedicated to a specific purpose. ’Persistent’ distinguishes agents from subroutines; agents have their own ideas about how to accomplish tasks, their own agendas. ’Special purpose’ distinguishes them from multifunction applications; agents are typically much smaller.

Hayes-Roth– ”Intelligent Agents continuously perform three functions:

perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions.


Agent: industrial definitions

IBM

– ”Intelligent agents are software entities that carry out some

set of operations on behalf of a user or another program

with some degree of independence or autonomy, and in

doing so, employ some knowledge or representations of

the user’s goals or desires”


Agent: weak notions

Wooldridge and Jennings– An Agent is a piece of hardware or (more commonly) software-

based computer system that enjoys the following properties Autonomy: agents operate without the direct intervention of

humans or others, and have some kind of control over their

actions and internal state;

Pro-activeness: agents do not simply act in response to their

environment, they are able to exhibit goal-directed behavior by

taking the initiative.

Reactivity: agents perceive their environment and respond to

it in timely fashion to changes that occur in it.

Social Ability: agents interact with other agents (and possibly

humans) via some kind of agent-communication language.”


Agent: strong notions

Wooldridge and Jennings– Weak notion in addition to

Mobility: the ability of an agent to move around a

network

Veracity: agent will not knowingly communicate false

information

Benevolence: agents do not have conflicting goals and

always try to do what is asked of it.

Rationality: an agent will act in order to achieve its

goals and will not act in such a way as to prevent its

goals being achieved


Summary of agent definitions

An agent act on behalf user or another entity.

An agent has the weak agent characteristics. (Autonomy, Pro-

activeness, Reactivity, Social ability)

An agent may have the strong agent characteristics. (Mobility,

Veracity, Benevolence, Rationality)


Dear child gets many names…

Many synonyms of the term “Intelligent agent”

– Robots

– Software agent or softbots

– Knowbots

– Taskbots

– Userbots

– ……


Autonomy is the key feature of agent

Examples

– Thermostat

Control / Regulator

Any control system

– Software Daemon

Print server

Http server

Most software daemons

Agent

Envi ronment

Act i onI nput

SensorI nput


Type of environment

An agent will not have complete control over its environment, but have partial control, in that it can influence it.

– Scientific computing or MIS in traditonal computing.

Classification of environment properties [Russell 1995, p49]

– Accessible vs. inaccessible– Deterministic vs. non-deterministic– Episodic vs. non-episodic– Static vs. dynamic– Discrete vs. continuous


Accessible vs. inaccessible

Accessible vs. inaccessible

– An accessible environment is one in which the

agent can obtain complete, accurate, up-to-date

information about the environment’s state. (also

complete observable vs. partial observable)

– Accessible: sensor give complete state of the

environment.

– In an accessible environment, agent needn’t keep

track of the world through its internal state.


Deterministic vs. non-deterministic

Deterministic vs. non-deterministic

– A deterministic environment is one in which any

action has a single guaranteed effect , there is no

uncertainty about the state that will result from

performing an action.

– That is, next state of the environment is

completely determined by the current state and

the action select by the agent.

– Non-deterministic: a probabilistic model could be

available.


Episodic vs. non-episodic

Episodic vs. non-episodic

– In an episodic environment, the performance of

an agent is dependent on a number of discrete

episodes, with no link between the performance

of an agent in different scenarios. It need not

reason about the interaction between this and

future episodes. (such as a game of chess)

– In an episodic environment, agent doesn’t need

to remember the past, and doesn’t have to think

the next episodic ahead.


Static vs. dynamic

Static vs. dynamic

– A static environment is one that can assumed to

remain unchanged expect by the performance of

actions by the agents.

– A dynamic environment is one that has other

processes operating on it which hence changes

in ways beyond the agent’s control.


Discrete vs. continuous

Discrete vs. continuous– An environment is discrete if there are a fixed,

finite number of actions and percepts in it.


Why classify environments

The type of environment largely determines the

design of agent.

Classifying environment can help guide the agent’s

design process (like system analysis in software

engineering).

Most complex general class of environments

– Are inaccessible, non-deterministic, non-

episodic, dynamic, and continuous.


Discuss about environment: Gripper

Gripper is a standard example for probabilistic

planning model

– Robot has three possible actions: paint (P), dry

(W) and pickup (U)

– State has four binary features: block painted,

gripper dry, holding block, gripper clean

– Initial state:

– Goal state:


Intelligent agent vs. agent

An intelligent agent is one that is capable of flexible

autonomous action in order to meet its design

objectives, where flexibility means three things:

– Pro-activeness: the ability of exhibit goal-directed

behavior by taking the initiative.

– Reactivity: the ability of percept the environment,

and respond in a timely fashion to changes that

occur in it.

– Social ability: the ability of interaction with other

agents (include human).


Pro-activeness

Pro-activeness– In functional system, apply pre-condition and post-

condition to realize goal directed behavior.

– But for non-functional system (dynamic system), goal must

remain valid at least until the action complete.

– agent blindly executing a procedure without regard to

whether the assumptions underpinning the procedure are

valid is a poor strategy.

Observe incompletely

Environment is non-deterministic

Other agent can affect the environment


Reactivity

Reactivity

– Agent must be responsive to events that occur in

its environment.

– Building a system that achieves an effective

balance between goal-directed and reactive

behavior is hard.


Social ability

Social ability– Must negotiate and cooperate with others.


Agent vs. object

Object

– Are defined as computational entities that

encapsulate some state, are able to perform

actions, or methods on this state, and

communicate by message passing. Are computational entities.

Encapsulate some internal state.

Are able to perform actions, or methods, to change this

state.

Communicate by message passing.


Agent and object

Differences between agent and object

– An object can be thought of as exhibiting

autonomy over its state: it has control over it. But

an object does not exhibit control over it’s

behavior.

– Other objects invoke their public method. Agent

can only request other agents to perform actions.

– “Objects do it for free, agents do it for money.”

– (implement agents using object-oriented

technology)……Thinking it.Thinking it.


Agent and object

– In standard object model has nothing whatsoever to say about how to build systems that integrate reactive, pro-active, social behavior.

– Each has their own thread of control. In the standard object model, there is a single thread of control in the system.

– (agent is similar with an active object.)

– Summary, Agent embody stronger notion of autonomy than object Agent are capable of flexible behavior Multi-agent system is inherently multi-threaded


Agent and expert system

Expert system

– Is one that is capable of solving problems or

giving advice in some knowledge-rich domain.

The most important distinction

– Expert system is disembodied, rather than being

situated.

– It do not interact with any environment. Give

feedback or advice to a third part.

– Are not required to interact with other agents.


Example of agents

MobileCustomer

Agent(Peer)

Agent(Peer)

Agent(Peer)

Agent(Peer)

M obileC ustom er

M obileC ustom er

M obileC ustom er


Distributed Artificial Intelligence (DAI)

DAI is a sub-field of AI

DAI is concerned with problem solving where agent

s solve (sub-) tasks (macro level)

Main area of DAI

– Distributed problem solving (DPS) Centralized Control and Distributed Data (Massively

Parallel Processing)

– Multi-agent system (MAS) Distributed Control and Distributed Data (coordination

crucial)

Some historiesSome histories


DAI is concerned with……

Agent granularity (agent size) Heterogeneity agent (agent type) Methods of distributing control (among agents) Communication possibilities

MAS– Coarse agent granularity– And high-level communication

Di st r i but edComput i ng

Ar t i fi ci alI nt el l i gence

Di st r i but edAI

Mul t i - AgentSyst ems

Di st r i but edProbl emSol vi ng


DAI is not concerned with……

Issues of coordination of concurrent processes at

the problem solving and representational level.

Parallel computer architecture, parallel

programming languages or distributed operation

system.

No semaphores, monitors or threads etc.

Higher semantics of communication (speech-act

level)


Motivation behind MAS

To solve problems too large for a centralized agent

– E.g. Financial system

To allow interconnection and interoperation of

multiple legacy system

– E.g. Web crawling

To provide a solution to inherently distributed

system

To provide a solution where expertise is distributed

To provide conceptual clarity and simplicity of

design


Benefits of MAS

Faster problem solving

Decreasing communication

– Higher semantics of communication (speech-act

level)

Flexibility

Increasing reliability


Heterogeneity degrees in MAS

Low

– Identical agents, different resources

Medium

– Different agent expertise

High

– Share only interaction protocol (e.g. FIPA or

KQML)


Cooperative and self-interested MAS

Cooperative– Agents designed by interdependent designers

– Agents act for increased good of the system (i.e. MAS)

– Concerned with increasing the systems performance and

not the individual agents

Self-interested– Agents designed by independent designer

– Agents have their own agenda and motivation

– Concerned with the benefit of each agent (’individualistic’)

– The latter more realistic in an Internet-setting?


Our categories about MAS

Cooperation

– Both has a common object

Competitive

– Each have different objects which are contradicto

ry.

Semi-competitive

– Each have different objects which are conflictive,

but the total system has one explicit (or implicit)

objectThe first now is known as TEAMWORK.


Distributed AI perspectives

Perspecti ves

Agent

Grou

p

Desi gnerSpeci fi c Approaches

Cooperati on

Coordi nat i on

Nego

tiat

i on

CoherentBehavi or

Pl anni ng

Di st r i but edAI

Met hodsAna

l ysi s

Desi gn

Tool s

Appl

i cat

i ons

Testbeds

Archi tecture

Reactive

Del iberative

Hybrid

TheoryLanguage


Our Thinking in MAS

Single benefit vs. collective benefit

No need central control

Social intelligence vs. single intelligence

Self-organize system

– Self-form, self-evolve

Intelligence is emergence, not innative

…..


Conclusions of lecture

Agent has general definition, weak definition and

strong definition

Classification of the environment

Differences between agent and intelligent agent,

agent and object, agent and expert system

Multi-agent system is macro issues of agent

systems


Coursework

1. Give other examples of agents (not necessarily intelligent) that you know of. For each, define as precisely as possible:– (a). the environment that the agent occupies, the

states that this environment can be in, and the type of environment.

– (b). The action repertoire available to the agent, and any pre-conditions associated with these actions;

– (c). The goal, or design objectives of the agent – what it is intended to achieve.


Coursework

2. If a traffic light (together with its control system) is considered as intelligent agent, which of agent’s properties should be employ? Illustrate your answer by examples.


Coursework

3. Please determine the environment’s type.

Chess Poker Mine-sweeper

E-shopping

Accessible??

Deterministic??

Episodic??

Static??

Discrete??

©intelligent agent technology and application, 2006, ai lab nju intelligent agent technology and...

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