Agent Technology

• Expert System (ES) vs Agent

• Definition

• Types of Agents

Expert Systems• Rule Based

• Case Based

• Knowledge system separate from inference engine

• Pros De-coupling of the system knowledge from the

inference mechanism supports easier maintenance and management of both

System architecture can be readily re-used for different applications by, for instance, using different FACTS and RULES

Financial/Engineering/IS

Expert Systems• Cons/Limitations

– Information Brittleness: If the environmental input is outside of the expert system's fact and/or rule-base scope it does nothing (quits)

• Set of Rules: User wants something outside of fixed scope

• Exception handling: Out of Scope

– Isolation: An expert system is stand-alone i.e., does not enter into "collaboration with other expert systems”

• Inefficient: Interrelated but no communication across ES systems

• Not enterprise level

– Example: ES 1 Finance/ES 2 Accounting

– Static Behavior: The Level of Behavior (LOB) of the expert system is static, i.e., it does not improve over time or use

– Incapable of Learning

• Should be able to learn from mistakes & improve

How to overcome the ES limitations• How to overcome brittleness:

– Support graceful degradation of performance by initiating and maintaining dialog with the environment (users)

• Instead of just stopping, try to increase scope gradually by including user into system

– Expand fact and rule bases automatically through learning mechanisms

• Introduce new variable, ok first time, next time will recognize

• How to overcome isolation:

– Add mechanisms for coordination and collaboration

• ES 1 Finance/ES 2 Accounting

– Permit communication with each other at enterprise level

How to overcome the ES limitations

• Static:

– provide learning mechanisms

Agent

The augmentation of expert system capabilities to overcome limitations leads, in an evolutionary fashion, to the creation of intelligent agents Sharing knowledge

Increase scope

What is an Agent

• A computer system that – has goals, perceptors, and effectors– decides autonomously which action to take

in a given situation

What is an Agent• Use networking to share data across enterprise• No longer independent• Becomes an “Agent Community” also includes “Human User”• Resolves isolation, brittleness, static

ES 1Finance

Fixed Scope

ES 3

Fixed Scope

ES 2Accounting

Fixed Scope

ES 4

Fixed Scope

Hub

OOP Similarity

• OOP Class uses Method invocation• Agent happens dynamically• OOP

– (Method invoked) + (Rich Symantec/syntax) = Agent– OOP (not intelligent) + ES (intelligent) = Agent– Object just performs function– 1) Agent can calculate or can reject (can think/then perform)– 2) Communication ability

Methodology - Action

Intelligent

Methodology - Action

Not intelligent

Class Def - Public/PrivateClass Def - Public/Private

Methodology

Class Def

The Class is the Intelligent Object

The OOP resides on top of the ES and creates an AI Agent

Definition of an Agent• Autonomy = Intelligent

– Can reason/can think/learns from previous experience and can apply to new experience

– an agent operates without the direct intervention of humans or others and has control over its own actions

– an agent is able to exhibit goal-directed behavior by taking initiatives

– Knows what problem to solve (goal directed behavior)• Social ability

– an agent interacts with other agents via Agent Communication Language (ACL)

• Symantec/Syntax of Language• Adaptive

– an agent learns to improve its behavior

Agents vs ES

• Personalized– agents -> different actions– ES -> same actions

• active, autonomous– agents - on their own– ES - passively answer

• adaptive– agent - learn and change– ES - remain static

How is Agent different from other Software

• Personalized, customized

• Proactive, takes initiatives

• Autonomous

• Adaptive

Types of Agents

Agents

Architecture Functionality Mobility

Deliberative Reactive Interface Information/ Mobile StationaryAgent Agent Agent Internet Agent Agent

Agent

Deliberate vs Reactive

• A deliberative agent has an internal representation of the sequence of actions necessary to achieve a goal given or an event triggered (a Pre-plan/Priori plan).

• Reactive agents do not store a priori plan of the actions. No internal representation of pre-plan exists within any of the reactive agents and, hence, the plans has to emerge upon an event through collaboration.

Interface vs Information

• Interface agents migrate from the direct command metaphor to one that delegates some of the tasks to the agents in order to accommodate novice users.

• Information (Internet) agents are designed to deal with the problem of information overload and the general issues of information management in Internet.

Mobile vs Stationary

• Mobile agents are able to roam the network such as WWW, interacting with foreign hosts, and performing the various duties assigned at a remote site

• Stationary agents stay at the client or at the server.

Why Mobile Agent?

• Reduces network traffic

• Shares load among machines

• Go to the data if the data can’t come to you

• User may have only infrequent connection to network