This document discusses and compares expert systems and agents. It defines expert systems as rule-based or case-based knowledge systems that are separate from their inference engines. While expert systems can be readily reused, they have limitations like being brittle, isolated, and static. Agents overcome these limitations by being autonomous, adaptive systems that can communicate and learn from each other. The document defines agents and describes different types including deliberative and reactive agents, as well as interface, information, mobile, and stationary agents.

1. Agent Technology
• Expert System (ES) vs Agent
• Definition
• Types of Agents

2. 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

3. 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

4. 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

5. How to overcome the ES limitations
• Static:
– provide learning mechanisms

6. 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

7. What is an Agent
• A computer system that
– has goals, perceptors, and effectors
– decides autonomously which action to take
in a given situation

8. 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 1
Finance
Fixed Scope
ES 3
Fixed Scope
ES 2
Accounting
Fixed Scope
ES 4
Fixed Scope
Hub

9. OOP Similarity
The Class is the Intelligent Object
Class Def Class Def - Public/Private - Public/Private
Class Def
• 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
Methodology
The OOP resides on top of the ES and creates an AI Agent

10. 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

11. 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

12. How is Agent different from other
Software
• Personalized, customized
• Proactive, takes initiatives
• Autonomous
• Adaptive

13. Types of Agents
Agents
Architecture Functionality Mobility
Deliberative Reactive Interface Information/ Mobile Stationary
Agent Agent Agent Internet Agent Agent
Agent

14. 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.

15. 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.

16. 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.

17. 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