Software Agents: An Overview by Hyacinth S. Nwana

andDesigning Behaviors for Information

Agents by Keith Decker, Anandeep Pannu, Katia Sycara

and Mike Williamson

Presenters: Wendy Nikiforuk, Rui Lopes, Brad Jones, and Chris Kliewer

February 10, 1999

Software Agents - Outline

• Introduction to Agents & Papers - Chris

• Typologies from Paper One - Brad

• A Framework for Information Agents - Rui

• Conclusions and the Future - Wendy

Designing Behavior For Information Agents

• Frameworks for constructing Agents

• Behavior of basic Information Agents

• WARREN

Software Agents: An Overview

• 2 strands of Agent research

• Strand 1 1977 to 1996– Deliberative Agents– Macro Issues– Research and Development

• Strand 2 1990 to 1996– Diversification of agent types

What is an Agent?

• No clear consensus on a definition

• The term has been over used

• Many physical forms

• A component of SW or HW capable of accomplishing tasks for its user.

Creating the Classes

• Mobility

• Deliberative or Reactive

• Roles

• Primary Attributes– Autonomy– Learning– Cooperation

• Secondary Attributes

A Typology Of Agents

• Collaborative• Interface• Mobile• Reactive• Hybrid• Heterogeneous Systems• Smart• Information / Internet

Collaborative Agents

• Emphasize autonomy and cooperation.

• Whole is greater than sum of the parts.

• promises– flexible solutions to complex problems

• problems– based on deliberative thinking paradigm– communication and stability issues– unclear implementation

Interface Agents

• Emphasize autonomy and learning.

• promises– automation of mundane or regular tasks– essentially an ‘avatar’

• problems– Is learning mechanism valid, competent,

upgradable, defined?– needed or desired?

Mobile Agents

• Agent is a non-static entity.

• promises– better / more efficient use of resources– easily coordinated and flexible asynchronous

system architecture

• problems– few “real world” examples– typical distributed computing problems

(transportation, security, performance, etc.)

Reactive Agents

• No internal, symbolic environmental model.

• Relatively simple & use emergent behavior.

• promises– robust, fault tolerant, flexible, and adaptable

• problems– unclear development methodology– potential scalability and performance issues

Hybrid Agents

• Combination of other agent philosophies.

• Combination is better than singular type.

• promises– combines ‘best’ of agent philosophies– provides focused applicability of agent

• problems– unspecified theories underlying hybrid systems– ad-hoc design

Heterogeneous Agent Systems

• System of different agent types.

• Focused on interoperability between agents.

• promises– provide flexible solutions to complex problems– provides new way approach to old problems

• problems– communication - what language, how, etc.– requires an standard framework

Information Agents

• Information source in support of other agents in RETSINA framework

• Framework encapsulates much of the reusable functionality

• Not a simple API

Functional Overview

• Three conceptual functional parts

– Current Activity And Request Information

– Local Information Database

– Problem Solving Plan Library

Reusable Behaviors

• Approaches to Accomplishing a Goal

• Information Agent Behaviors– Advertising

– Message Polling

– Information Monitoring

– Query Answering

– Cloning

Agent Architecture

• Building Blocks for Agent Behaviors

– Planning• higher level tasks broken down into lower level

primitive actions

– Scheduling

• dynamically decides which primitive action gets run

next

Agent Architecture 2

– Execution Monitoring

• prepares, monitors and completes agent’s next

intended action

– Local Agent Infobase

• local data store defined by an ontology, a set of

attributes, a language, and a schema

Odds and Ends

• Multi-Source Information Agents– One agent assumes responsibility for many

others

• WARREN– Six? information agents

• two stock ticker agents

• news agent

• current and historical sales information agent

• company annual report agent

What Agents Are Not

• Expert Systems

• Modules in distributed Computing– rarely smart– low level messaging– run at symbol level

Societal Issues

• For success in the future, there are several societal issues which must be handled– Privacy– Responsibility– Legal– Ethical– Etiquette– Restricting agents

Conclusions

• Agents can work independently, but more powerful when they work together.

• Truly smart or intelligent agents to not exist

• Fear of agents

• Evolutionary not Revolutionary

• Can exploit diverse and distributed knowledge

Conclusions

• Agents are not a passing fad– ‘agent’ not ‘intelligent agent’– have papers reviewed by a colleague– do not oversell the domain– be critical of the progress