Software Agents: An Overview by Hyacinth S. Nwana and Designing 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 to 1996 –Deliberative Agents –Macro Issues –Research and Development Strand 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