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Software Agents: An Overview by Hyacinth S. NwanaandDesigning Behaviors for Information Agentsby 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