Intelligent Agents: An Overview

1. Intelligent Agents:An Overview From: Chapter 1, A. Canlayan and C. Harrison, Agent: Sourcebook, Wiley 1997.

2. Contents • Attributes of intelligent agents • End user taxonomy of agents • Intelligent agent applications • Benefits of agents • Business obstacles for agent acceptance • Agent use prediction • Cost of development

3. Background • A definition of an agent: Agent: A person or thing that acts or is capable of acting or is empowered to act for another. The Webster’s New World Dictionary 1970 • Two key attributes are pointed out: • An agent does things. • An agent acts on behalf of someone or something.

4. Background • Intelligent agent that resides on computers always incorporate these two central attributes. • The following definition of an agent will suffice to discuss the business applications of an agent: Software agent: A computing entity that performs user delegated tasks autonomously. • Mail filtering agents, information retrieval agents, and desktop automation agents all fit this definition.

5. Attributes of Intelligent Agents • The agent possesses the following minimal characteristics: • Delegation • Communication skills • Autonomy • Monitoring • Actuation • Intelligence

6. Attributes of Intelligent Agents • The concept of an agent introduces • an indirect management metaphor in a computerized environment • to supplement today‘s mainstream style of direct manipulatioon metaphor via GUI. (Alan, K. (1984). “Computer Software,” Scientific American (March).)

7. Attributes of Intelligent Agents • The origins of agent technology are rooted in • the computational intelligence, • software engineering, and • human interface domain.

8. Neural Networks Objects High-level Event Inferencing Intentional Systems Image and Speech Processing KBS on-line Monitoring Reasoning Theory Intelligent Agents Computational Intelligence Software Engineering Intelligent Tutoring Interactive Experiments Cognitive Engineering User Modeling Human Interface

9. Attributes of Intelligent Agents • Agent model from a user perspective

10. Agent Task Level Skills Knowledge Communication Skills Task A Priori Knowledge Learning With User With Other Agents Information Retrieval Information Filtering Coaching Developer Specified User Specified System Specified Dialog Based Memory Based Neural Network Case-Based Neural Expert Interface Speech Social Interagent Communication Language

11. End User Taxonomy of Agents • It is helpful to define the agent environment, task and arachitecture. • Environment: Agents are designed to performed in a particular environment such as an OS, an application, and a computer network. • Internet agets, OS agents, WWW agents • Assistants, experts, and wizards for a given application

12. End User Taxonomy of Agents • Task: Task-specific agents are named accoding to what the agent does. • Information filtering, • informaton retrieval, and • search agents

13. End User Taxonomy of Agents • Architecture: Agents are labeled according to the internal knowledge architecture. • Learning agents • Neural agents

14. End User Taxonomy of Agents • Taxonomy in this book: • Desktop agents: • OS agents: interface agents that provide user assistance with the desktop OS • Application agents: interface agents that provide assistance to the user in a particular application • Application suite agents: interface agents that help users in dealing with a suite of applications

15. End User Taxonomy of Agents • Internet agents: • Web search agents • Web server agents: Internet agents that reside at a specific Web site to provide agent services • Information filtering agents • Information retrieval agents • Notification agents • Service agents • Mobile agents: agents that travel from one place to another to execute user-specified tasks

16. End User Taxonomy of Agents • Intranet agents: • Collaborative customization agents: intranet agents that automate workflow processes in business units • Process automation agents: intranet agents that atomate business workflow processes • Database agents: intranet agents that provide agent services for users of enterprise databases • Resource brokering agents: agents that perform resource allocation in client/server architecture

17. Benefits of Agents

18. Benefits of Agents • Automation • Particularly applicable for automating: • Repetitive behavor of single user • Similar behavior of a group of users • Repetitive sequential behavior of a number of users in a workflow thread • Repetieive behavior can be • time-based or • Evenet-based

19. Benefits of Agents • Cutomization • Fit into the traditional broadcast and publishing models. • There are three basic architecture choices in the implementation of such a model: • the agents can be implemented at the broadcast site, • at the user end, or • in the middle as a broker agent that serves multiple broadcaster and users.

20. Benefits of Agents • Notification: • For instance, such an agent can monitor evetnts of personal changes, and report them to a user.

21. Benefits of Agents • Learning: • An agent with a learning capability can learn tasks that can be automated or preference that can be used for customization: • Learning and offering to automate the repetitive tasks of a single user, this releiving the user of the need to toil with what, when, and how to automate • Leanring the similar attributes of a group of users to customize information based on group characteristics • Learning similar behavior of a group of users to provide workgroup productivity enhancement • Learning and offering to automate recurrent sequential behavior of a group of users in a workflow thread, thus relieving the workgroup of repetitive tasks

22. Benefits of Agents • Tutoring • An agent with a tutoring capability can coach a user in context thanks to its event monitoring and inferencing capabilities, thus reducing the training requirements. • For example, application wizards in the Windows OS

23. Benefits of Agents • Messaging • A messaging agent enables user to accomplish tasks off-line at remote sites. • Mobile agents are examples of messaging agents that can transport themselves from place to place to interact with other agents to perfrom tasks on behalf of a user.

24. Business Obstacles for Agent Acceptance • Hype • The concept of an intelligent is easily grasped by anyone, and generalized freely. • Users do not care about the complexity in being able to deliver such functionality across all applications. • Unfortunately, the delived functionality cannot easily keep up with the generalized expectations of users. • The solution is to focus on task-specific agents for narrow domains.

25. Business Obstacles for Agent Acceptance • User Experience — Indirect Manipulation • A new human-computer interaction beyond today‘s direct manipulation metaphor with GUI • Mass market acceptance of a change in user experience does usually take a number of years. • Business Model • Security • Privacy

26. Agent Use Predictions • Software agetns will be accepted as a design paragigm like object-oriented programming or client/server computing according the following observation: • Task-centered computing is slowly replacing the current application centered computing paradigm. • The move toward document-centered computing with OLE and HTML will accelerate this trend. • The software agent model is a better fit to task-centered computing than the current application software model.

27. Agent Use Predictions • Prediction for the desktop: • SA will be incorporated into task-specific applications to provide apllication-specific assistance. • SA will supplement today‘s GUIs with intelligent backend services, for example, MS wizards. • This replacement will be very much like the replacement of command line ionterface software with applications supporting industry-standard GUIs.

28. Agent Use Predictions • Predication for Intranet • Agents will emerge as critical components of workflow solutions within the enterprise. • Task-specific agents will serve as intelligent front ends to enterprise information systems. • Internet-based agents will get modified for intranet applications to manage the specialized information needs of the corporation.

29. Agent Use Predictions • Predication on the Internet • Agents, in the short term, will emerge as information brokers for specialized domains implemented as centralized Web services. • In essence, agents will be components of Web-based services incorporating agent functionality. • Web search engine exemplify such a trend.