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The Technology of The Technology of intelligent Agentsintelligent Agents

From: Chapter 5, A. Canlayan and C. Harrison, Agent: Sourcebook, Wiley 1997.

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ContentsContents

• Background• The Historical View• The Technical View• Machinery, Content, Access, and Security• Putting the Pieces Together

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BackgroundBackground

• Agents’ capabilities are product of straightforward software technology.

• The goal– To give you a mental framework for assessing which

technologies are required for building particular kind of applications

– To help you distinguish between the reality and the hype of agents

– To prepare you for introducing these technologies in your company

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The Historical ViewThe Historical View

GUI

emergence in 1970s

The vast majority of applications were written for command line-oriented OS and text-based terminal.

Supporting reasons:structured programming,screen-to-screen navigation,4GL

The evolution of agentsresembles the emergencein the 1970s of the GUI.

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The Historical ViewThe Historical View

• DOS explore the GUI concept in early 1980.– The Apple Macintosh brought it in.

– The rest of PC community caught up in the early 1990s.

• 1983, no GUI-based applications– By 1985, WYSIWYG text processor and graphical editor began to e

merge.

– Many non-GUI applications continue to exist into the early 1990s.

• Today, X-Windows, Windows, MacOS, or OS/2 GUI (although text-based applications developed for or by business for their own use are still common.)

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The Historical ViewThe Historical View

• The benefit of this transition has been a dramatic improvement in ease of use and in the quality of results.

• In other cases, such as presentation tools, hypertext documents, or multimedia applications, the benefit has been the creation of an entire new set of capabilities.

command line GUI(transition)

several year

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The Historical ViewThe Historical View

• In the early to mid-1980s, commercial software developers wanted the GUI capabilities for competitive purposes, and probably did not think of them as a distinctive technology at the time.

• The technology include– high resolution, color, all point addressable displays and

corresponding printers

– scalable fonts

– printing devices and direct manipulation, and

– WYSIWYG editors and clipboard, etc.

• Today the GUI is recognized as a distinct subsystem within the operating system, which application can exploit.

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The Historical ViewThe Historical View

• The emergence of agent technology is similar in many ways to the GUI story– AT is not a single, new, emerging technology, but rather the

integrated applications of a number of technologies.

– AT will often be new sets if capabilities added to existing applications

• evolution focus of this book• revolution focus of press

– Agents functions will emerge initially within individual applications, but with experience we will be able to define a set of applications that will become part of the OS or application environment.

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The Historical ViewThe Historical View

– Agent applications inevitably have strong human-computer interaction aspect.

– Usability and functional competitive advantage in the short term;

– becoming standard in the long term

– Ultimately, applications that do not exploit the agent support in the OS will be severely disadvantaged.

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The Historical ViewThe Historical View

• We are roughly at the same point that the GUI had reached in 1980.– It was still an active research area.– Isolated pioneer products were emerging.– The full set of required technologies was not available.– The technologies were not independent with one another.– There was no consensus on the required abstractions which could be

provided by an OS.– Despite the high level of expectations aroused by the hype, the

technology was not yet in widespread use, nor had it been widely accepted as an inevitable trend in the evolution of application technologies.

– But there was a set of early adapters who were able to demonstrate that there was value in this approach.

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The Technical ViewThe Technical View

• Agent technology is a pragmatic set of application characteristics supported by various technologies, which extend the functionality or value of the application.– In other words, developers do not set out to create “agent

application”; they set out to add additional values to a new or existing application and find that the agent approach is a unique or at least advantageous means to this end.

• So the search for a technical definition of intelligent agents become instead a method of describing the “agent” technologies of a wide range of applications.

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Intelligent and AgencyIntelligent and Agency

• Two major dimensions of the landscape– Intelligence

• the degree to which the application employs reasoning, learning, and other technologies to interpret the information or knowledge to which it has access or which is presented to it.

– Agency• the degree to which the agents can perceive its environment

and act on it.

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Intelligent and AgencyIntelligent and Agency

• The path along the dimension of intelligence– preference: relatively formal statements of desired behavior

– reasoning capability: preferences are expressed in a formalized rules

– general ability to modify the reasoning behavior, i.e., learning

• The path along the dimension of agency– asynchrony

– user representation

– data interactivity

– application interactivity

– service interactivity

– agent interactivity

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Intelligent and AgencyIntelligent and Agency

• Wooldridge and Jennings’ (115) definition of an agent emphasize autonomyautonomy and perceptionperception:– Perhaps the most general way in which the term agent is used is

to denote a hardware or software-based computer system that enjoys the following properties:

• Autonomy: Agent operate without the direct intervention of human or others and have some kind of control over their actions and internal state.

• Social ability: Agents interact with other agents via some kind of agent-communication language.

• Reactivity: Agents perceive their environment, and respond in a timely fashion to changes that occur in it.

• Pro-activeness: Agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative.

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Intelligent and AgencyIntelligent and Agency

Agentinteractivity Serviceinteractivity

Applicationinteractivity

Datainteractivity

Userrepresentation

Asynchrony

Preference Reasoning Learning

Threshold of Intelligent Agency

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Intelligent and AgencyIntelligent and AgencyAgentinteractivity Serviceinteractivity

Applicationinteractivity

Datainteractivity

Userrepresentation

Asynchrony

Preference Reasoning Learning

Threshold of Intelligent Agency

SNMP V2

DB agents

Imbedded agents

IBM Agent Building Environment

Mail agents

Mobile agents

User interface agentsLotus Notes

Workflow automation

WWW search agents

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Machinery, Content, Access, and SecurityMachinery, Content, Access, and Security

• We relate the two dimensions to several software technologies– Machinery and content intelligence

– Access and security agency

• Agent technology factors– Machinery

• Inferencing, learning,validation, representation

– Content• rules, context, application ontology & grammars

– Security• mutual, public authentication, privacy, payment

– Access• to applications, data & services, network,mobility

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Machinery, Content, Access, and SecurityMachinery, Content, Access, and Security

• The technology factors of intelligent agents– Machinery

• Engines of various kinds, which support the varying degree of intelligence

– Content• Data employed by the machinery in reasoning and learning

– Access• Methods to enable the machinery to perceive content and

perform actions outcomes of reasoning

– Security• Concerns related to distributed computing, augmented by a few

special concerns to intelligent agents

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MachineryMachinery

• Machinery refers to engines of various kinds, mainly developed in the field of AI, which support varying degree of inference.

• These engines include– Various forms of inferencing

– Various forms of learning

– Tools for the user’s creation and modification of rules and other knowledge

– Tools for the validation of the rule sets

– Tools for the development of strategies for negotiation and collaboration among agents and users

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ContentContent

• Data for machinery• Content includes structured knowledge

– Rules: user’s expression of preference of policies

– Interpretable representation of real-world knowledge, so that agents and applications can communicate with one another about goods and services of interest to the user

• Subject of research in AI: – knowledge representation and

– Knowledge base

• Grammars required to support dialogue among agents and between agents and users

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ContentContent

• Non-structured knowledge– Free text (with little hint of knowledge by formatting, e.g.,

HTML)– Relying on filtering and natural language tools to extract

structured information

• Agents must be able to learn from “observation” of user behavior– The result of observation is structured but its significance

may not be interpretable, and – The agent may need guidance from the user or may rely

explicitly on interpretation by the user.

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AccessAccess

• Access is the degree to which the agent can interact with its environment.

• Binding access functions to the action procedure of the machinery, so that inferencing and learning can lead to actions on the local or external applications.

agent

API

Shared memory

DBSFile system

agent

Messaging

RPC

HTTP

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AccessAccess

• A further example of access is mobile agents.– They are independent programs, generally written in a script

language, which are capable of migrating themselves, including process state and instance data, between the user’s computer and one or more remote servers.

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SecuritySecurity

• In EC the agent may have the legal authority and responsibility of the user.

• The agent, in some cases, will be performing EC on behalf of the user; this requires a conventional electronic payment scheme, methods of reconciliation, and auditability.

• Protection of personal information contained in an agent, e.g., preference or a negotiation strategy.

• Unanticipated behavior when no human is directly observing them.– Accidental or intentional virus

– Collective phenomena arising from interaction among large population of autonomous agents.

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Putting the Pieces Together Putting the Pieces Together ——Example of a News Processing AgentExample of a News Processing Agent

ForagingMethods

PreprocessContent

SearchContent

User AccessMethods

Inferencing

Learning

Verificationof Action

Database,BBSWWW

RawContent Machinery

KnowledgeContent

Access Security

Newsstreams

EventContent

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Putting the Pieces Together Putting the Pieces Together ——Conceptual Model of an AgentConceptual Model of an Agent

ReasoningEngine

AccessControl

LearningEngineKnowledge

Access

Content

SecurityAccess

Machinery

Event

Knowledge

Action