Ch5 - 1 The Technology of intelligent Agents From: Chapter 5, A. Canlayan and C. Harrison, Agent: Sourcebook, Wiley 1997.

Ch5 - 2 Contents Background The Historical View The Technical View Machinery, Content, Access, and Security Putting the Pieces Together

Ch5 - 3 Background 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

Ch5 - 4 The 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 agents resembles the emergence in the 1970s of the GUI.

Ch5 - 5 The Historical View DOS explore the GUI concept in early –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 emerge. –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.)

Ch5 - 6 The 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

Ch5 - 7 The 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.

Ch5 - 8 The 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.

Ch5 - 9 The 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.

Ch The Historical View We are roughly at the same point that the GUI had reached in –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.

Ch The 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.

Ch Intelligent 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.

Ch Intelligent 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

Ch Intelligent and Agency autonomyperceptionWooldridge and Jennings’ (115) definition of an agent emphasize autonomy and perception: – 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.

Ch Intelligent and Agency Agent interactivity Service interactivity Application interactivity Data interactivity User representation Asynchrony PreferenceReasoningLearning Threshold of Intelligent Agency

Ch Intelligent and Agency Agent interactivity Service interactivity Application interactivity Data interactivity User representation Asynchrony PreferenceReasoningLearning Threshold of Intelligent Agency  SNMP V2  DB agents  Imbedded agents  IBM Agent Building Environment  Mail agents  Mobile agents  User interface agents  Lotus Notes  Workflow automation  WWW search agents

Ch Machinery, 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

Ch Machinery, 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

Ch Machinery 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

Ch Content 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

Ch Content 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.

Ch Access 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 DBS File system agent Messaging RPC HTTP

Ch Access 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.

Ch Security 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.

Ch Putting the Pieces Together — Example of a News Processing Agent Foraging Methods Preprocess Content Search Content User Access Methods Inferencing Learning Verification of Action Database, BBS WWW Raw Content Machinery Knowledge Content AccessSecurity News streams Event Content

Ch Putting the Pieces Together — Conceptual Model of an Agent Reasoning Engine Access Control Learning Engine Knowledge Access Content Security Access Machinery Event Knowledge Action