SEAS Help System
|Introduction:||About||Getting Started||How To|
|Topics:||Concepts||Template Style Guide||Frequently Asked Questions|
|Viewers:||Manager||Publication Information||Situation Information|
|Multi. Arg.||Uni. Arg. Derivative Question||Uni. Arg. Primitive Question|
|Multi. Tmp.||Uni. Tmp. Derivative Question||Uni. Tmp. Primitive Question|
|Table Viewer||Summary Viewer||Memos|
|Collection Manager||Collection Viewers||Merging Tool|
SEAS (a.k.a Structured Evidential Argumentation System or SRI Early Alert System) is designed to aid analysts in predicting potential opportunities/crises. It is implemented as a web server that supports the construction and exploitation of a corporate memory filled with analytic products, methods, and their interrelationships, indexed by the situations to which they apply. Objects from this corporate memory are viewed and edited through the use of a standard browser client, with the SEAS server producing ephemeral HTML based upon the contents of the SEAS knowledge base that constitutes corporate memory. The foundation of this corporate memory is an ontology of arguments and situations that includes three main types of formal objects: argument templates, arguments, and situation descriptors. Roughly speaking, an argument template records an analytic method as a hierarchically structured set of interrelated questions, an argument instantiates an argument template by answering the questions posed relative to a specific situation in the world, and situation descriptors characterize the type of situations for which the argument templates were designed and the specific situations that arguments address. SEAS emphasizes the use of simple and regular inference structures as the foundation of its argument templates, making the reasoning easy to follow and making it possible for analysts to independently author new templates. Argument templates include discovery tools, recommended methods of acquiring information pertaining to the questions posed by the template. An analyst wanting to record an argument, selects an appropriate template given the situation, uses the discovery tools to retrieve potentially relevant information, selects that information to retain as part of the argument and records its relevance to the questions at hand, answers the multiple-choice questions by selecting those answers that bound what is known, and records the rationale for the answers selected. This structured argumentation methodology encourages a careful analysis by reminding the analyst of the full spectrum of indicators to be considered, eases argument comprehension by allowing the analyst to "drill down" along the component lines of reasoning to discover the basis and rationale of others' arguments, and invites and facilitates argument comparison by framing arguments within common structures.
SEAS is built upon a foundation composed of the following systems:
ALP, Gister, Grasper, & Ocelot/Perk
Allegro Common Lisp & AllegroServe
The high level architecture of SEAS is pictured above. SEAS consists of the SEAS Server connected to the SEAS Corporate Memory. The SEAS Server is a web server that takes in HTTP and produces HTML based upon the contents of Corporate Memory.
TIP: Click on the components in this image to go to their corresponding web site.
The SEAS server is built on top of the AllegroServe web server developed by Franz Inc.. This is an HTTP server written in Common Lisp. Since our other components are written in Common Lisp (e.g., Ocelot [PLK], Gister [LGS, Low], and Grasper [KLSW]) this provided an ideal basis for the development of the SEAS Server. The HTML Generator creates HTML pages on the fly upon request, utilizing the ALP (Active Lisp Pages) scripting environment for creating dynamic pages and Grasper for creating graphical depictions of SEAS arguments and templates; GD is used to produce images of the Grasper graphical depictions. AllegroServe parses the HTTP requests and calls upon the HTML Generator to create pages that are then returned to the requesters by AllegroServe. The pages are generated by consulting the SEAS Corporate Memory Manager which in turn consults the SEAS Corporate Memory stored in the Ocelot KBMS. It connects to a KBMS via the GFP [CFFKR-97] or OKBC [CFFKR-98] API . If the request changes the answer to some question in an argument, the Gister Engine is invoked to update related answers in that argument in the KB. The Ocelot/Perk knowledge base management system is a KB management system server that optionally connects to the DBMS Server via SQL; otherwise Ocelot stores the KB as a file in the system file.
What happens when the user clicks on a light to answer a question?
[CFFKR-97] Chaudhri, Vinay K. and Farquhar, Adam and Fikes, Richard and Karp, Peter D. and Rice, James P. The Generic Frame Protocol 2.0. Artificial Intelligence Center, SRI International, Jul 1997.
[CFFKR-98] Vinay K. Chaudhri, Adam Farquhar, Richard Fikes, Peter D. Karp, and James P. Rice, "OKBC: A Programmatic Foundation for Knowledge Base Interoperability," in Proceedings of National Conference on Artificial Intelligence, 1998.
[KCP] Peter D. Karp and Vinay K. Chaudhri and Suzanne M. Paley, "A Collaborative Environment for Authoring Large Knowledge Bases," in Journal of Intelligent Information Systems, 1999, Vol. 13, pp. 155-194.
[KLSW] Peter D. Karp, John D. Lowrance, Thomas M. Strat, and David E. Wilkins, "The Grasper-CL Graph Management System," in LISP and Symbolic Computation: An International Journal, Kluwer Academic Publishers, 1994, Vol. 7, pp. 251-290.
[LGS] John D. Lowrance, Thomas D. Garvey, and Thomas M. Strat, "A Framework for Evidential-Reasoning Systems," Uncertain Reasoning, Ed. Glenn Shafer and Judea Pearl, Morgan Kaufman Publishers, Inc., 1990, pp. 611-618.
[Low] John D. Lowrance, "Automated Argument Construction," Journal of Statistical Planning and Inference, vol. 20, 1988.
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