Path: utzoo!utgpu!jarvis.csri.toronto.edu!neat.cs.toronto.edu!marina
Newsgroups: ont.events
From: marina@ai.toronto.edu (Marina Haloulos)
Subject: Dr. N.S. Sridharan, Wednesday 22 November 1989: ARTIFICIAL INTELLIGENC
Message-ID: <89Nov13.144138est.2899@neat.cs.toronto.edu>
Date: 13 Nov 89 19:42:22 GMT

           Department of Computer Science, University of Toronto
         (SF = Sandford Fleming Building, 10 King's College Road)

       -------------------------------------------------------------

                      ARTIFICIAL INTELLIGENCE SEMINAR
             SF1013, at 11:00 a.m., Wednesday 22 November 1989

                            Dr. N.S. Sridharan
                              FMC Corporation

            "Real-Time Performance for Interactive AI Systems"

Achieving real-time performance in a highly interactive and complex
knowledge-based systems, such as the Pilot's Associate, poses serious
technical challenges.  Novel hardware and software approaches must be
brought together to yield the desired performance improvements.  We review
the current stock of best technical innovations along these lines.  We also
summarize our experience in designing the architecture for the Pilot's
Assocaite (DARPA/US Air Force) and Submarine Operational Automation System
(DARPA/US Navy).

Real-time requirements are often narrowly described in terms of speed
alone.  There are four important dimensions that are particularly
significant to interactive, decision-aiding systems:
   - speed,

   - responsiveness,

   - timeliness, and

   - graceful adaptation.

Speed is measured as the number of tasks executed per unit time.
Responsiveness refers to the ability of the systems to take on new tasks
quickly.  Timeliness characterizes the system's abilities to conform to
task priorities. Graceful adaptation is the system's ability to reset
priorities based on changing workload.

We describe a knowledge-processing architecture we have designed with all
four dimensions of real-time performance in mind. This system, built in
Common Lisp and Flavors, is an event-driven blackboard system, geared
toward asynchronous parallel processing.

This talk will describe the performance probes and metrics used to measure
responsiveness and timeliness.