sage ariva ariva pi conference, november 2005 unclassified//for official use only

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ARIVAARIVA PI Conference, November 2005

UNCLASSIFIED//FOR OFFICIAL USE ONLY

2

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Team Roles

Manage project/sustain vision

Intel and human factors expertise

Cognitive Science research

Build VizArch, simBorgs

Build Slate

HII Research

– Network data

– Image management

Novel Gaming Interfaces

– CMU/Dynamix ARGUS Cognitive Systems Engineering

– Nutech ABEM

Selmer BringsjordAndrew Shilliday, Joshua TaylorKonstantine Arkoudas

Sangeet Khemlani, Eric Pratt,Gabe Mulley, Bettina SchimanskiRensselaer AI & Reasoning (RAIR)

LaboratoryDepartment of Cognitive ScienceDepartment of Computer Science

Rensselaer Polytechnic Institute (RPI)Troy NY 12180 US

11.30.05

The Slate System:The Slate System:Four New DevelopmentsFour New Developments

QuickTime™ and aGraphics decompressor

are needed to see this picture.

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(Not based on ppt source.)

Original Concept

Current Release: v2.0

Reflects new theories of hypothesis generation

E.g., MMOI-based hypo genE.g., scenario generaion

via MDF

Automatic (v1) report generation

Includes a proved-to-be sound system, S, for assembling proofs, arguments, composite arguments, meta-arguments — and these arguments can be automatically assessed

Facilitates not just deduction, but abduction, induction, and model-based reasoning

Seamless integration with the world’s best automated and interactive machine reasoning systems and model finders (Vampire, Paradox, Athena, etc.)

IKL-compliant (empirically confirmed)

Some Innovative Features

http://www.cogsci.rpi.edu/research/rair

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http://www.cogsci.rpi.edu/research/rair/projects.php

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All of CS #4

Translated into Common LogicSo, we’re making progress, and will have this “mega” round trip working @ Nov/Dec 05 PI

meetingSAGE

QuickTime™ and aSorenson Video 3 decompressorare needed to see this picture.

The Visualization “Hookup” Challenge

We are swimming in an expanding sea ofsystems for exploring and visualizing datain the IA domain.

We need interoperability here as well!

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Challenge Reduced to Software Engineering:

• The Vivid family, mathematically, up to the challenge of visual data integration and interoperability.

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The Uncertainty Challenge

can be met; we have the formalism; simple example

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77%

82%

79%

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Argument against an argument

Argument against an argument

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Novel Methods of Human-Computer Interaction

Network Data Visualization

Catherine Plaisant, Benjamin B. Bederson

Bongshin Lee, Hyunmo KangHuman-Computer Interaction Lab

University of Maryland

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Two applications forNetwork Data Visualization

TreePlus Node-link graph visualization Tree-centered solution

Improve incremental navigation

NetLens Interactive coordinated overviews Dynamic filtering Bipartite graphs

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Challenge of Network Data Visualization

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TreePlus Project Goals

Develop a good tree-centered solution for iterative inspection of large networks

and in cooperation with the team of Wayne Gray

Determine for which tasks and data sets different solutions works better

Better understand how people use visualization tools to explore graphs

Rensselaer

++ reada

bility +

+

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TreePlus

Extraction of spanning tree “Plant a seed and watch it grow”

Dynamic root selection Incremental Exploration + Integrated Search Pan/Zoom + Animation

Interaction techniques Highlight and preview of adjacent nodes Hints of graph structure Animated transitions

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TreePlus

Written in C#with Piccolo.NET

Bars give a preview of how fruitful it would be to go down a pathWhen cursor hovers over a node, a preview of all nodes connected to that node appear on the right

SAGEGraphPlus

TreePlus

Controlled experiment

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Promising Results

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Task

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GraphPlus

TreePlus

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High Density Low DensityData Density

Completion Time (sec) `

GraphPlus

TreePlus

• 28 subjects• Compared TreePlus to GraphPlus• Controlled interface “density”

Study in collaboration with Wayne Gray - RPI

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Two applications forNetwork Data Visualization

TreePlus Node-link graph visualization Tree-centered solution Improve incremental navigation

NetLens Interactive coordinated overviews Dynamic filtering Interaction history Bipartite graphs

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NetLens – Papers (ACM DL) & Authors

Papers on the leftAuthors on the right

Overviews provided for all attributes (here for number of papersper year)

Filtered to show only papers related to visualization, and the people who wrote those papers are shown on the right side, aggregated by institution type.

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NetLens –Email (Enron) & People

Emails on the leftPeople on the right

Overviews provided for all attributes (here for emotional tone on emails side)

Filtered to show only emails related to CAenergy crisis; and the people who sentthem are shown on

the right side.

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Rensselaer CognitiveScience

Complex Visualizations of Massive Data:The Impact of Design on Mind

ARDA ARIVA WorkshopOrlando, Florida

30 November 2005

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Intelligently Designed Interfaces for Intelligence Analysts

• NIMD will bring a superhighway of information technology to the Intelligence Analyst

• But, by themselves the NIMD technologies will not bridge the crucial human-computer-information gap

• All of these technologies REQUIRE a user interface for the IA

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Interface = Visualization + Interaction

Is this a good interface?

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Is this?

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How about this?

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Or this?

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Design

Mind Tasks

Performance

Performance

Understanding the Impact of Design on Mind

• A good interface is one that optimizes performance on a task that you care about

• Understanding what makes an interface good or bad requires understanding the constraints and affordances in the interactions between Mind, Design, and Tasks

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Cognitive Metric Profile -- showing dynamic changes

in task demands on human cognitive, perceptual, and action resources

Our Goals: Forecasting Dynamic Changes in Cognitive Workload

• Create a new generation of tools inspired by cognitive science theory that will enable us to predict the changing demands on human cognition, perception, and action imposed by an interface during task performance

• Apply these tools to aid the evaluation and design of the next generation of visualization and interaction techniques for software intended for use by Intelligence Analysts

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Our Approach

• 1st – Play well with others (VIA)

– Develop tools and techniques so that our data collection and modeling systems can interact with tools built by others

• 2nd – Simulated Human Visualizers (simBorgs)

– Develop generation of cognitive theory based models of human visualization and interaction

• 3rd – Cognitive Metrics Profiling

– Toolkit for accessing and predicting dynamic changes in demands that an interface makes on human cognition, perception, and action as the human user performs a typical task

• Human data – eye fixations, scanpaths, mouseclicks, performance time

• simBorg data – predictions of changes in internal use of memory, attention, and perception

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Current research focuses on three

nested technologies

CogWorks VIA: Visualization-Interaction Architecture

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VIA

• VIA release 0.0 used at UMd in August/September to evaluate an innovative design for visual display of abstract data

• VIA release 0.9 used to interact with a random application downloaded from the internet

– required addition of two lines of code

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Demos

• VIA in action -- demo of ease of instrumenting C# applications to run with VIA

• Playback of logfile created during UMd session• Logfiles analyzed for UMd dissertation• Eye data analyzed for UMd dissertation• simBorg written that uses VIA to perform tasks on UMd software• Information scent implemented in simBorgs in real-time use

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Bridging the Human-Computer-Information Gap by . . .

• Engineer interfaces for the next generation tools developed for the IA by creating a new generation of human performance models focused on cognitive issues in usability

• To tailor the designed environment to best fit the ways in which the IA

–thinks

–perceives

–and acts

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Questions?

sage ariva ariva pi conference, november 2005 unclassified//for official use only

Documents

composite arguments

visualization tools

new developmentssagenot

slate system

sound system

mmoibased hypo gene

node ap

model finders vampire