Pat LangleyComputer Science and Engineering / Psychology

Arizona State UniversityTempe, Arizona

Challenges and Opportunities inInformatics Research and Education

Thanks to many collaborators for their contributions. This talk reports research funded by NSF, NASA, ONR, and DARPA, which are not responsible for its contents.

The Informatics Revolution

In the 19th and 20th Centuries, the Industrial Revolution brought new tools to aid physical activities.

We are now in the midst the Informatics Revolution, which has brought us new tools for mental activities.

However, to take full advantage of this change, we must:

Educate students in how to use these tools;

Train students to develop improved tools; and

Carry out research on more powerful types of tools.

Drexel University is well positioned to advance the Informatics Revolution on each front.

What is Informatics?

Informatics is a field that designs, develops, and studies artifacts that automate, model, or assist in mental activities like:

Storing and retrieving information content in memory

Encoding and carrying out routine mental activities

Modeling and drawing inferences about situations

Achieving goals via decision making and problem solving

Interacting and exchanging information with others

Formulating creative and innovative responses

Informatics adopts the computational metaphor, but it moves beyond computer science in a number of ways.

Adaptive Interfaces for Personalized Services

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A Personalized Travel Agent

A Personalized Travel Agent

This project required developing: a database for airline flights a heuristic search module a graphical user interface machine learning of preferences

We also had to combine them in an effective way.

Ecosystem Dynamics in the Ross SeaEcosystem Dynamics in the Ross Sea

d[phyto,t,1] = 0.307 phyto 0.495 zoo + 0.411 phyto

d[zoo,t,1] = 0.251 zoo + 0.615 0.495 zoo

d[detritus,t,1] = 0.307 phyto + 0.251 zoo + 0.385 0.495 zoo 0.005 detritus

d[nitro,t,1] = 0.098 0.411 phyto + 0.005 detritus

As phytoplankton uptakes nitrogen, its concentration increases and the nitrogen decreases. This continues until the nitrogen is exhausted, which leads to a phytoplankton die off. This produces detritus, which gradually remineralizes to replenish nitrogen. Zooplankton grazes on phytoplankton, which slows the latter’s increase and also produces detritus.

A Process Model for the Ross Sea

model Ross_Sea_Ecosystem

variables: phyto, zoo, nitro, detritusobservables: phyto, nitro

process phyto_loss equations: d[phyto,t,1] = 0.307 phyto

d[detritus,t,1] = 0.307 phyto

process zoo_loss equations: d[zoo,t,1] = 0.251 zoo

d[detritus,t,1] = 0.251 zoo

process zoo_phyto_grazing equations: d[zoo,t,1] = 0.615 0.495 zoo

d[detritus,t,1] = 0.385 0.495 zood[phyto,t,1] = 0.495 zoo

process nitro_uptake equations: d[phyto,t,1] = 0.411 phyto

d[nitro,t,1] = 0.098 0.411 phyto

process nitro_remineralization; equations: d[nitro,t,1] = 0.005 detritus

d[detritus,t,1 ] = 0.005 detritus

We can reorganize these equations as a quantitative process model.

Such a model is equivalent to a standard differential equation model, but it makes explicit assumptions about processes that are involved.

Each process indicates that certain terms in equations must stand or fall together.

process exponential_growth variables: P {population} equations: d[P,t] = [0, 1,] P

process logistic_growth variables: P {population} equations: d[P,t] = [0, 1, ] P (1 P / [0, 1, ])

process constant_inflow variables: I {inorganic_nutrient} equations: d[I,t] = [0, 1, ]

process consumption variables: P1 {population}, P2 {population}, nutrient_P2 equations: d[P1,t] = [0, 1, ] P1 nutrient_P2, d[P2,t] = [0, 1, ] P1 nutrient_P2

process no_saturation variables: P {number}, nutrient_P {number} equations: nutrient_P = P

process saturation variables: P {number}, nutrient_P {number} equations: nutrient_P = P / (P + [0, 1, ])

Inductive Process ModelingInductive Process Modeling

model AquaticEcosystem

variables: nitro, phyto, zoo, nutrient_nitro, nutrient_phytoobservables: nitro, phyto, zoo

process phyto_exponential_growth equations: d[phyto,t] = 0.1 phyto

process zoo_logistic_growth equations: d[zoo,t] = 0.1 zoo / (1 zoo / 1.5)

process phyto_nitro_consumption equations: d[nitro,t] = 1 phyto nutrient_nitro, d[phyto,t] = 1 phyto nutrient_nitro

process phyto_nitro_no_saturation equations: nutrient_nitro = nitro

process zoo_phyto_consumption equations: d[phyto,t] = 1 zoo nutrient_phyto, d[zoo,t] = 1 zoo nutrient_phyto

process zoo_phyto_saturation equations: nutrient_phyto = phyto / (phyto + 0.5)

HeuristicSearch

data: time-series observations

knowledge: generic processes

interpretable process model

phyto, nitro, zoo, nutrient_nitro, nutrient_phyto

variables

Generality of Inductive Process Modeling

acquatic ecosystems protist dynamics

hydrology biochemical kinetics

The Prometheus Modeling Environment

The Prometheus Modeling Environment

This project required that we develop: a new formalism for process models a knowledge base of generic processes constrained search for model structures estimation of nonlinear ODE parameters an appropriate graphical user interface

We also had to combine these elements in an effective way.

More Examples of Informatics Research

Map learning for mobile robot localization and navigation

Visual learning to improve analysis of aerial photographs

Adaptive assistance for crisis-response scheduling

Refining digital road maps using GPS traces from vehicles

Model-driven monitoring of the Space Station power grid

Data-guided revision of a terrestrial ecosystem model

Over the past 15 years, I have also done significant research on:

Many of these efforts have involved collaboration with researchers in fields other than AI and computer science.

Informatics draws on concepts and techniques from computer science, but it differs by emphasizing:

Problem-driven research

address challenge problems, not theoretical issues

System-level innovation

integrate component algorithms, not refine them

User-oriented systems

respond to user needs, not write stand-alone programs

This applied focus makes informatics highly interdisciplinary.

Characterizing Informatics

Application Areas in Informatics

We can organize informatics into broad areas of application:

Health informatics (e.g., order entry systems)

Transportation informatics (e.g., air traffic control)

Military informatics (e.g., command and control systems)

Consumer informatics (e.g., Web recommender systems)

Educational informatics (e.g., intelligent tutoring systems)

Entertainment informatics (e.g., virtual environments/agents)

Science informatics (e.g., in biology, ecology, chemistry)

Because informatics mimics human cognition, it has relevance to all facets of human endeavor.

General Training in Informatics

Both undergraduate and graduate offerings in informatics can teach students to:

Make effective use of existing informatics tools

Understand the principles behind their operation

Gain experience in using them through hands-on projects

Creatively compose them to accomplish complex tasks

Appreciate both their generality and their limitations

Every student would benefit from exposure to informatics tools and their effective use.

Drexel has the opportunity to offer generic informatics courses to its entire student body.

Opportunity

What Is Science Informatics?

collection and storage of scientific data representation and use of scientific models discovery of new scientific knowledge scientific communication and interaction

Science informatics involves the use informatics technology to aid the scientific enterprise.

This broad research area investigates four main topics:

Historically, scientific challenges have often served to motivate informatics research.

Advances in science informatics increase understanding of the scientific process and stimulate new discoveries.

Applications of Science InformaticsA

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The Prometheus Modeling Environment(Bridewell et al., 2007)

An Environment for Systems Biology of Aging

Claims about Science Informatics

Science has always been a computational endeavor; new technology can aid it but not alter its nature.

Information technology is not limited to one facet of science, but cuts across its entire range.

Science informatics rests on general principles that hold across all disciplines.

We can state some interesting hypotheses about the science informatics or e-science movement:

These assumptions have implications for both research and education in science informatics.

Training in Science Informatics

We can train undergraduate and graduate students in science informatics topics like:

the basic structures and processes of science the computational character of science informatics tools that can aid scientific research how these tools operate and principles behind them similarities and differences among disciplines the potential of science informatics and open issues

They should be prepared to use informatics tools in their scientific careers or develop tools for others to use.

Drexel has the opportunity to offer a distinctive minor in science informatics.

Opportunity

A Center for Science Informatics

Drexel would also gain from a center for science informatics that carries out research on computational tools for:

collecting, storing, and managing scientific data creating and simulating scientific models discovering and revising laws and models supporting scientific communities for both general use and for specific fields

Center researchers would collaborate with Drexel scientists in the context of discipline-driven projects.

A few such institutes already exist, but the centers’s breadth would distinguish it and draw international attention.

Opportunity

Candidate Project: Health and the Environment

One promising science informatics project would study the relation between health and environment by:

collecting person-centric data with wearable sensors developing models of how variables are related refining the models to reflect individual differences visualizing both the data and model predictions making results available over the World Wide Web

Such a project would extend science informatics while aiding understanding of environmental effects on health.

In addition, it would support citizen science and aid decisions about environmental and health policy.

Opportunity

Informatics and Virtual Environments

Specify detailed physical settings that reflect characteristics of the real world;

Support visualization and animation of these environments, including interaction with virtual objects; and

Include synthetic characters that operate in the environment and interact with human users.

Another cross-cutting theme in informatics is the growing use of virtual environments that:

Virtual environments have broad applications in entertainment, education, medicine, business, and science.

Within 20 years, most Americans will spend a large fraction of their lives within virtual worlds.

Intelligent Agents and Synthetic Characters

Make inferences about their situations

Carry out activities to achieve their goals

Generate plans that address novel problems

Interact with other agents on joint activities

Synthetic environments pose an ideal setting to drive research on intelligent agents that:

They let us study integrated approaches to embodied cognition in complex but controlled scenarios.

Progress in this area would benefit simulation-based training, interactive entertainment, and other applications.

The ICARUS Cognitive Architecture(Langley, 2006)

Long-TermLong-TermConceptualConceptual

MemoryMemory

Short-TermShort-TermBeliefBelief

MemoryMemory

Short-TermShort-TermGoal MemoryGoal Memory

ConceptualConceptualInferenceInference

SkillSkillExecutionExecution

PerceptionPerception

EnvironmentEnvironment

PerceptualPerceptualBufferBuffer

Problem SolvingProblem SolvingSkill LearningSkill Learning

MotorMotorBufferBuffer

Skill RetrievalSkill Retrievaland Selectionand Selection

Long-TermLong-TermSkill MemorySkill Memory

Synthetic Agents in ICARUSU

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Synthetic Agents for Urban Driving

We have developed an urban driving environment using Garage Games’ Torque game engine.

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are needed to see this picture.

We have created ICARUS agents that combine cognitive and sensori-motor behavior to operate in this complex and dynamic setting.

Synthetic Agents for Urban Driving

We have developed an urban driving environment using Garage Games’ Torque game engine.

QuickTime™ and a decompressor

are needed to see this picture.

We have created ICARUS agents that combine cognitive and sensori-motor behavior to operate in this complex and dynamic setting.

This project required that we develop: a Torque physical driving simulator a specific layout for buildings / streets a formalism for stating ICARUS agents an interpreter for ICARUS programs behaviors for other cars / pedestrians

We also had to integrate these elements.

A Center for Virtual Environments

Urban and natural structures in some environment

Processes and mechanisms that govern their dynamics

Agents that operate in the simulated environment

Knowledge and goals that govern their behavior

Drexel would benefit from a research center that develops new technology for creating and using virtual worlds, including:

The center would develop models at different aggregation levels(buildings, organizations, cities, regions).

Researchers would also develop informatics tools to support the construction, visualization, and simulation of such models.

Opportunity

Candidate Project: A Virtual University

Buildings, streets, and other infrastructure Environmental processes that affect these structures Students, faculty, and staff in the community Knowledge and goals that guide their behavior

One project could involve developing a virtual environment for Drexel University that includes:

Different interest groups could use this model in different ways:

Opportunity

Researchers could test the model against observations Administrators could use the model for decision making Students could visualize the model before attending

Such a virtual presence would make Drexel far more visible, in many senses of the term.

Links to Existing Drexel Activities

College of Information Science and Technology

Department of Computer Science

Biomedical Engineering, Science and Health Systems

Applied Communications and Information Networking Institute

Drexel Engineering Cities Initiative

Human Cognition Enhancement Program

These ideas have clear relations to existing Drexel academic units and research initiatives:

However, their inherent interdisciplinary character makes other connections likely as well.

Some General Activities

Defining challenges and opportunities in an area

Launching a research center in that area

Organizing annual symposia on the topic

Offering tutorials and summer schools in the area

Authoring technical and popular books on the topic

Creating a Web presence and building user communities

Whichever thrusts Drexel pursues in research and education, it can increase its influence and visibility by:

Together, these activities will help make Drexel University a leader in whatever areas it decides to pursue.

Personal Research Themes

Modeling the behavior of complex systems

Developing integrated software frameworks

Combining symbolic with numeric processing

Incorporating insights about human cognition

Building systems that interact with human users

My research trajectory has exhibited some recurring themes:

My future research will continue to follow these principles, independent of the specific problems addressed.

Simon’s Research Heuristics

Be audacious. Tackle challenging problems that others are reluctant to face or even to admit are solvable.

Ignore discipline boundaries. Become familiar with all fields relevant to your research problem and incorporate their ideas.

Use a secret weapon. Take advantage of metaphors and tools that you have mastered but that are not yet widely available.

Balance theory and data. Realize that scientific accounts must respond phenomena but also connect to existing knowledge.

Satisfice. Do not attempt everything at once; idealize your challenging problems enough to make them tractable.

Persevere. Build incrementally on your previous results, extending them to cover ever more phenomena.

The career of Herbert Simon offers guides for scientific research:

I have attempted to follow these principles in my own research.

Concluding Remarks

Training students to use information technology

Developing and understanding new informatics tools

Using problem-driven, system-level, user-centric research

Relating to many socially-relevant application areas

The field of informatics offers challenges and opportunities for:

Drexel University is well situated to take an international lead in this critical area of research and education.

Science informatics and virtual environments, broadly defined, are two themes that hold special promise.

End of Presentation