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Pat Langley Computer Science and Engineering / Psychology Arizona State University Tempe, Arizona Challenges and Opportunities in Informatics Research.

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Presentation on theme: "Pat Langley Computer Science and Engineering / Psychology Arizona State University Tempe, Arizona Challenges and Opportunities in Informatics Research."— Presentation transcript:

1 Pat Langley Computer Science and Engineering / Psychology Arizona State University Tempe, Arizona Challenges and Opportunities in Informatics 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.

2 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.

3 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.

4 Adaptive Interfaces for Personalized Services Personalized Radio Stock Advisor Travel Agent Apartment Finder

5 A Personalized Travel Agent

6 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.

7 Ecosystem 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 latters increase and also produces detritus.

8 A Process Model for the Ross Sea model Ross_Sea_Ecosystem variables: phyto, zoo, nitro, detritus observables: 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 zoo d[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.

9 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 Modeling model AquaticEcosystem variables: nitro, phyto, zoo, nutrient_nitro, nutrient_phyto observables: 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) Heuristic Search data: time-series observations knowledge: generic processes interpretable process model phyto, nitro, zoo, nutrient_nitro, nutrient_phyto variables

10 Generality of Inductive Process Modeling acquatic ecosystems protist dynamics hydrology biochemical kinetics

11 The Prometheus Modeling Environment

12 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.

13 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.

14 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

15 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.

16 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

17 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.

18 Applications of Science Informatics Analyzing sky surveys Clustering gene expressions Recording brain activity Analyzing satellite images

19 Applications of Science Informatics Building ontologies Visualizing simulations Building scientific models Scientific workflows

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

21 An Environment for Systems Biology of Aging

22 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.

23 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

24 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 centerss breadth would distinguish it and draw international attention. Opportunity

25 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

26 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.

27 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.

28 The I CARUS Cognitive Architecture (Langley, 2006) Long-TermConceptualMemory Short-TermBeliefMemory Short-Term Goal Memory ConceptualInference SkillExecution Perception Environment PerceptualBuffer Problem Solving Skill Learning MotorBuffer Skill Retrieval and Selection Long-Term Skill Memory

29 Synthetic Agents in I CARUS Urban Combat Twig Rush 2008 MadRTS

30 Synthetic Agents for Urban Driving We have developed an urban driving environment using Garage Games Torque game engine. We have created I CARUS agents that combine cognitive and sensori-motor behavior to operate in this complex and dynamic setting.

31 Synthetic Agents for Urban Driving We have developed an urban driving environment using Garage Games Torque game engine. We have created I CARUS 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 I CARUS agents an interpreter for I CARUS programs behaviors for other cars / pedestrians We also had to integrate these elements.

32 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

33 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.

34 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.

35 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.

36 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.

37 Simons 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.

38 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.

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