Development of a Decision Support System for TrumpeterSwan Management
STUDY PLAN
USDI - Geological Survey, Biological Resources Division,Midcontinent Ecological Science Center
Work Unit Title: Decision support and adaptivemanagement for resource managersStudy Title:
See: General Description ofDevelopment of a Decision Support Systemfor Trumpeter Swan managementBackground and Justification: A decision support system can be described as an interactive, computer-based system
designed to help decision makers solve poorly structured problems. Using a combination of models, analytical techniques, andinformation retrieval, such systems help develop and evaluate appropriate alternatives (Adelman 1992; Sprague and Carlson1982). Decision support systems should focus on strategic decisions, not operational ones. More specifically, they shouldcontribute to reducing the uncertainty faced by managers when they need to make decisions regarding future options (Grahamand Jones 1988). It is in this light that we have chosen to develop a decision support system to assist biologists with themanagement of the Rocky Mountain population of trumpeter swans (Cygnus buccinator).The many agencies represented by the Rocky Mountain Population of Trumpeter Swans Subcommittee of the Pacific FlywayCouncil (Subcommittee) wish to be able to integrate what is known about swan ecology into a model that can be used tosimulate and test different management scenarios. They are concerned by their lack of a mechanism to objectively exploremanagement options and identify critical information gaps that will contribute towards population recovery. They recognizethat their decision making is cyclic, and they wish to iteratively plan, implement, evaluate, and improve their managementstrategies. Unfortunately, optimizing any management of migratory birds throughout a flyway with cyclic planning is so
complex that it is often all but impossible to implement without computerized decision support (Sojda et al. 1994). And, pastconditions and future needs are ecological constraints to current decisions. Distributed decision making approaches suitproblems where the complexity prevents an individual decision maker from conceptualizing, or otherwise dealing with theentire problem (Boland et al. 1992; Brehmer 1991). The Swan Decision Support System will allow realistic and
ecologically-based management for migratory birds at multiple geographic and temporal scales. It will do this through
implementations of artificial intelligence methods such as expert systems (Rich and Knight 1991, Russell and Norvig 1995),blackboards (Corkill 1991, Nii 1986a, 1986b), and cooperative distributed problem solving (Carver et al. 1991, Durfee et al.1989).
The USDI - Fish and Wildlife Service has been the primary federal agency recommending the development of a SwanDecision Support System. Through their encouragement, the primary principle investigator (Sojda) has focused on thisproblem in pursuing a PhD at Colorado State University. Part of the work outlined in this study plan is intended to form thebasis for his dissertation. Field managers in both the USDI - National Park Service and the USDA - Forest Service have
expressed an interest in this research, as well. State waterfowl biologists from Oregon, Idaho, Montana, Wyoming, Utah, andNevada have also supported the concept.
Objectives: The broad thesis proposed is: cooperative distributed problem solving can be used to implement a flyway
approach to trumpeter swan management. Unfortunately this is not directly testable for several reasons. Foremost are (1) thelack of any gold standard, in an ecological sense, against which the system's performance can be evaluated; (2) the need tohave a working system that is forward-looking, simulating possible future scenarios as they are actually unfolding; and (3) thecomplications of verification and validation of any knowledge-based system that relies heavily on heuristics. Therefore, wehave identified hypotheses (in Objective B) at a lower-level than the broad thesis. Based on the outcomes from testing thesehypotheses, a qualitative assessment of broad goal attainment will be made. This project has two objectives:
Objective A. Provide a decision support system that allows the Subcommittee to examine management actionsaddressing population and migration objectives at a flyway scale, and allows individual land managers to evaluatemanagement actions at a site specific scale.
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Objective B. Test hypotheses regarding ecological issues and system operation.
The following hypotheses are those previously approved by Sojda's graduate committee as the basis of his dissertation.
1. Are trumpeter swan management potentials increased by decision support technology that provides the
capability to plan in multiple geographic and temporal scales? For the technology to foster this, swan distributionsas suggested by the Subcommittee need to be simulated, habitat recommendations must be satisfactory forsupporting increasing populations, and recommendations must be reliable over a specified time period.(Management potentials are those ecological conditions that can be exploited in pursuing trumpeter swan
objectives. Included are habitat quality, quantity, distribution, and availability, and freedom from disturbance.)Specific hypotheses to be subsequently tested are:
a. Recommendations are provided that simulate distributions of trumpeter swans in time and spacedifferent than those observed during 1985-1994.
b. Recommended acreages and availability of wetland habitats are satisfactory for maximizing thenumbers of trumpeter swans and distributing them appropriately by season.
c. Simulated distributions of swans differ when the system is not allowed to consider futurepopulation objectives.
d. Simulated distributions of swans differ when the system is not allowed to consider wetland habitatconditions and related future constraints and options.
e. Simulated distributions of swans differ when the system is not allowed to consider the effects ofdisturbance.
f. There is an optimum time scale over which the system can make effective recommendations, interms of both simulating long term consequences, and exceeding the capabilities of the knowledgeand rule bases. This will establish temporal bonds beyond which the decision support system shouldnot be used.
g. Recommendations do not allow simulated swan populations and distributions to fall belowpredetermined levels.
2. Does cooperative distributed problem solving among refuges, parks, other management areas, and the internalknowledge bases effectively integrate local management actions with small-scale landscapes? For this integrationto occur, information must be shared among human and electronic nodes, individual knowledge bases shouldcontribute to recommendations, and principles of adaptive management must be incorporated. Hypotheses to besubsequently tested are:
a. The system support module directs the internal rules so that individual local areas share informationwith each other and with the knowledge bases.
b. Simulations using the knowledge bases as actual inputs result in increased trumpeter swanmanagement potentials when compared with simulations based on random inputs.
c. Certain individual knowledge bases have greater influence on swan management recommendationsthan others as evaluated using sensitivity analysis.
d. The system is able to identify information gaps. When it can, it is able to prioritize the contributionthat eliminating each gap would make towards increasing the precision of the recommendations.
Procedures: Figure 1 shows the detailed framework that will be the Swan Decision Support System. Key elements are thefocus on cooperative distributed problem solving, and the organization of the knowledge bases, databases, and outputs. To alarge degree, this is a complex constraint satisfaction problem. Constraints include population objectives, ecological
principles represented in the knowledge bases, on-the-ground management capabilities, past management actions, potentialmanagement actions in the future, and implementations of adaptive management. Based on this and on the input from theSubcommittee and other swan experts, we have identified four management questions to be addressed through decision
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support system simulations:
Simulation #1. If a particular management action is implemented at a particular site and a particular time, what arethe consequences for that site and for other sites in the flyway?
Simulation #2. Given an objective for spatial and temporal distribution of swans, what is the best set of
management actions across all sites to achieve this? The decision support system will also have the capability forthe manager to provide an alternative objective.
Simulation #3. Given some subset of management action(s) across all sites, and given an objective for spatial andtemporal distributions of swans, what is the best complementary subset of management actions to achieve this?Simulation #4. Given a satisfactory set of management actions across all sites to achieve an objective for swandistribution, if an alternative management action were to be implemented at a particular site, what are theconsequences for that site and for other sites in the flyway in terms of reaching their respective objectives?
To address Simulation #1, a blackboard approach will be taken. When a particular management action is proposed for aparticular site, that information will be posted to the blackboard. Demons residing there will fire as necessary to activate theuse of appropriate rules and expert systems to simulate the effects of the proposed action for the current time at that site, aswell as at other sites in the flyway. New and impending constraints that the proposed action will impose on futuremanagement will also be generated and presented.
To address Simulations #2-4, a more complex search of the solution space will be required, and cooperative distributedproblem solving will be used. Each geographic node in the system will need to function both independently and
collaboratively with themselves and with the knowledge bases, exchanging \"tentative and partial results in order to convergeon a solution\" (Carver et al. 1991). These more complex simulations will require the concurrent development and posting ofpartially completed plans and potential management options from all geographic sites. The goal is to find a satisfactory set ofsolutions for management at all sites. This will be done by sharing information among themselves and with the knowledgebases and databases, and by recursively searching for a set of management options that satisfies the population level anddistribution objectives, and that addresses the constraints in the system.
This project will build the decision support system in two distinct phases. Phase One will assemble a prototype system todemonstrate the feasibility of applying blackboards and cooperative distributed problem solving to flyway management oftrumpeter swans. It will not concentrate on methods of input and output, but rather on the underlying algorithms to addressSimulations #1 and #2. Fundamental knowledge bases and databases will be built. Phase One will conclude during year threewith the testing of the above hypotheses and the completion of Sojda's dissertation.
Phase Two will be dependent on the amount of programming and travel support available to effectively interact with swanmanagers, and is scheduled to begin in year 3. It will involve major expansion, refinement, and enrichment of the rudimentaryknowledge bases to increase the system's applicability to swan management issues. The system will also be expanded toaddress Simulations #3 and #4. During Phase Two graphical user interfaces for input and output will be developed and fieldtested that will allow swan managers to use the models developed in Phase One on their own and without significant technicalcomputer support. It is anticipated that the system will be accessible via the internet as a telnet or web application. In contrastduring Phase One, simulations will be available but require some direct guidance of project personnel.
Data Handling and Analysis: Verification and validation of knowledge-based systems is known to be more problematic thanin other modeling efforts for many reasons (Gupta 1991). Under some conditions, modeling research can test performanceagainst a preselected gold standard. Often, as in this case, such a standard does not exist. This is particularly true with nearreal-time decision support that is expected to predict and guide future scenarios while those scenarios are, in fact, unfolding.Also, not only is it important for a system to handle the most common cases, it ought to be able to deal with extreme events.This latter attribute is one often only found with human experts. To overcome such problems, analysis can focus onverification and validation of the system. The \"Objectives\" section of this Study Plan delineates the hypotheses to beexamined in this vein. Adelman (1992) hinges successful implementation of decision support and expert systems on
incorporating three evaluation procedures, and they are incorporated in our verification and validation methods. However, weconsider user satisfaction as part of validation.
Verification is ensuring that the system is internally complete, coherent, and logical. We will ensure that the rules associatedwith the system support module direct the system internally to adequately and appropriately share information in a
cooperative distributed problem solving framework. In procedural programming environments, modularity allows for morestraightforward verification than in symbolic, knowledge-based systems. In the latter, especially those using backward
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chaining inference engines, the number of possible states and interactions among those states eliminates the possibility ofmore traditional, exhaustive testing (Geissman and Schultz 1988). Pedersen (1989a, 1989b, 1989c) summarizes the need toconsider knowledge base development as part of good verification procedures, and describes some of the common pitfalls toavoid. Verification of this system will be an iterative process of testing and altering the system for improvement. At a
minimum, outputs resulting from using the internal knowledge bases as input ought to provide more useful recommendationsthan random input of knowledge.
Validation relates to examining whether the system is providing a realistic and useful model of swan ecology and
management. Sprague and Carlson (1982) recommend that an organization building their first decision support system
recognize that it essentially is a research activity, and that evaluation should center on a general, \"value analysis\". They statethat iterative prototyping will ensure a quality product from the managers' perspectives, but recognize the qualitative nature ofsuch evaluation. Our approach is an attempt to add analytic and quantitative rigor beyond that. Sensitivity analysis can be apowerful tool for validation, especially for heuristic-based systems, and for systems where few or no test cases are availablefor comparison (O'Keefe et al. 1987). Another issue suggested by Rushby (1991) is that it is necessary to show not only howwell a system performs, but also to show that it can avoid a catastrophic recommendation. This is important in a species liketrumpeter swan where there is great concern for low population levels. Wilkins and Buchanan (1986) provide one option,using penalty constraints, for debugging such situations when reasoning under uncertainty. However, their algorithms arespecific to systems that provide diagnoses, and do not directly apply here.
It is sometimes possible to test expert system performance against an independent panel of experts (O'Keefe et al. 1987). Wewill not do that in this case for two reasons. First, the panel of experts needed for such an evaluation would be the samepeople who will be closely connected to system development itself. This would add such confounding effects that no
reasonable experimental design is feasible. Second, one of the basic tenets of distributed decision making is that the system isaddressing questions that are beyond the capability of single persons to conceptualize and solve (Boland et al. 1992; Brehmer1991).
Validation of our system will ensure that the recommendations for timing of availability of habitats will be sufficient tosupport the requisite number of birds. We will also examine whether recommended distributions of swans change when thesystem is not allowed to consider particular parameters such as population objectives, wetland habitat conditions, ordisturbance. Again, validation will be an iterative process. The overall intent will be to determine whether cooperativedistributed problem solving is an effective technique for imparting a flyway management approach to the Rocky Mountainpopulation of trumpeter swans. Additionally, we will query swan managers and experts about their satisfaction with thesystem. The encompassing question to be addressed by this qualitative and quantitative mix of verification and validation issimply: Was Objective A achieved?
Users: Members of the Rocky Mountain Population of Trumpeter Swan Subcommittee will be the primary users. It is
composed of state wildlife agency waterfowl biologists, state and federal land managers, Fish and Wildlife Service's RegionalMigratory Bird Coordinators and Flyway Representatives, and university experts.
Technology Transfer: Because the development of this system can only occur with close collaboration with the communityof swan managers, technology transfer is part and parcel with the research effort. A rough prototype has already been
developed and presented to a meeting of the Subcommittee, and subsequently comprised the base of an experimental webpage[http://webmmesc.mesc.usgs.gov/swan]. This will be continued but migrated to Sojda's workstation. Small workshops andpersonal interactions will continue throughout the project as a result of knowledge engineering efforts and prototype testing.The system itself is scheduled to be developed using Exsys software on a PC, and then served as a Unix application on theworkstation. It will be accessible to the managers either as a telnet or web application via the internet. Likely other internettechnologies will be developed and available by the time the system is ready for final distribution.
A minimum of two manuscripts will be submitted to scientific journals, and presentations will be made at two scientificmeetings.
Location: Richard Sojda is located with the Greater Yellowstone Research Group of MESC at Montana State University in Bozeman, MT. Development will primarily be accomplished there, with the system residing on a Sun workstation withinternet access in his office. Sojda is completing his PhD at Colorado State University and resources there will also beutilized. David Hamilton is located at MESC in Fort Collins, CO.
Figure 2 depicts primary locations of concern to swan managers, showing the geographic venue of the project.Work and Product Schedule (by Calendar Year):
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Tasks & Products
Gather ideas from Subcommittee and other experts; identify managementactions to be included in simulations
Design and flowchart blackboard component - Simulation #1&2Develop cooperative distributed problem solving component - Simulation#1&2
Initial knowledge engineering and database development; observe swan fieldbehavior and ecology; conduct review of literatureValidate and field test prototypeTest hypothesesComplete dissertation
Expanded knowledge engineering and field observation
Design and flowchart cooperative distributed problem solving component -Simulation #3&4
Develop cooperative distributed problem solving component - Simulation#3&4
Validate and field test system
Develop graphical user interface for input/output
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Investigators:
Principal: Richard S. Sojda [web page] - sojda@montana.edu [email]
Collaborator: David B. Hamilton [web page] - david_b_hamilton@usgs.gov [email]Other collaborators include:
Dr. John E. Cornely, Regional Migratory Bird Coordinator, USDI - Fish and Wildlife Service,Denver, CO ; john_cornely@fws.gov
Dr. Denis J. Dean , Assistant Professor, Natural Resources Information Systems, Department ofForest Sciences, Colorado State University , Fort Collins, CO; denis@cnr.colostate.eduDr. Leigh H. Fredrickson, Professor & Director, Gaylord Laboratory, University of Missouri Puxico, MO; leigh_fredrickson@muccmail.missouri.edu
,
Dr. Daniel Goodman, Professor, Environmental Statistics Group, Biology Department, Montana StateUniversity , Bozeman, MT; goodman@rivers.oscs.montana.edu
Dr. Adele E. Howe , Assistant Professor, Department of Computer Science, Colorado StateUniversity , Fort Collins, CO; howe@cs.colostate.edu
Dr. John B. Loomis, Associate Professor, Department of Agricultural and Resource Economics,Colorado State University , Fort Collins, CO; jloomis@ceres.agsci.colostate.edu
References:
Adelman, L. 1992. Evaluating decisions support and expert systems. John Wiley and Sons, Inc. New York, N Y. 232 pages.Boland, R. J., A. K. Mahewshwari, D. Te'eni, D. G. Schwartz, and R. V. Tenkasi. 1992. Sharing perspectives in distributed
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decision making. Pages 306-313 in: Proceedings of the Conference on Computer-Supported Cooperative Work. Associationfor Computing Machinery. New York, N Y.
Brehmer, B. 1991. Distributed decision making: some notes on the literature. Pages 3-14 in: Distributed Decision Making:Cognitive Models for Cooperative Work, J. Rasmussen, B. Brehmer, and J. Leplat, editors. John Wiley and Sons, Chichester,England.
Carver, N., Z. Cvetanovic, and V. Lesser. 1991. Sophisticated cooperation in FA/C distributed problem solving systems.Pages 191-198 in: Proceedings of the National Conference on Artificial Intelligence 1991.Corkill, D. D. 1991. Blackboard Systems. AI Expert 6(9): 40-47.
Durfee, E. H., V. R. Lesser, and D. D. Corkill. 1989. Cooperative distributed problem solving. Pages 84-147 in: The
handbook of artificial intelligence, vol. IV, A. Barr, P. R. Cohen, and E. A. Feigenbaum, editors. Addison-Wesley. Reading,MA.
Geissman, J. R., and R. D. Schultz. 1988. Verification and validation of expert systems. AI Expert 3(2):26-33.
Graham, I., and P. L. Jones. 1988. Expert systems: knowledge, uncertainty, and decision. Chapman and Hall. New York, NewYork. 363 pages.
Gupta, U. 1991. Validating and verifying knowledge-based systems. IEEE Computer Society Press. Washington, DC. 423pages.
O'Keefe, R. M., O. Balci, and E. P. Smith. 1987. Validating expert system performance. IEEE Expert 2(4):81-90.Pedersen, K. 1989a. Well structured knowledge bases - part I. AI Expert 4(4):44-55.Pedersen, K. 1989b. Well structured knowledge bases - part II. AI Expert 4(7):45-48.Pedersen, K. 1989c. Well structured knowledge bases - part III. AI Expert 4(11):36-41.
Rich, E., and K. Knight. 1991. Artificial intelligence. McGraw-Hill, Inc. New York, NY. Second edition. 621 pages.Rushby, J. 1991. Validation and testing of knowledge-based systems: how bad can it get? pages 77-83 in: Validating andverifying knowledge-based systems. U. Gupta, editor. IEEE Computer Society Press. Washington, DC.
Russell, S., and P. Norvig. 1995. Artificial intelligence: a modern approach. Prentice-Hall, Inc. Englewood Cliffs, NJ. 932pages.
Sojda, R. S., D. J. Dean, and A. E. Howe. 1994. A decision support system for wetland management on national wildliferefuges. AI Applications 8(2):44-50.
Sprague, R. H., Jr. and E. D. Carlson. 1982. Building effective decision support systems. Prentice-Hall. Englewood Cliffs, NJ.329 pages.
Wilkins, D. C., and B. G. Buchanan. 1986. On debugging rule sets when reasoning under uncertainty. Proceedings of the FifthNational Conference on Artificial Intelligence (AAAI-86) 1:448-454.
General Description of Development of a Decision Support System for Trumpeter Swan Management Richard Sojda Staff Page List of MESC Research ProjectsFor more information concerning page content, please contact: Richard Sojda, sojda@montana.eduU.S. Department of the InteriorU.S. Geological Survey6 of 719/02/01 2:10 PM
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