Abstract
Information is a force multiplier. Knowledge of the enemy's capability and intentions may be of far more value to a military force than additional troops or firepower. Situation assessment is the ongoing process of inferring relevant information about the forces of concern in a military situation. Relevant information can include force types, firepower, location, and past, present and future course of action. Situation assessment involves the incorporation of uncertain evidence from diverse sources. These include photographs, radar scans, and other forms of image intelligence, or IMINT; electronics intelligence, or ELINT, derived from characteristics (e.g., wavelength) of emissions generated by enemy equipment; communications intelligence, or COMINT, derived from the characteristics of messages sent by the enemy; and reports from human informants (HUMINT). These sources must be combined to form a model of the situation. The sheer volume of data, the ubiquity of uncertainty, the number and complexity of hypotheses to consider, the high-stakes environment, the compressed time frame, and deception and damage from hostile forces, combine to present a staggeringly complex problem. Even if one could formulate a decision problem in reasonable time, explicit determination of an optimal decision policy exceeds any reasonable computational resources. While it is tempting to drop any attempt at rational analysis and rely purely on simple heuristics, we argue that this can lead to catastrophic outcomes. We present an architecture for a ``complex decision machine'' that performs rational deliberation to make decisions in real time. We argue that resource limits require such an architecture to be grounded in simple heuristic reactive processes. We thus argue that both simple heuristics and complex decision machines are required for effective decision making in real time for complex problems. We describe an implementation of our architecture applied to the problem of military situation assessment.
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References
Adelman, L., Cohen, M.S., Bresnick, T.A., Chinnis, J.O. and Laskey, K.B. (1993) Real-Time Expert System Interfaces, Cognitive Processes, and task Performance: An Empirical Assessment. Human Factors 35(2), pp. 243-261.
Agre, P. (1989), The Dynamic Structure of Everyday life. Ph.D. dissertation, MIT.
Bratman, M., Israel, D. and Pollack, M. (1988), Plans and Resource-Bounded Practical Reasoning, Computational Intelligence 4, pp. 349-355.
Brooks, R. (1986), A layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1), pp. 14-23.
Brooks, R.R. and Iyengar, S. (1997), Multisensor Fusion: Fundamentals and Applications with Software, Prentice Hall.
von Clausewitz, C. (1991), On War, New York: Dorset Press.
Cohen, P. (1981), Trial by Fire: Understanding the Design Requirements for Agents in Complex Environments, AI Magazine 10(3), pp. 32-48.
D'Ambrosio, B. and Fehling, M. Constrained Rational Agency. In Proceedings of the International Conference on Systems, Man and Cybernetics. IEEE, November, 1990.
Forster, M. Fast and Frugal Heuristics in Machines: How Would One Make them Work, Minds and Machines, this issue.
Gigerenzer, G. and Todd, P. (eds). Simple Heuristics that Make Us Smart, New York: Oxford University Press, 1999.
Ginzberg, M., Chapman, D. and Schoppers, M. (1989), Planning and Reaction (with responses), AI Magazine. 10(4).
Hayes-Roth, B. (1985), A Blackboard Architecture for Control. Artificial Intelligence, 26(3): 251-253.
Hall, D. (1992), Mathematical Techniques in Multisensor Data Fusion, Artech House.
Heckerman, D. (1991), Probabilistic Similarity Networks, Boston: MIT Press.
Horvitz, E., Cooper. G. and Heckerman, D. Reflection and Action under Scarce Resources: Theoretical Principles and Empirical Study. In Proceedings of IJCAI89, IJCAI, August, 1989.
Howard, R. and Matheson, J. (1984), Influence Diagrams. In Howard, R. and Matheson, J. (eds), The Principles and Applications of Decision Analysis, Menlo Park, CA: Addison-Wesley.
Johnson-Laird, Newell, A. and Rosenbloom, P. (1987), SOAR: An Architecture for General Intelligence, Artificial Intelligence, 33(1), pp. 1-64.
Kline, G. (1996), The Effects of Acute Stressors in Decision Making. In Driskell, J.E. and Salas, E. (eds), Stress and Human Performance, pp. 49-88. Mahwah, NJ: Erlbaum.
Laskey, K.B. and Mahoney, S.M. (1997), Network Fragments: Representing Knowledge for Constructing Probabilistic Models. In Geiger, D. and Shenoy, P. (eds), Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference, San Francisco, CA: Morgan Kaufmann, pp. 334-341.
Mahoney, S.M. (1999), Network Fragments, Ph.D. Dissertation, School of Information Technology and Engineering, George Mason University, Fairfax, VA.
Mahoney, S.M. and Laskey, K.B. (1996), Network Engineering for Complex Belief Networks. In E. Horvitz and F. Jensen (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference San Francisco, CA: Morgan Kaufmann, pp. 370-378.
Mahoney, S.M. and Laskey, K.B. (1998), Constructing Situation-Specific Belief Networks. In G. Cooper and S. Moral (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference. San Francisco, CA: Morgan Kaufmann, pp. 370-378.
Martignon, L. and Laskey K.B. (1999), Taming Wilder Demons: Bayesian Benchmarks for Fast and Frugal Heuristics. In G. Gigerenzer and P. Todd (eds.), Simple Heuristics that Make Us Smart, New York: Oxford University Press.
Martignon, L. and Schmitt, M., Simplicity and Robustness of Fast and Frugal Strategies. Minds and Machines 9, pp. 565-593.
Morgan, B.B. Jr. and Bowers, C.A (1995), Teamwork Stress: Implications for Team Decision Making. In Guzzo, R.A., Salas, E. and Associates (eds.), Team Effectiveness and Decision Making in Organizations, pp. 262-290, San Francisco: Jossey-Bass Publishers.
Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems, San Francisco: Morgan Kaufmann.
Raiffa, H. and Schlaifer, R. (1961), Applied Statistical Decision Theory. Cambridge, MA: Harvard University Press.
Rosenschein, S. (1989), Synthesizing Information Tracking Automata from Environment Descriptions. In Proceedings on the First International Conference on Principles of Knowledge Representation and Metareasoning, AAAI.
Russell, S. (1989), Principles of Metareasoning. In Proceedings on the First International on Principles of Knowledge Representation and Metareasoning, AAAI.
Sadjadi, F. (ed.) (1996), Selected Papers on Sensor and Data Fusion, Society of Photo-optical Instrumentation Engineers.
Savage, L. (1972), The Foundations of Statistics. Dover Publications.
Sternberg, R. (1998), Cognitive Psychology. Harcourt Brace.
Sun-Tzu (1983), The Principles of War, New York: Delacorte Press.
Svenson, O. and Maule, A.J. (eds.) (1993), Time Pressure and Stress in Human Judgment and Decision Making, New York: Plenum Press.
Waltz, E. and Llinas, J. (1990), Multisensor Data Fusion, Artech House.
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Blackmond Laskey, K., D'Ambrosio, B., Levitt, T.S. et al. Limited Rationality in Action: Decision Support for Military Situation Assessment. Minds and Machines 10, 53–77 (2000). https://doi.org/10.1023/A:1008398822124
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DOI: https://doi.org/10.1023/A:1008398822124