An Information Fusion Game Component

In emergency management and in military operations, command and control comprises the collection of functions, systems and staff personnel that one or several executives draw on to arrive at decisions and seeing that these decisions are carried out. The large amount of available information coupled with modern computers and computer networks brings along the potential for making well-informed and quick decisions. Hence, decision-making is a central aspect in command and control, emphasizing an obvious need for development of adequate decision-supporting tools to be used in command and control centers. However, command and control takes place in a versatile environment, including both humans and artifacts, making the design of useful computer tools both challenging and multi-faceted. This thesis deals with preparatory action in command and control settings with a focus on the strategic properties of a situation, i.e., to aid commanders in their operational planning activities with the utmost goal of ensuring that strategic interaction occurs under the most favorable circumstances possible. The thesis highlights and investigates the common features of interaction by approaching them broadly using a gaming perspective, taking into account various forms of strategic interaction in command and control. This governing idea, the command and control gaming perspective, is considered an overall contribution of the thesis. Taking the gaming perspective, it turns out that the area ought to be approached from several research directions. In particular, the persistent gap between theory and applications can be bridged by approaching the command and control gaming perspective using both an applied and a theoretical research direction. On the one hand, the area of game theory in conjunction with research findings stemming from artificial intelligence need to be modified to be of use in applied command and control settings. On the other hand, existing games and simulations need to be adapted further to take theoretical game models into account. Results include the following points: (1) classification of information with proposed measurements for a piece of information's precision, fitness for purpose and expected benefit, (2) identification of decision help and decision analysis as the two main directions for development of computerized tools in support of command and control, (3) development and implementation of a rule based algorithm for map-based decision analysis, (4) construction of an open source generic simulation environment to support command and control microworld research, (5) development of a generic tool for prediction of forthcoming troop movements using an algorithm stemming from particle filtering, (6) a non-linear multi-attribute utility function intended to take prevailing cognitive decision-making models into account, and (7) a framework based on game theory and influence diagrams to be used for command and control situation awareness enhancements. Field evaluations in cooperation with military commanders as well as game-theoretic computer experiments are presented in support of the results.

[1]  M. Johansson,et al.  Particle filter-based information acquisition for robust plan recognition , 2005, 2005 7th International Conference on Information Fusion.

[2]  David S Alberts,et al.  Network Centric Warfare: Developing and Leveraging Information Superiority , 1999 .

[3]  Ross D. Shachter.  Evaluating Influence Diagrams , 1986, Oper. Res..

[4]  Rina Azoulay-Schwartz,et al.  Negotiation on Data Allocation in Multi-Agent Environments , 1997, Autonomous Agents and Multi-Agent Systems.

[5]  Avi Pfeffer,et al.  Representations and Solutions for Game-Theoretic Problems , 1997, Artif. Intell..

[6]  D. J. Barrett,et al.  Model-Data Fusion , 2003 .

[7]  Ronald A. Howard,et al.  Influence Diagrams , 2005, Decis. Anal..

[8]  Daphne Koller,et al.  Multi-Agent Influence Diagrams for Representing and Solving Games , 2001, IJCAI.

[9]  B. Stengel,et al.  COMPUTING EQUILIBRIA FOR TWO-PERSON GAMES , 1996 .

[10]  Robert Suzic,et al.  Representation and Recognition of Uncertain Enemy Policies Using Statistical Models , 2004 .

[11]  Bernhard von Stengel,et al.  Fast algorithms for finding randomized strategies in game trees , 1994, STOC '94.

[12]  Gary E. Horne.  Maneuver warfare distillations: essence not verisimilitude , 1999, WSC '99.

[13]  Michael Wooldridge,et al.  Game Theory and Decision Theory in Multi-Agent Systems , 2002, Autonomous Agents and Multi-Agent Systems.

[14]  Kai Virtanen,et al.  Modeling Air Combat by a Moving Horizon Influence Diagram Game , 2006 .

[15]  D. Koller,et al.  Efficient Computation of Equilibria for Extensive Two-Person Games , 1996 .

[16]  Ning Xiong,et al.  Multi-sensor management for information fusion: issues and approaches , 2002, Inf. Fusion.

[17]  Stefan Arnborg,et al.  Bayesian Games for Threat Prediction and Situation Analysis , 2004 .

[18]  Eitan Zemel,et al.  Nash and correlated equilibria: Some complexity considerations , 1989 .

[19]  Berndt Brehmer,et al.  Using Microworlds To Study Intuitive Battle Dynamics: A Concept For The Future , 1999 .

[20]  Peter Thunholm,et al.  Military decision making and planning : towards a new prescriptive model , 2003 .

[21]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[22]  Edmund H. Durfee,et al.  Rational Coordination in Multi-Agent Environments , 2000, Autonomous Agents and Multi-Agent Systems.

[23]  Klas Wallenius,et al.  Support for situation awareness in command and control , 2004 .

[24]  Jonathan Schaeffer,et al.  Approximating Game-Theoretic Optimal Strategies for Full-scale Poker , 2003, IJCAI.

[25]  Joel Brynielsson,et al.  Game environment for command andcontrol operations (GECCO) , 2001 .

[26]  Massimo Warglien,et al.  Playing the Wrong Game: An Experimental Analysis of Relational Complexity and Strategic Misrepresentation , 2008, Games Econ. Behav..

[27]  Paul E. Lehner,et al.  Using Hypergames to Increase Planned Payoff and Reduce Risk , 2002, Autonomous Agents and Multi-Agent Systems.

[28]  Ya'akov Gal,et al.  A language for modeling agents' decision making processes in games , 2003, AAMAS '03.

[29]  B. Butler,et al.  "Game Commander"-applying an architecture of game theory and tree lookahead to the command and control process , 1994, Fifth Annual Conference on AI, and Planning in High Autonomy Systems.

[30]  David M. Kreps,et al.  Game Theory and Economic Modelling , 1992 .

[31]  Eric Horvitz,et al.  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users , 1998, UAI.

[32]  James Llinas,et al.  Revisiting the JDL Data Fusion Model II , 2004 .

[33]  Joacim Rydmark,et al.  Some research results obtained with DKE : A dynamic war-game for experiments , 2004 .

[34]  S. Arnborg,et al.  Refinements of the command and control game component , 2005, 2005 7th International Conference on Information Fusion.

[35]  H. Kuk.  On equilibrium points in bimatrix games , 1996 .

[36]  Stig K. Andersen,et al.  Probabilistic reasoning in intelligent systems: Networks of plausible inference , 1991 .

[37]  S. Zamir,et al.  Formulation of Bayesian analysis for games with incomplete information , 1985 .

[38]  Donald E. Knuth,et al.  The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm , 1981, ICALP.

[39]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.

[40]  B. Stengel,et al.  Efficient Computation of Behavior Strategies , 1996 .

[41]  Richard W. Cottle,et al.  Linear Complementarity Problem. , 1992 .

[42]  Henrik I. Christensen,et al.  A game theoretic model for management of mobile sensors , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[43]  Erik Durschmied.  The Hinge Factor: How Chance and Stupidity Have Changed History , 1999 .

[44]  Thomas P Coakley,et al.  Command and Control for War and Peace , 1992 .

[45]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[46]  Joel Brynielsson,et al.  Enhanced Situation Awareness using Random Particles , 2005 .

[47]  James Moffat,et al.  Complexity Theory and Network Centric Warfare , 2003 .

[48]  John C. Harsanyi,et al.  Games with Incomplete Information Played by "Bayesian" Players, I-III: Part I. The Basic Model& , 2004, Manag. Sci..

[49]  Stefan Arnborg,et al.  Information awareness in command and control: precision, quality, utility , 2000, Proceedings of the Third International Conference on Information Fusion.