2004 Command and Control Research and Technology Symposium The Power of Information Age Concepts and Technologies
A Scalable and Extensible Interactive Scenario Architecture for Distributed Command and Control Simulations
Magy Seif El-Nasr
Rashaad Jones
Michael McNeese
Assistant Professor
PhD Candidate
Associate Professor
School of Information Science and Technology
School of Information Science and Technology
School of Information Science and Technology
PennState University
PennState University
PennState University
Contact Person: Magy Seif El-Nasr
Assistant Professor School of Information Science and Technology Pennsylvania State University 001 Thomas Building University Park, PA 16802
[email protected]
Designing an effective interactive simulation to support and study distributed decisionmaking in areas of Command and Control (C2), combat simulation, civilian and military mission resource allocation involve the use of interactive dynamic scenarios that adapt to users’ goals and intentions. In this paper, we propose a multi-agent interactive dynamic scenario architecture that is scalable and extensible. Additionally, we propose integrating a user modeling and monitoring technique to yield adaptable interactive dynamic scenarios. The development and authoring of interactive dynamic scenarios is often hard and difficult to accomplish using current techniques. Many interactive scenario developers use decision trees, which, due to their exponential growth, do not scale and are difficult to author and modify. In addition, since predicting users’ behaviors is difficult, decision trees often yield very limiting and unfulfilling interactive simulations, because they do not stimulate thinking or interaction beyond the scripted paths. This drawback can be seen in many interactive virtual simulations. Furthermore, decision trees are authored at design time mainly using immediate user actions, which limits their ability to dynamically adapt to the users’ cumulative abilities or state. Some researchers recognized the need for a new technique for developing and representing interactive scenarios. Mateas and Stern proposed a HAP-based architecture for interactive narratives (Mateas and Stern 00, Mataes 01). Young proposed a plan-based interactive narrative architecture, where plans are revised depending on users’ actions (Young 00). Peter Weyhrauch proposed the use of game theory to adapt the scenario to user’s behaviors (Weyhrauch 97). These explorations, and many others, present major achievements to break the norm of decision tree-based interactive scenarios. Although these projects have succeeded in developing interactive scenarios that are more maintainable than decision trees, they do not scale and are time consuming to author. In addition, these architectures do not integrate users’ goals or intentions or use such information to select scenario events; thus, they yield inflexible scenarios that do not adapt suitably to users’ goals or behaviors. Alternatively, the proposed interactive dynamic scenario architecture will integrate a user modeling approach and divides problem solving among several layers. The contribution of the paper can be outlined as follows: •
The proposed architecture will enhance scalability and reuse by using a multiagent layered problem solving (Forbus and Kleer 1992) technique. This technique enhances scalability by dividing the problem into smaller problems, and thus reducing the number of rules and predicates processed at each layer. The architecture also enhances reuse since it decouples several scenario constructs, thus abstracting problem solving within different scenario components.
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In a second direction, the interactive scenario architecture will appropriately and automatically adapt to users’ goals by integrating two constructs: (1) a user model to adapt the scenario dynamically to player’s goals (McKee 97), and (2) a user monitoring technique to evaluate the failure or success of selected events and agent behaviors (Benedetti 94); some example user monitoring techniques include monitoring users’ actions to evaluate that his/her attention is directed towards the desired object/character.
The proposed approach is an implementation of a dynamic scenario architecture within a distributed command and control simulation, called NeoCITIES. The NeoCITIES’s task is both a renovation, as well as an expansion, of the original CITIES task (Wellens & Ergener, 1988). As with the original CITIES task, the Neo Command and Control (C2) Interactive Task for Identifying Emerging Situations (NeoCITIES) has been designed and developed for the purpose of studying group collaborative decision-making processes, knowledge acquisition and knowledge management using information acquired from multiple sources within a command and control (C2) setting. As such, NeoCITIES is essentially an adaptable problem interface, which allows the close examination of semiautonomous, spatially distributed decision-making teams. These decision-making teams can be presented with several different overarching, dynamic and detailed resource allocation problem scenarios, for which they are required to find suitable solutions meeting the needs of given constituents and working around various problem space constraints existing within the task. The scenarios designed and types of teams selected to be used in this simulation were purposefully designed to be contextually relevant to the concerns of the Department of Homeland Security in the areas of crisis management. These scenarios have each been taken and adapted from news stories of recent world events or from stories in popular media. Additionally, experts from the field (crisis management personnel that includes DHS intelligent analysts, police and fire authorities, etc) were interviewed to assist in the development of scenarios to provide a highly-realistic and context-relevant setting. At this time NeoCITIES is still work in progress. However, by the time of full paper submission we plan to demonstrate the capabilities of the interactive scenario architecture to dynamically adapt to users’ goals and report results from experiments to verify and evaluate the architecture’s scalability and ease of use. References R. Benedetti. (1994). Actor at Work, 6th ed. Englewood Cliffs: Prentice-Hall. M. Mateas, and A. Stern. (2000). Towards Integrating Plot and Character for Interactive Drama, Socially Intelligent Agents: The Human in the Loop AAAI Fall Symposium. M. Mateas. (2001). Interactive Drama, Research Proposal, Carnegie Mellon University, Pittsburgh. R. McKee. (1997). Story:Substance, Structure, Style, and the Principles of Screenwriting. New York: HarperCollins. K. Forbus and J. de Kleer. (1993). Building Problem Solvers. MIT Press. R. Wellens, and D. Ergener. (1988). The C.I.T.I.E.S. game: A computer-based situation assessment task for studying distributed decision making. Simulation & Games, 19(3), 304-327. P. Weyhrauch. (1997). Guiding Interactive Drama. PhD Thesis. Pittsburgh: Carnegie Mellon University. M. Young. (2000). Notes on the use of plan structures in the creation of interactive plot, AAAI Fall Symposium on Narrative Intelligence, MA.
