Papers and Publications, Center of Excellence in Command, Control, Communications, and Intelligence
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This collection contains papers written by members and fellows of the C4I Center.
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Browsing Papers and Publications, Center of Excellence in Command, Control, Communications, and Intelligence by Subject "Decision support"
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Item Evaluation of Geospatial Digital Support Products(2010-04-20T14:50:40Z) Powell, Walter A.; Laskey, Kathryn Blackmond; Adelman, Leonard; Johnson, Ryan; Dorgan, Shiloh; Hieb, Micheal; Braswell, Kenneth; Powers, MichaelGeospatial Reasoning has been an essential aspect of military planning since the invention of cartography. Geospatial Digital Support Products (GDSPs) are ubiquitous within current military forces as well as civil and humanitarian organizations. Nevertheless, there is too little empirical evidence to quantify the military value of geospatial products to the warfighter. We conducted a hypothesis-driven experiment to evaluate the military value of the Battlefield Terrain Reasoning and Awareness – Battle Command (BTRA-BC) GDSP. Realistic scenarios and appropriate measures to assess performance were developed in collaboration with Subject Matter Experts (SMEs). The measures included time to completion, objectively assessed solution quality, subjectively assessed solution quality, and terrain understanding. BTRA-BC was integrated into the Army’s Digital Topographic Support System (DTSS). A within-subjects design was employed, in which the participants completed scenarios using DTSS with and without BTRA-BC functionality. Statistical analysis of the data indicated that when the participants used BTRA-BC functionality, they created outputs faster and of higher quality without reducing their knowledge of the impact of the terrain on military decision-making. This paper discusses the scope of the current experiment, the hypotheses, the experimental design, and the results.Item MEBN Logic: A Key Enabler for Network Centric Warfare(CCRP Publications, 2005-06) Costa, Paulo C. G.; Laskey, Kathryn B.; Takikawa, Masami; Pool, Michael; Fung, Francis; Wright, Edward J.Among the lessons learned from recent conflicts stands the dramatic change in the very way wars are fought. There are no more clear-cut enemies or allies; rules of engagement have become increasingly fuzzy; guerrilla and insurgent tactics are now commonplace: in short, the battlespace is a very different place from what it used to be. Furthermore, advances in sensor technology and network computing have brought a new element to the complex equation of warfare: information overload. Nowadays, instead of merely gathering information and displaying assets, command and control systems must be able to fill the gap between the glut of information arriving from a networked grid of sensors and the capacity of human commanders to make sense of it. In short, the quest today is for systems that work under the knowledge paradigm. Systems must automatically provide decision makers with a clear picture of what is happening, how it relates to the current situation, and what are the options and their respective consequences. Facing this challenge with technologies of the past is a recipe for failure. New, more powerful approaches are needed. The objective of this paper is to argue for two claims: (1) Bayesian decision theory is an appropriate technology for modeling human decision-making in complex, ambiguous scenarios; and (2) Bayesian reasoning technology is a promising enabler for Network Centric Warfare. To support both claims, we have applied Multi-Entity Bayesian Networks (MEBN) to model a historical tactical decision from the naval domain. MEBN is a breakthrough Bayesian reasoning system in which complex probabilistic models are constructed from modular components that can be replicated and combined in an infinite variety of ways. MEBN allows models to capture important and subtle aspects of objects and their interrelationships that would be impossible to model using existing technologies. We provide a brief overview of modeling in MEBN and then present our model and the outcome of applying it to a historical scenario. Our results clearly support the validity of our approach.Item Of Starships and Klingons: Bayesian Logic for the 23rd Century(AUAI Press, 2005-07) Laskey, Kathryn B.; Costa, Paulo C. G.Intelligent systems in an open world must reason about many interacting entities related to each other in diverse ways and having uncertain features and relationships. Traditional probabilistic languages lack the expressive power to handle relational domains, whereas classical first-order logic is sufficiently expressive but lacks a coherent plausible reasoning capability. Recent years have seen the emergence of a variety of approaches to integrating first-order logic, probability, and machine learning. This paper presents Multi-entity Bayesian networks (MEBN), a formal system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures. We present the logic using an example inspired by the Paramount Series Star Trek. MEBN semantics integrates random variables as formalized in mathematical statistics with model theoretic semantics for first-order logic.