Silverman, Barry G
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Publication Athena's Prism - A Diplomatic Strategy Role Playing Simulation for Generating Ideas and Exploring Alternatives(2005-05-01) Silverman, Barry G; Rees, Richard L; Toth, Jozsef A; Cornwell, Jason; O'Brien, Kevin; Johns, Michael; Caplan, MartyIntelligence analysts must clear at least three hurdles to get good product out the door: cognitive biases, social biases and self-imposed organizational impediments. Others (e.g., Gilovich, et al., Heuer, and Kahneman and Tversky), explain the cognitive processes that can help or trip us. A less well mapped set of dangers arises in the social dynamics of communicating tasking, working with other analysts, editing and customer interaction. Finally, the mere fact of a unit's published record creates analytic inertia - an argument at rest tends to stay at rest and one in motion (i.e., ambiguous or uncertain) tends to stay in motion. (A variation of this includes groupthink.)Publication Human Terrain Data – What Should We Do With It?(2007-12-01) Silverman, Barry GWhat are we in the Modeling & Simulation (M&S) community to do with the volumes of 'human terrain' data now being published by the military and others in databases of the demographics and needs/values/norms of populations of interest? This paper suggests that the M&S community would be remiss if it did not rise to this challenge and suggest next steps for the use of this Human Terrain (HT) data resource. These datasets are a key asset for those interested in synthesis of two major agent-based modeling paradigms – the cognitive and the social – as this paper argues. We pursue this argument with a case study integrating a cognitive agent environment (PMFserv) and a social agent environment (FactionSim) and applying them to various regions of interest (Iraq, SE Asia, Crusades) to assess their validity and realism.Publication What is a Good Pattern of Life Model? Guidance for Simulations(2018-08-01) Silverman, Barry G; Bharathy, Gnana K.; Weyer, NathanWe have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each.Publication Modeling and Simulating Terrorist Decision-making: A 'Performance Moderator Function' Approach to Generating Virtual Opponents(2001-05-01) Weaver, Ransom; Silverman, Barry G; Shin, Hogeun; Dubois, RichardAn elusive goal in virtual training environments is to be able to dial up the opponent of choice – e.g., the Iraqi Republican Guard, an Hamas-type of Suicide Bomber, or the clandestine minions of Bin Laden, as a few examples. In researching alternative ways to offer such a "dial up" capability, our focus thus far is to analyze actual organizations to identify "individual differences" in the form of Performance Moderator Function scorecards and a hierarchical game theoretic approach that captures the situation, organization, population, ideologic/motivation, strategic, and tactical layers of their decision making. We are also crafting a tool that can use the scorecards to semi-automatically assemble and deploy non-traditional Semi-Automated Forces or agents on a virtual battlefield. As an initial proof of concept test, we have manually applied the approach to a scenario involving a bank bomber approaching a vehicle checkpoint. The results to date indicate the approach seems to be a useful representational formalism for generic, implementation-free models of terrorist organizations and the behavior of their members. Our next steps will be to scale up the approach and try to implement it as a terrorist generator for an existing virtual-reality training environment.Publication An embeddable testbed for insurgent and terrorist agent theories: InsurgiSim(2008-11-01) Silverman, Barry G; Normoyle, Aline; Kannan, Praveen; Pater, Richard; Chandrasekaran, Deepthi; Bharathy, Gnana KMany simulators today contain traditional opponents and lack an asymmetric insurgent style adversary. InsurgiSim prototypes an embeddable testbed containing a threat network of agents that one can easily configure and deploy for training and analysis purposes. The insurgent network was constructed inside a socio-cognitive agent framework (FactionSim-PMFserv) that includes: (a) a synthesis of best-of-breed models of personality, culture, values, emotions, stress, social relations, mobilization, as well as (b) an IDE for authoring and managing reusable archetypes and their task-sets (Section 2). Agents and markups in this library are not scripted, and act to follow their values and fulfill their needs. So it's desirable to profile the agents (eg, faction leaders, cell logisticians, followers, bomb maker, financier, recruiter, etc.) as faithfully to the real world as possible. Doing this will improve the utility of InsurgiSim for studying what may be driving the insurgent agents in a given area of operation as Section 3 explains. InsurgiSim's bridge is an HLA federate and can be embedded to drive all or some of the insurgent agents in a 3rd party simulator. Three such examples are summarized in Section 4. The paper closes with next steps to improve InsurgiSim's capabilities and utility.Publication Enhancing the Behaviorial Fidelity of Synthetic Entities with Human Behavior Models(2004-05-12) van Lent, Michael; McAlinden, Ryan; Probst, Paul; Silverman, Barry G; O'Brien, Kevin; Cornwell, JasonHuman-behavior models (HBMs) and artificial intelligence systems are called on to fill a wide variety of roles in military simulations. Each of the "off the shelf" human behavior models available today focuses on a specific area of human cognition and behavior. While this makes these HBMs very effective in specific roles, none are single-handedly capable of supporting the full range of roles necessary in an urban military scenario involving asymmetric opponents and potentially hostile civilians. The research presented here explores the integration of three separate human behavior models to support three different roles for synthetic participants in a single simulated scenario. The Soar architecture, focusing on knowledge-based, goal-directed behavior, supports a fire team of U.S. Army Rangers. PMFServ, focusing on a physiologically/stress constrained model of decision-making based on emotional utility, supports civilians that may become hostile. Finally, AI.Implant, focusing on individual and crowd navigation, supports a small group of opposing militia. Due to the autonomy and wide range of behavior supported by the three human behavior models, the scenario is more flexible and dynamic than many military simulations and commercial computer games.Publication Blackboard System Generator (BSG): An Alternative Distributed Problem-Solving Paradigm(1989-03-01) Silverman, Barry G; Chang, Joseph S; Feggos, KostasThe classical blackboard model employs a number of relaxations of team decision theory that are commonly organized into three panels of AI heuristics, including: 1) a shared information panel that offers a capability for ensuring agent knowledge sharing, 2) a contract formalism for the agent and event scheduling, coordinating, and control panel, and 3) a blackboard panel for metalevel planning and guidance that offers whole situation recognition, top down reasoning, and adaptive learning. The nature and implications of these relaxations are explained in terms of the blackboard system generator (BSG) and via comparisons to what is done in other blackboard shells. Particular attention is paid to theoretical relaxations inherent in the classical blackboard model and to research opportunities arising as a result. Progress made to date to counteract adverse effects of some of these relaxations is described in terms of a project management/work breakdown paradigm adopted in BSG that: 1) alleviates the knowledge engineering bottlenecks of traditional blackboards and that provides BSG with a semantic rather than just syntactic understanding of blackboard control and scheduling; 2) allows a distributed problem-solving capability for connecting agents at virtual addresses on a logical network and that permits concurrent processing on any machine available on the network; 3) establishes an open architecture that includes techniques for integrating preexisting agent methods (e.g., expert systems, procedures, or data bases) while laying the foundation for assessing the impact of “black boxes” on the global and local objective functions; and 4) utilizes project management techniques for team agents planning as well as an analogical reasoner subsystem for BSG metaplanning and generic controlled learning. This latter item is supported by a connectionist scheme for its associative memory. The techniques of each of the three panels and of the four sets of paradigm-related advances are described along with selected results from classroom teaching experiments and from three applications using BSG to date.Publication What is a good pattern of life (PoL) model? – Guidance for simulations(2019-01-01) Silverman, Barry; Bharathy, Gnana; Weyer, Nathan; Sun, QiweiPublication Social Learning and Adoption of New Behavior in a Virtual Agent Society(2013-01-01) Nye, Benjamin D.; Silverman, Barry G.Social learning and adoption of new behavior govern the rise of a variety of behaviors: from actions as mundane as dance steps to those as dangerous as new ways to make IED detonators. However, agents in immersive virtual environments lack the ability to realistically simulate the spread of new behavior. To address this gap, a cognitive model was designed that represents well-known socio-cognitive factors of attention, social influence, and motivation that influence learning and adoption of a new behavior. To explore the effectiveness of this model, simulations modeled the spread of two competing memes in Hamariyah, an archetypal Iraqi village developed for cross-cultural training. Diffusion and clustering analyses were used to examine adoption patterns in these simulations. Agents produced well-defined clusters of early versus late adoption based on their social influences, personality, and contextual factors such as employment status. These findings indicate that the spread of behavior can be simulated plausibly in a virtual agent society and has the potential to increase the realism of immersive virtual environments.Publication Systems Social Seience: A Design Inquiry Approach for Stabilization and Reconstruction of Social Systems(2010-01-25) Silverman, Barry G.This paper explores novel approaches under the design inquiry paradigm that promise to help organizations better understand and solve socio-technical dilemmas. Design inquiry is contrasted with scientific inquiry (Section 1). Section 2 presents a meso-scale model of models methodology for design inquiry that synthesizes systems science, agent modeling and simulation, knowledge management architectures, and domain theories and knowledge. The goal is to focus computational science on exploring underlying mechanisms (white box modeling) and to support reflective theorizing and discourse to explain social dilemmas and potential resolutions. Section 3 then describes an evolving agent modeling and simulation testbed while Section 4 offers two gameworld applications that implement this approach and that serve as an example of the new types of instruments useful for systems social science. The conclusions wrapup by reviewing lessons learned about 10 criteria that have guided this research.