Pythagoras: A New Agent-based Simulation System - Northrop ...

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Pythagoras: A New Agent-based Simulation System Figure 1. Combat environment: Physical elements, human factors, and leadership are tightly coupled With those three research objectives in mind, Project Albert was conceived. Initially led by the U.S. Marine Corps Combat Development Command (MCCDC) at Quantico, Virginia, the project is currently headed by the Marine Corps Warfighting Laboratory, also at Quantico. It quickly expanded into an international effort involving eight other countries. The approach was to proceed in parallel along three different but related paths. First, Project Albert would harness the power of supercomputing technology to enable massive experimentation and analysis on a scale then rarely, if ever, attempted. Second, the consortium would develop a set of visualization tools to assist in the analysis of the experiments. Third, Project Albert commissioned the development of new agent-based simulations (called distillations) that did not start with the traditional physical foundations, but instead concentrated on the exploration of human factors and leadership in the combat environment. For a description of Project Albert, see the Web page [1]. Overview of Pythagoras Distillation Northrop Grumman was tasked to build a new distillation that used agent-based technology in diverse, innovative ways to model human behavior and its effect on combat and noncombat situations. Three design criteria were specified for the new distillation: • It must be easy enough to use that a military officer with a nontechnical degree could learn it in no more than eight hours. • It must incorporate fuzzy logic in some way. • It must be data farmable (i.e., able to run in batch mode on a parallel-processor supercomputer—specifically, at the Maui High Performance Computing Center in Maui, Hawaii). The resulting prototype distillation, christened Pythagoras, enables a user to create various intelligent agents and assign them behaviors based on motivators and detractors. 46 Weapons Sensors Platforms Ammunition Terrain Weather • Decision Making Intelligence Communication Inspiration Physical Elements Leadership Human Factors Technology Review Journal • Spring/Summer 2003 Morale Training Confidence Fatigue Fear Comradeship
Pythagoras: A New Agent-based Simulation System The agents can either act as individuals or be loosely or tightly controlled by one or more leader agents. Pythagoras is written in Java, making it platform-independent. It can be run in a supercomputer environment as a batch job, enabling tens of thousands of repetitions to be run in a short time; or it can be used and run interactively on a PC through a graphical user interface (GUI). Pythagoras offers a unique set of capabilities in the area of agent-based simulations: • Incorporates soft rules to distinguish unique agents • Uses desires to motivate agents into moving and shooting • Includes the concept of affiliation (established by sidedness, or color value) to differentiate agents into members of a unit, friendly agents, neutrals, or enemies • Allows for behavior-changing events and actions (called triggers) that may be invoked in response to simulation activities • Retains traditional weapons, sensors, and terrain Each capability is discussed below. Soft Rules to Create Individuality Each person has a unique interpretation of language. History offers countless examples of military orders whose intent was misinterpreted, leading to unexpected and sometimes disastrous consequences. In a military context, an order may be given as “Hold fire until the enemy gets close.” To a fighter pilot, close may be six miles, but an embassy security guard may interpret the same order to mean six feet. And even if several people agree on a distance—say, 400 feet—some will evaluate that distance a little long and be seen as trigger-happy, whereas others will evaluate it a little short and be seen as overcautious. Fuzzy logic attempts to capture the complex, dynamic nature of the world and the human interpretation of that world, while creating a mathematical underpinning that will allow a “crisp”-thinking digital computer to represent and act on it. However, for analysis, we determined that a strict interpretation and implementation of fuzzy logic might cloud the interpretation of the results of our Pythagoras experiments. Instead, we designed a feature for Pythagoras called soft decision rules, which not only assigns each agent its own threshold within the fuzzy-logic trade space but also ensures traceability. It avoids the “everything is gray” phenomenon that sometimes occurs in direct implementations of fuzzy logic. Pythagoras allows all decision variables and some human factors to be softened by the user. The approach is to reflect variation between individual agents by establishing a midpoint for the variable in question and then allowing the user to provide a uniformly distributed range around that value. When an agent is instantiated at the beginning of a scenario run, it selects its decision-variable values from the distribution at random. By controlling the spread, agents can be instantiated as very homogeneous (e.g., welltrained, disciplined military troops) or quite heterogeneous (e.g., a crowd of villagers), or some value in between. Each decision variable has its own control, so some aspects of behavior can be tight, others loose. Figure 2 illustrates three alternative degrees of individuality. Technology Review Journal • Spring/Summer 2003 47
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