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

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Pythagoras: A New Agent-based Simulation System unfolds. Because they are separate objects, different agent types can share the behavior templates, and there is no practical limit to the number of templates that can be created. Weapon Options Pythagoras allows agents to carry as many as three different weapons. Weapons can be either direct fire (which requires a line of sight) or indirect fire (which does not): • Direct-fire weapons have range-dependent probabilities of hit—and of kill, given a hit—which may be modified by the target agent’s vulnerability. • Indirect-fire weapons have circular-error-probable accuracies and lethal radii, both of which may be range-dependant. An indirect-fire weapon’s lethal radii can be modeled using either the traditional Carlton damage function or a “cookie-cutter” blast. The Carlton damage function describes the damage radiating out from a hit point through the following equation: where Pk is the probability of kill, distance is the distance of the target from the impact point, and radius is the size of the blast radius. The cookie-cutter blast causes an equal amount of damage to all objects within the blast radius, regardless of their distance from the impact point. Both weapon types have input rates of fire and limited ammunition. In Pythagoras, the target may be suppressed for multiple time steps by a hit by a directfire weapon or a near miss by an indirect-fire weapon that does not kill the target. Suppressed agents do not move, sense, or shoot, but they recover those capabilities after a user-input amount of time. At the user’s discretion, kills can be random (stochastic), deterministic (fractional damage), or a combination of both. Agents can also change each other’s colors, by using military countermeasures or nonlethal weapons that can act as paintball guns. An example of a nonlethal weapon would be propaganda that alters an agent’s affiliation in some way. Propaganda in the form of leaflets can be modeled as a direct-fire weapon aimed at a specific target. On the other hand, preaching or any other form of public speech or exhortation can be modeled as an indirect-fire weapon, aimed in a general direction and affecting everyone within range. Agents will automatically shoot at enemies within range, unless they are given a “hold fire” command that allows them to shoot only if fired on within a user-specified number of time steps. The user can assign agents multiple weapons and set the weapon selection criteria to be based on either the highest probability of hit (best chance that the target will be suppressed) or the lowest probability of kill (for a nonlethal situation). Multiband Sensors Sensors are also abstracted. Each agent may have up to three, each of which operates in a specific signature band, labeled A, B, or C. The sensors have a range-dependent probability of detection, which is modified by intervening terrain and a target agent’s detectability. For example, signature band A could be the bandwidth of the naked eye and signature band B might describe the bandwidth of an infrared device. A terrain feature, such as foliage, would thwart the naked eye’s view, but an infrared device could “see” the 52 P=e k –[( distance × distance)/( radius × radius)] , Technology Review Journal • Spring/Summer 2003
Pythagoras: A New Agent-based Simulation System thermal image of a camouflaged person standing in the foliage. Such foliage could be modeled in Pythagoras as having a concealment value of 100% in band A and 0% in band B. Sensors also have a field of view and, for modeling humans, a probability that the agent is looking forward (i.e., in the direction of travel), to the side, or to the rear. Terrain features reduce the probability of line of detection differently in each band. Representation of Terrain All agents exist in a user-defined playbox of up to 1000 × 1000 pixels. On the playbox, a user can create terrain features—polygons that have a floor, a ceiling, and factors for mobility; concealment in each of the three signature bands; and protection that reduces the weapon’s effectiveness. Currently, a pixel can be associated with at most one terrain feature. Agents can either move on the terrain or floor or operate at an altitude. Sample Scenarios Pythagoras has two major advantages: • It can be used quickly to assess various sample scenarios. • It can be combined with more complex and detailed models to provide insights into situations that may not be possible with other simulations. Rapid prototyping of scenarios, weapons, sensors, behavior patterns, etc., allows users to undertake complex problems in new ways without the burden of months of software development. Several sample scenarios are discussed below. Village Patrol. We have used Pythagoras to study crowd behavior in an antiterrorist/ force protection scenario, represented as a village patrol, developed for the Marine Corps to evaluate various night-vision devices in peacekeeping operations. Figure 4 shows a screen shot of the “play forward” capability of the Pythagoras GUI for that scenario. The scene is a village with buildings, parks, and streets, represented in Figure 4 as large areas of color—e.g., the geometric shapes such as rectangles and trapezoids denote buildings. The blue dots represent Marines, the red dots are peaceful villagers, and the orange dots are hostile villagers. The hostiles include a terrorist leader, located in a religious structure, who can communicate with the hostiles, warning them of the presence of the Marines. Marine agents patrol the village at night, inspecting the villagers and looking for terrorists. The Marines are directed in part by a scout/sniper team that is strategically placed to help spot villagers. The villagers and terrorists alike move toward the food distribution point at the harbor at the top of the screen. Peaceful villagers, once approached by Marines and found to have proper identification and no weapons, are directed to the food distribution point. Hostile villagers attempt to avoid the Marines and are shackled in place after the Marines find and inspect them. Measures of effectiveness (MOEs) include the number of villagers detected and fed, the number of hostiles shackled, and the number of hostiles reaching the food distribution point, a potentially disastrous event. We first used this scenario to establish a night-vision-capability base case by modeling the probability of detection for the Pythagoras sensors, applying the results of physics- Technology Review Journal • Spring/Summer 2003 53
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