Semantic correlation of behavior for the interoperability of heterogeneous simulations

A desirable goal of military simulation training is to provide large scale or joint exercises to train personnel at higher echelons. To help meet this goal, many of the lower echelon combatants must consist of computer generated forces with some of these echelons composed of units from different simulations. The object of the research described is to correlate the behaviors of entities in different simulations so that they can interoperate with one another to support simulation training. Specific source behaviors can be translated to a form in terms of general behaviors which can then be correlated to any desired specific destination simulation behavior without prior knowledge of the pairing. The correlation, however, does not result in 100% effectiveness because most simulations have different semantics and were designed for different training needs. An ontology of general behaviors and behavior parameters, a database of source behaviors written in terms of these general behaviors with a database of destination behaviors. This comparison is based upon the similarity of sub-behaviors and the behavior parameters. Source behaviors/parameters may be deemed similar based upon their sub-behaviors or sub-parameters and their relationship (more specific or more general) to destination behaviors/parameters. As an additional constraint for correlation, a conversion path from all required destination parameters to a source parameter must be found in order for the behavior to be correlated and thus executed. The length of this conversion path often determines the similarity for behavior parameters, both source and destination. This research has shown, through a set of experiments, that heuristic metrics, in conjunction with a corresponding behavior and parameter ontology, are sufficient for the correlation of heterogeneous simulation behavior. These metrics successfully correlated known pairings provided by experts and provided reasonable correlations for behaviors that have no corresponding destination behavior. For different simulations, these metrics serve as a foundation for more complex methods of behavior correlation

Chain of command in autonomous cooperative agents for battles in real-time strategy games

This paper investigates incorporating chain of command in swarm intelligence of honey bees to create groups of ranked co-operative autonomous agents for an RTS game in to create and re-enact battle simulations. The behaviour of the agents are based on the foraging and defensive behaviours of honey bees, adapted to a human environment. The chain of command is implemented using a hierarchical decision model. The groups consist of multiple model-based reflex agents, with individual blackboards for working memory, with a colony level blackboard to mimic the foraging patterns and include commands received from ranking agents. An agent architecture and environment are proposed that allows for creation of autonomous cooperative agents. The behaviour of agents is then evaluated both mathematically and empirically using an adaptation of anytime universal intelligence test and agent believability metric

Terrain Representation And Reasoning In Computer Generated Forces : A Survey Of Computer Generated Forces Systems And How They Represent And Reason About Terrain

Report on a survey of computer systems used to produce realistic or intelligent behavior by autonomous entities in simulation systems. In particular, it is concerned with the data structures used by computer generated forces systems to represent terrain and the algorithmic approaches used by those systems to reason about terrain

Smart Simulation for Decision Support at Headquarters

While serious games are being widely adopted by NATO and partner nations, their use is currently limited to training and operations planning. In this paper, we explore new methods that use simulations for decision support during the execution of military operations. During this phase, the commander makes decisions based on knowledge of the situation and the primary objectives. We propose here to take a simulation containing smart and autonomous units, and use it to create new kinds of decision support tools capable of improving situation awareness, and consequently the quality of decisions. The breakthrough behind this initiative is the realization that we can provide HQ decision makers with access to a version of the information that smart simulated units use to make decisions. To ensure the approach was sound we first studied decision-making processes, and analyzed how situation awareness improves decision making. After analysis of the decision-making processes at various headquarters, and the types of decision criteria employed, we are able to produce innovative information, computed by the simulation, and fed by the command and control system. We then propose a prerequisite architecture, and describe the first results of our proof of concept work based on the SWORD (Simulation Wargaming for Operational Research and Doctrine) simulation. Based on the current situation (intelligence, operational state, logistics, etc.) and the current maneuver (current task), examples of what we are now capable of are as follows:  provide an immediate local force ratio map, produce a capacities map (detection, combat), compute contextual fire or logistic support time required, automatically generate lines of battle such as the Forward Line of Own Troops (FLOT), Limit Of Advance (LOA), Line of Contact (LC), Forward Edge of Battle Area (FEBA), or propose an effect based maneuver map in order to understand the current effect of the forces on the ground. We then propose a prerequisite architecture for use as a decision-support system at HQ, and describe the next smart layers that we believe should be developed for optimal results

Evolving scout agents for military simulations

textSimulations play an increasingly significant role in training and preparing the military, particularly in environments with constrained budgets. Unfortunately, in most cases a small number of people must control a large number of simulated vehicles and soldiers. This often leads to micromanagement of computer-controlled forces in order to get them to exhibit the human-like characteristics of an enemy force. This thesis uses Neuroevolution of Augmenting Topologies (NEAT) to train neural networks to perform the role of scouts which analyze the terrain and decide where to place themselves to best observe the enemy forces. The main attribute that the scout agents consider is a vapor flow rate from the enemy starting location to their intended objective, which according to previous studies indicates likely chokepoints along the enemy route. This thesis experiments with different configurations of sensors and fitness functions in order to maximize how much of the enemy team is spotted over the course of the scenario. The results show that these agents perform better than randomly placed scouts and better than scouts deployed using heuristics in many situations, although not consistently so. Evolutionary optimization of scout agents using vapor flow is thus a promising approach for developing autonomous scout agents in military simulations.Computer Science

Command Agent Belief Architecture to Support Commander Decision Making in Military Simulation

In the war, military conflicts have many aspects that are consistent with complexity theory e.g., the higher commander’s decision is directed at animate entity that react under hierarchical and self-organised structure in decentralised command and control for the collectivist dynamism of decomposed elements due to nonlinear complexity of warfare on the battlefield. Agent technology have been found to be suitable for modelling tactical behaviour of entities at multiple level of resolution under hierarchical command and control (C2) structure and provide a powerful abstraction mechanism required for designing simulations of complex and dynamic battlefield situations. Intelligent agents can potentially reduce the overhead on such experiments and studies. Command agents, plan how to carry out the operation and assign tasks to subordinate agents. They receive information from battlefield environment and use such information to build situation awareness and also to respond to unforeseen situations. In the paper, we have proposed a mechanism for modelling tactical behaviour of an intelligent agent by which higher command level entities should be able to synthesize their beliefs derived from the lower level sub ordinates entities. This paper presents a role-based belief, desire and intention mechanism to facilitate in the representation of military hierarchy, modelling of tactical behaviour based on agent current belief, teammate’s belief propagation, and coordination issues. Higher commander can view the battlefield information at different levels of abstraction based on concept of aggregation and disaggregation and take appropriate reactive response to any unforeseen circumstances happening in battlefield

Research Naval Postgraduate School, v.12, no.3, October 2002

NPS Research is published by the Research and Sponsored Programs, Office of the Vice President and Dean of Research, in accordance with NAVSOP-35. Views and opinions expressed are not necessarily those of the Department of the Navy.Approved for public release; distribution is unlimited

ENLISTING AI IN COURSE OF ACTION ANALYSIS AS APPLIED TO NAVAL FREEDOM OF NAVIGATION OPERATIONS

Navy Planning Process (NPP) Course of Action (COA) analysis requires time and subject matter experts (SMEs) to function properly. Independent steamers (lone destroyers) can soon find themselves lacking time or more than 1–2 SMEs or both. Artificial Intelligence (AI) techniques implemented in real-time strategy (RTS) wargames can be applied to military wargaming to aid military decision-makers’ COA analysis. Using a deep-Q network (DQN) and the ATLATL wargaming framework, I was able to train AI agents that could operate as the opposing force (OPFOR) commander at both satisfactory and near-optimal levels of performance, after less than 24 hours of training or 500000–learning steps. I also show that under 6 hours or 150000–learning steps does not result in a satisfactory AI admiral capable of playing the role as the OPFOR commander in a similarly sized freedom of navigation operation (FONOP) scenario. Applying these AI techniques can save both time onboard and time for reachback personnel. Training AI admirals as quality OPFOR commanders can enhance the NPP for the entire Navy without adding additional strain and without creating analysis paralysis. The meaningful insights and localized flashpoints revealed through hundreds of thousands of constructive operations and experienced by the crew in live simulation or simulation replays will lead to real world, combat-ready naval forces capable of deterring aggression and maintaining freedom of the seas.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Agent-Based Modeling of Physical Activity Behavior and Environmental Correlations: An Introduction and Illustration

Purpose: To introduce Agent-Based Model (ABM) to physical activity (PA) research and, using data from a study of neighborhood walkability and walking behavior, to illustrate parameters for an ABM of walking behavior. Method: The concept, brief history, mechanism, major components, key steps, advantages, and limitations of ABM were first introduced. For illustration, 10 participants (age in years: mean = 68, SD = 8) were recruited from a walkable and a nonwalkable neighborhood. They wore AMP 331 triaxial accelerometers and GeoLogger GPA tracking devices for 21 days. Data were analyzed using conventional statistics and highresolution geographic image analysis, which focused on a) path length, b) path duration, c) number of GPS reporting points, and d) interaction between distances and time. Results: Average steps by subjects ranged from 1810-10,453 steps per day (mean = 6899, SD = 3823). No statistical difference in walking behavior was found between neighborhoods (Walkable = 6710 ± 2781, Nonwalkable = 7096 ± 4674). Three environment parameters (ie, sidewalk, crosswalk, and path) were identified for future ABM simulation. Conclusion: ABM should provide a better understanding of PA behavior\u27s interaction with the environment, as illustrated using a real-life example. PA field should take advantage of ABM in future research