Effectiveness of autonomous decision making for unmanned combat aerial vehicles in dogfight engagements

The main objective of this work is to perform a study of the utility of unmanned combat aerial vehicles (UCAVs) in dogfighting (DF) engagements, with DF defined as an aerial battle between two fighter aircraft taking place at close range. The key problem is to assess effectiveness of UCAVs in DF combat when using autonomous decision making based on a representative guidance law and a game-theoretic algorithm. The UCAV DF problem is considered here as a two-player (two fighters), zero-sum, sequential-interaction game with limited information (i.e., each fighter only knows the last three positions of its opponent every time a decision needs to be made). A software simulator has been developed to represent a one-versus-one, clear-sky, close-range aerial battle involving three-dimensional trajectories with high-angle-of-attack (AOA) maneuvers for fighters with similar/dissimilar performance capabilities, considered under four initial conditions: offensive, defensive, neutral, and opposing engagements. Different “levels of intelligence” of the enemy are implemented to validate the performance of the UCAV autonomous decision making against diverse opponents. The simulation-based parametric study elucidates the influence of fighters’ performance capabilities and the fighters’ skill on the outcome of the engagement

Adaptive computer‐generated forces for simulator‐based training, Expert Systems with Applications

Simulator-based training is in constant pursuit of increasing level of realism. The transition from doctrine-driven computer-generated forces (CGF) to adaptive CGF represents one such effort. The use of doctrine-driven CGF is fraught with challenges such as modeling of complex expert knowledge and adapting to the trainees’ progress in real time. Therefore, this paper reports on how the use of adaptive CGF can overcome these challenges. Using a self-organizing neural network to implement the adaptive CGF, air combat maneuvering strategies are learned incrementally and generalized in real time. The state space and action space are extracted from the same hierarchical doctrine used by the rule-based CGF. In addition, this hierarchical doctrine is used to bootstrap the self-organizing neural network to improve learning efficiency and reduce model complexity. Two case studies are conducted. The first case study shows how adaptive CGF can converge to the effective air combat maneuvers against rule-based CGF. The subsequent case study replaces the rule-based CGF with human pilots as the opponent to the adaptive CGF. The results from these two case studies show how positive outcome from learning against rule-based CGF can differ markedly from learning against human subjects for the same tasks. With a better understanding of the existing constraints, an adaptive CGF that performs well against rule-based CGF and human subjects can be designed

Optimal pilot decisions and flight trajectories in air combat

The thesis concerns the analysis and synthesis of pilot decision-making and the design of optimal flight trajectories. In the synthesis framework, the methodology of influence diagrams is applied for modeling and simulating the maneuvering decision process of the pilot in one-on-one air combat. The influence diagram representations describing the maneuvering decision in a one sided optimization setting and in a game setting are constructed. The synthesis of team decision-making in a multiplayer air combat is tackled by formulating a decision theoretical information prioritization approach based on a value function and interval analysis. It gives the team optimal sequence of tactical data that is transmitted between cooperating air units for improving the situation awareness of the friendly pilots in the best possible way. In the optimal trajectory planning framework, an approach towards the interactive automated solution of deterministic aircraft trajectory optimization problems is presented. It offers design principles for a trajectory optimization software that can be operated automatically by a nonexpert user. In addition, the representation of preferences and uncertainties in trajectory optimization is considered by developing a multistage influence diagram that describes a series of the maneuvering decisions in a one-on-one air combat setting. This influence diagram representation as well as the synthesis elaborations provide seminal ways to treat uncertainties in air combat modeling. The work on influence diagrams can also be seen as the extension of the methodology to dynamically evolving decision situations involving possibly multiple actors with conflicting objectives. From the practical point of view, all the synthesis models can be utilized in decision-making systems of air combat simulators. The information prioritization approach can also be implemented in an onboard data link system.reviewe

Coordinating Team Tactics for Swarm-vs.-Swarm Adversarial Games

While swarms of UAVs have received much attention in the last few years, adversarial swarms (i.e., competitive, swarm-vs.-swarm games) have been less well studied. In this dissertation, I investigate the factors influential in team-vs.-team UAV aerial combat scenarios, elucidating the impacts of force concentration and opponent spread in the engagement space. Specifically, this dissertation makes the following contributions: (1) Tactical Analysis: Identifies conditions under which either explicitly-coordinating tactics or decentralized, greedy tactics are superior in engagements as small as 2-vs.-2 and as large as 10-vs.-10, and examines how these patterns change with the quality of the teams' weapons; (2) Coordinating Tactics: Introduces and demonstrates a deep-reinforcement-learning framework that equips agents to learn to use their own and their teammates' situational context to decide which pre-scripted tactics to employ in what situations, and which teammates, if any, to coordinate with throughout the engagement; the efficacy of agents using the neural network trained within this framework outperform baseline tactics in engagements against teams of agents employing baseline tactics in N-vs.-N engagements for N as small as two and as large as 64; and (3) Bio-Inspired Coordination: Discovers through Monte-Carlo agent-based simulations the importance of prioritizing the team's force concentration against the most threatening opponent agents, but also of preserving some resources by deploying a smaller defense force and defending against lower-penalty threats in addition to high-priority threats to maximize the remaining fuel within the defending team's fuel reservoir.Ph.D

Decision-Making for Air Combat Dogfight

基于敌对的两机空战背景,利用不断更新的威胁态势作为飞行员的决策依据,采用影响图分析法描述空战飞行员的决策过程,建立了空战飞行员的决策模型.其特点是空战威胁态势的评估及决策方式易于根据飞行员的经验和偏好进行描述与调整.仿真结果表明,利用所建立的空战决策模型可以有效地导引战机格斗,决策过程符合空战格斗理念.Considering a scenario of air combat involving two opposing fighter aircrafts,a decision-making model for the pilot in air combat is established.The model is based on a situation of continually updated threat.An influence diagram analysis method is employed to describe the pilot decision-making process.Evaluation of air combat threat and the decision-making behavior are easy to describe and modify,if necessary,according to the experience and preference of the individual pilot.Simulation results show that the proposed model is effective to guide the fighter aircraft during air combat dogfight,and its decision-making process conforms to the concept of air combat dogfight.航空科学基金资助项目(02F15001

Following the Principles: Case Studies in Operations Other than War, 1945-1999

In the post-World War II-era, operations other than war (OOTW) were the types of conflict most commonly faced by the United States. This term for what had previously been called by such names as small wars and low intensity conflict was incorporated in the Army’s capstone manual, Field Manual (FM) 100-5, Operations, in 1993. Field Manual 100-5 also listed objective, unity of effort, legitimacy, perseverance, restraint, and security as the six principles of OOTW. An analysis of eight OOTWs that occurred between 1945 and 1999 indicates that the balanced application of these principles is a reliable predicator of the operation’s outcome and that there is a relationship among several of the principles themselves. These findings suggest the principles of OOTW are a useful planning tool for military commanders and staffs

Design and analysis techniques for concurrent blackboard systems

Blackboard systems are a natural progression of Artificial Intelligence based systems into a more powerful problem solving technique. They provide a way for several highly specialized knowledge sources to cooperate to solve large, complex problems. Blackboard systems incorporate the concepts developed by rule-based and expert systems programmers and include the ability to add conventionally coded knowledge sources. The small and specialized knowledge sources are easier to develop and test, and are hosted on hardware specifically suited to the task that they are solving.;Designing and developing blackboard systems is a difficult process. The designer is attempting to balance several conflicting goals and achieve a high degree of concurrent knowledge source execution while maintaining both knowledge and semantic consistency on the blackboard. Blackboard systems have not attained their apparent potential because no established tools or methods exist to guide in their construction or analyze their performance.;The Formal Model for Blackboard Systems was developed to provide a formal method for describing a blackboard system. The formal model outlines the basic components of a blackboard system, and how the components interact. A set of blackboard system design tools has been developed and validated for implementing systems that are expressed using the formal model. The tools are used to test and refine a proposed blackboard system design before the design is implemented. The set of blackboard system design tools consists of a Knowledge Source Organizer, a Knowledge Source Input/Output Connectivity Analyzer, and a validated Blackboard System Simulation Model. My preliminary research has shown that the level of independence and specialization of the knowledge sources directly affects the performance of blackboard systems. Using the design, simulation, and analysis tools I developed a concurrent object-oriented blackboard system that is faster, more efficient, and more powerful than existing systems. The use of the design and analysis tools provided the highly specialized and highly independent knowledge sources required for my concurrent blackboard system to achieve its design goals

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