Artificial Intelligence techniques

ARTIFICIAL INTELLIGENCE——人工智能1 Artificial intelligence (AI) is, in theory, the ability of an artificial mechanism to demonstrate some form of intelligent behavior equivalent to the behaviors observed in intelligent living organisms. Artificial intelligence is also the name of the field of science and technology in which artificial mechanisms that exhibit behavior resembling intelligence are developed and studied.2 The term AI itself, and the phenomena actually observed, invite --- indeed demand --- philosophical speculation about what in fact constitutes the mind or intelligence. These kinds of questions can be considered separately, however, from a description of the various endeavors to construct increasingly sophisticated mechanisms that exhibit “intelligence.”3 Research into all aspects of AI is vigorous. Some concern exists among workers in the field, however, that both the progress and expectations of AI have been overstated. AI programs are primitive when compared to the kinds of intuitive reasoning and induction of which the human brain or even the brains of much less advanced organisms are capable. AI has indeed shown great promise in the area of expert systems --- that is, knowledge-based expert programs --- but while these programs are powerful when answering questions within a specific domain, they are nevertheless incapable of any type of adaptable, or truly intelligent, reasoning.4 Examples of AI systems include computer programs that perform such tasks as medical diagnoses and mineral prospecting. Computers have also been programmed to display some degree of legal reasoning, speech understanding, vision interpretation, natural-language processing, problem solving, and learning. Although most of these systems have proved valuable either as research vehicles or in specific, practical applications, most of them are also still very far from being perfected.5 CHARACTERISTICS OF AI: No generally accepted theories have yet emerged within the field of AI, owing in part to the fact that AI is a very young science. It is assumed, however, that on the highest level an AI system must receive input from its environment, determine an action or response, and deliver an output to its environment. A mechanism for interpreting the input is needed. This need has led to research in speech understanding, vision, and natural language. The interpretation must be represented in some form that can be manipulated by the machine.6 In order to achieve this goal, techniques of knowledge representation are invoked. The AI interpretation of this, together with knowledge obtained previously, ismanipulated within the system under study by means of some mechanism or algorithm. The system thus arrives at an internal representation of the response or action. The development of such processes requires techniques of expert reasoning, common-sense reasoning, problem solving, planning, signal interpretation, and learning. Finally, the system must网construct an effective response. This requires techniques of natural-language generation.7 THE FIFTH-GENERATION ATTEMPT: In the 1980s, in an attempt to develop an expert system on a very large scale, the Japanese government began building powerful computers with hardware that made logical inferences in the computer language PROLOG. (Following the idea of representing knowledge declaratively, the logic programming PROLOG had been developed in England and France. PROLOG is actually an inference engine that searches declared facts and rules to confirm or deny a hypothesis. A drawback of PROLOG is that it cannot be altered by the programmer.) The Japanese referred to such machines as “fifth-generation” computers.8 By the early 1990s, however, Japan had forsaken this plan and even announced that they were ready to release its software. Although they did not detail reasons for their abandonment of the fifth-generation program, U.S scientists faulted their efforts at AI as being too much in the direction of computer-type logic and too little in the direction of human thinking processes. The choice of PROLOG was also criticized. Other nations were by then not developing software in that computer language and were showing little further enthusiasm for it. Furthermore, the Japanese were not making much progress in parallel processing, a kind of computer architecture involving many independent processors working together in parallel—a method increasingly important in the field of computer science. The Japanese have now defined a “sixth-generation” goal instead, called the Real World Computing Project, that veers away from the expert-systems approach that works only by built-in logical rules.9 THE FUTURE OF AI RESEARCH: One impediment to building even more useful expert systems has been, from the start, the problem of input---in particular, the feeding of raw data into an AI system. To this end, much effort has been devoted to speech recognition, character recognition, machine vision, and natural-language processing. A second problem is in obtaining knowledge. It has proved arduous toextract knowledge from an expert and then code it for use by the machine, so a great deal of effort is also being devoted to learning and knowledge acquisition.10 One of the most useful ideas that has emerged from AI research, however, is that facts and rules (declarative knowledge) can be represented separately from decision-making algorithms (procedural knowledge). This realization has had a profound effect both on the way that scientists approach problems and on the engineering techniques used to produce AI systems. By adopting a particular procedural element, called an inference engine, development of an AI system is reduced to obtaining and codifying sufficient rules and facts from the problem domain. This codification process is called knowledge engineering. Reducing system development to knowledge engineering has opened the door to non-AI practitioners. In addition, business and industry have been recruiting AI scientists to build expert systems.11 In particular, a large number of these problems in the AI field have been associated with robotics. There are, first of all, the mechanical problems of getting a machine to make very precise or delicate movements. Beyond that are the much more difficult problems of programming sequences of movements that will enable a robot to interact effectively with a natural environment, rather than some carefully designed laboratory setting. Much work in this area involves problem solving and planning.12 A radical approach to such problems has been to abandon the aim of developing “reasoning” AI systems and to produce, instead, robots that function “reflexively”. A leading figure in this field has been Rodney Brooks of the Massachusetts Institute of Technology. These AI researchers felt that preceding efforts in robotics were doomed to failure because the systems produced could not function in the real world. Rather than trying to construct integrated networks that operate under a centralizing control and maintain a logically consistent model of the world, they are pursuing a behavior-based approach named subsumption architecture.13 Subsumption architecture employs a design technique called “layering,”---a form of parallel processing in which each layer is a separate behavior-producing network that functions on its own, with no central control. No true separation exists, in these layers, between data and computation. Both of them are distributed over the same networks. Connections between sensors and actuators in these systems are kept short as well. The resulting robots might be called “mindless,” but in fact they have demonstrated remarkable abilities to learn and to adapt to real-life circumstances.14 The apparent successes of this new approach have not convinced many supporters of integrated-systems development that the alternative is a valid one for drawing nearer to the goal of producing true AI. The arguments that have arisen between practitioners of the two different methodologies are in fact profound ones. They have implications about the nature of intelligence in general, whether natural or artificial。

高三英语人工智能单选题40题1. Artificial intelligence is a branch of computer science that aims to create intelligent _____.A.machinesB.devicesC.equipmentsD.instruments答案解析：A。

“machines”通常指机器，在人工智能领域常指具有一定智能的机器；“devices”一般指设备、装置；“equipments”是“equipment”的复数形式，指装备、器材；“instruments”指仪器、工具。

2. In the field of artificial intelligence, algorithms are used to train _____.A.modelsB.patternsC.shapesD.forms答案解析：A。

“models”在人工智能中常指模型，可以通过算法进行训练；“patterns”指模式、图案；“shapes”指形状；“forms”指形式。

3. Deep learning is a powerful technique in artificial intelligence that uses neural _____.worksB.systemsC.structuresanizations答案解析：A。

“networks”指网络，在深度学习中常指神经网络；“systems”指系统；“structures”指结构；“organizations”指组织。

4. Artificial intelligence can process large amounts of data to make accurate _____.A.predictionsB.forecastsC.projectionsD.expectations答案解析：A。

人工智能技术的英语artificial intelligence technology常见释义：英[ˌɑːtɪˈfɪʃl ɪnˈtelɪdʒəns tekˈnɒlədʒi]美[ˌɑːrtɪˈfɪʃl ɪnˈtelɪdʒəns tekˈnɑːlədʒi]例句：虽然现在针对选择题和判断正误题的自动评分系统已经非常普遍，但利用人工智能技术对短文进行评分尚未得到教育工作者的广泛认可，而且批评声也很多。

Although automated grading systems for multiple-choice and true-false tests are now widespread, the use of artificial intelligence technology to grade essay answers has not yet received widespread acceptance by educators and has many critics.将CAD和人工智能技术引入组合夹具设计中，可以提高生产效率、减轻劳动强度、缩短生产准备周期和加快产品上市时间。

Introducing CAD and artificial intelligence technology into modular fixture design can improve production efficiency, lighten working intensity, reduce manufacturing lead-time and marketing time.听上去够酷的了，不过该公司已经开始研发包含摄像头和人工智能技术的升级版鞋子，不仅可以探测到障碍物，还可以检测出是何种障碍物。

That sounds impressive enough, but the company is already working on a much more advanced version that incorporates cameras and artificial intelligence to not only detectobstacles but also their nature.Java语言特点及其对人工智能技术的影响和促进Characteristics of Java Language and the Action of Influence and Promotion of it for AI Technology。

科技与创新┃Science and Technology&Innovation2021年第07期文章编号：2095－6835（2021）07－0172－02浅析人工智能技术在5G时代的发展与应用王君1，4，廖华杰1，宋泽生2，欧子龙1，梁薇薇3，廖君1，夏愚然1（1.中山大学南方学院，广东广州510970；2.南昌大学，江西南昌330027；3.重庆邮电大学，重庆400065；4.广州恒通智联科技有限公司，广东广州510630）摘要：随着社会科技的不断发展，人工智能已经从最初的起步发展期发展到了现在的飞速发展期，它最主要的集中发展时期可以分为应用发展期和飞速发展期。

随着5G时代的到来，医疗保障、教育培训、交通运输等诸多领域的服务水平难以满足人类对智能化的需求。

为了解决这些问题，满足日益增长的服务和效率需求，人工智能技术随之产生。

人工智能技术是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。

人工智能的现状发展主要在人脸识别、机器人、专家系统等方面，人工智能与5G网络的相互结合，加快了数字时代的发展，人工智能带给人们很多好处的同时，也带来了许多不足，比如核心技术成熟度不足，对于应用场景的依赖性较强，存在数据孤岛和数据碎片化。

关键词：人工智能；5G时代；技术革命；智能机器中图分类号：TN929.5文献标志码：A DOI：10.15913/ki.kjycx.2021.07.073随着现代高科技的发展，人工智能技术占据了主导地位，人工智能融合了智能机器以及机器智能的技术。

以前的人工智能已经不满足于现代技术的发展需求，因此人们提出了新的人工智能技术，被广泛应用在各大领域当中。

它是计算机与信息网络的真正结合，成为了新一轮的技术革命。

人工智能技术是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。

它是计算机与互联网的真正结合以及它在计算机的基础上实现了智能化，从而更加追求智能机器到高水平的人机、脑机相互协同和融合[1-4]。

智能科学与技术专业英语一、单词1. Artificial Intelligence (AI)- 英语释义：The theory and development ofputer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision - making, and translation between languages.- 用法：“Artificial Intelligence” is often abbreviated as “AI” and can be used as a subject or in phrases like “AI technology” or “the field of AI”.- 双语例句：- Artificial Intelligence has made great progress in recent years. (近年来，人工智能取得了巨大的进展。

)- Manypanies are investing heavily in artificial intelligence research. (许多公司正在大力投资人工智能研究。

)2. Algorithm- 英语释义：A set ofputational steps and rules for performing a specific task.- 用法：Can be used as a countable noun, e.g. “T his algorithm is very efficient.”- 双语例句：- The new algorithm can solve the problem much faster. (新算法可以更快地解决这个问题。

市场调研方法外文文献及翻译1. Market Research Methods: Incorporating Social Media into Traditional Approaches文章介绍了如何在市场调研中运用社交媒体，以帮助企业更好地了解消费者。

研究人员将社交媒体与传统的定量调研和定性调研相结合，以获得更全面的信息。

通过采集社交媒体的数据分析消费者的行为和偏好，以及对产品或服务的反馈意见。

2. Using Eye Tracking in Market Research: A Guide to Best Practices该文献介绍了视觉追踪技术在市场调研中的应用。

作者指出，视觉追踪技术可以帮助研究人员理解消费者在浏览产品或服务时的注意力分配和行为模式。

文章介绍了适用于市场调研的视觉追踪应用程序的最佳实践和测试方法。

3. Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice这篇文章介绍了一种被称为 "共轭分析" 的调研方法，该方法可以帮助研究人员了解消费者在购买某种产品或服务时的偏好和决策过程。

文献称，共轭分析已经成为市场营销领域最为普遍的工具之一。

文章还介绍了最新的研究和在实践中的应用，并探讨了一些特定情况下共轭分析的限制。

4. Qualitative Market Research: An International Journal这个杂志专注于定性市场调研方法。

它包括与确定消费者需求、分析竞争对手、建立品牌等相关的研究。

文章强调定性市场调研可以提供深入的见解和对产品或服务的更清晰的理解，帮助企业做出更明智的营销和业务决策。

每一期都包括来自该领域的专家的文章，并提供案例研究和最佳实践。

5. Use of Artificial Intelligence Techniques in Market Research: A Review该文献介绍了如何使用人工智能技术进行市场调研。

APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) PROGRAMMINGTECHNIQUES TO TACTICAL GUIDANCE FOR FIGHTER AIRCRAFTJohn W. McManusandKenneth H. GoodrichNASA Langley Research CenterMail Stop 489Hampton, Virginia 23665-5225(804)864-4037/(804)864-4009AIAA Guidance, Navigation, and Control ConferenceAugust 14-16, 1989Boston, MassachusettsAutonomous Systems and Mission PlanningABSTRACTA research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within-Visual-Range (WVR) air combat engagements is discussed. The application of AI methods for development and implementation of the TDG is presented. The history of the Adaptive Maneuvering Logic (AML) program is traced and current versions of the AML program are compared and contrasted with the TDG system. The Knowledge-Based Systems (KBS) used by the TDG to aid in the decision-making process are outlined in detail and example rules are presented. The results of tests to evaluate the performance of the TDG versus a version of AML and versus human pilots in the Langley Differential Maneuvering Simulator (DMS) are presented. To date, these results have shown significant performance gains in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify than the updated FORTRAN AML programs.INTRODUCTIONThe development of all-aspect and "fire and forget" weapons has increased the complexity of the air-to-air combat environment. Modern sensors provide critical tactical information to the aircraft a range and precision that were impossible 20 years ago. This increased complexity, combined with the expanded capabilities of high performance aircraft, has changed the future of air combat engagements. The need for a modern, realistic air combat simulation that can be used to evaluate the current and future air combat environment has been well documented [Burgin 1975, 1986, 1988; Hankins 1979]. Existing tools such as the Adaptive Maneuvering Logic program (AML) [Burgin 1975, 1986, 1988], TAC Brawler [Kerchner 1985], and AASPEM have generally centered their efforts on the development and refinement of high-fidelity aircraft dynamics modeling techniques and not on the developmentand refinement of tactical decision generation logic for WVR engagements. In support of the study of superagile aircraft at Langley Research Center (LaRC) a Tactical Guidance Research and Evaluation System (TGRES, pronounced "tigress") is being developed [Goodrich 1989].Figure 1. TGRES SYSTEM.TGRES DESCRIPTIONThe TGRES system, shown in figure 1, provides a means by which researchers can develop and evaluate, in a tactically significant environment, various systems for high performance aircraft. While TGRES is aimed specifically at the development and evaluation of maneuvering strategies and advanced guidance/control systems for superagile aircraft, TGRES's modularity will make it easily adaptable to the analysis of other types of aircraft systems. TGRES is composed of three main elements--the TDG, the Tactical Maneuver Simulator (TMS) , and the DMS.The TDG is a knowledge-based guidance system designed to provide insight into the tactical benefits and costs of enhanced aircraft controllability and maneuverability throughout an expanded flight envelope (i.e. superagility). The two remaining elements of TGRES, the TMS and the DMS, provide simulation environments in which the TDG is exercised. The TMS simulation environment was developed using conventional computer languages on a VAXStation 3200. The TDG was developed on a Symbolics 3650 workstation. The separation of the aircraft simulation and decision logic components allows each module to be developed using hardware and programming techniques specifically designed for its function. This separation of tasks also increases the efficiency of the simulation by allowing some parallel processing. The two processes are executed as co-tasks and communicate via an EtherNet connection. (See fig. 2.)Figure 2. CURRENT HARDWARE CONFIGURATION.The user interface system consists of a color graphics package designed to replayboth TMS and DMS engagements, and a mouse sensitive representation of the TDG aircraft and its basic systems that allows the user to interact with the TDG aircraft during the execution of TMS runs. The Engagement Replay System (ERS) software is available for a VAX color workstation and a Symbolics color workstation. The ERS display, shown in figure 3, displays the two aircraft on a three-dimensional axis and has dedicated windows used to display several aircraft variables including the thrust, Mach number, and deviation angles of the two aircraft. The viewing angle for each engagement can be rotated 360°around both the X and Z axis to provide the most information to the user. The interactive TMS display includes a graphical representation of the TDG aircraft's major systems suchas engines, offensive and defensive systems, and a system status display. During the simulation run the user can enable and/or disable the aircraft's systems using the mouse sensitive display and evaluate how the changes effect the TDG's decision generation process.Figure 3. ERS DISPLAY.The final element of TGRES is the Differential Maneuvering Simulator. The DMS consistsof two 40' diameter domes located at Langley. The facility is intended for the real-time simulation of engagements between piloted aircraft. By using the TDG to drive one of the airplanes, it is possible to test the TDG against a human opponent. This feature allows the guidance logic to be evaluated against an unpredictable and adaptive opponent. A thirddome (20' in diameter) is being added to the DMS facility. This addition will allow the guidance logic to be evaluated in one-versus-two or two-versus-one scenarios, further enhancing the tactical capability of the DMS environment.THE AML PROGRAMThe TDG is being developed as a KBS incorporating some of the features first outlined in the AML program [Burgin 1975, Hankins 1979]. The AML program was selected as a baseline for several reasons, including its past performance as a real-time WVR tactical adversary in the Langley DMS and the modular design of the FORTRAN source code. The tactical decision generation method developed for the original AML program, outlined in figure 4, is a unique approach that attempts to model the goal-seeking behavior of a pilot by mapping the physical situation between the two aircraft into a finite, abstract situation space. A set of the three basic control variables (bank angle, load factor, and thrust) can be determined to maximize some performance index in the situation space [Burgin 76]. Each triplet of controlvariables defines an "elemental maneuver," and a sequence of these elemental maneuvers may form classical or "text book" air combat maneuvers.Figure 4. HOW AML WORKS.Although the logic and geometry used by AML to make tactical decisions is complex,the basic concepts it uses are simple. At each decision interval, the "attacking" aircraft predicts the future position and velocity of its opponent using a curve-fitting algorithm and past known positions of the opponent. The attacker then uses a set of elemental maneuvers (described above) to predict a set of positions that it can reach from its current state.The AML program forms a "situation state vector" for each trial maneuver evaluated.The vector is used to represent the responses to a set of questions about the current situation.Figure 5 shows the binary scoring method (0 = NO, 1 = YES) used to determine the value of each each cell in the vector.This vector is multiplied by a "scoring weight" vector to form a scalar product that represents the situation space value for the current maneuver. A detailed description of the trial maneuver generation and scoring process and an explanation of how the scoring weights have evolved can be found in [Burgin 1988]. The questions used to form the situation state vector were obtained from several sources including air combat maneuvering manuals, interviews with fighter pilots, and detailed analysis of the original DMS engagements. In the original version of AML, each question had a positive, non-zero weight. The questions were formulated so that a "YES" answer reflects a favorable condition, increasing the score for the maneuver. It is important to note that in the original AML research "no systematic investigation was made to optimize these weight factors; they were usually all set to one." The early AML versions [Burgin 1975; Hankins 1979] were designed to perform as a conservative opponent. The scoring rules rated offense and defense evenly and risked giving up some positional advantage to the opponent only when there was reasonable assurance the attacker would gain at least as much offsetting advantage. This conservative approach may be the product of a philosophy stated in [Burgin 1988],"The objective of the decision-making process is to derive maneuvers which will bring one's own weapons to bear on the target while at the same time minimizing exposure to the other side's weapons."This is a one-dimensional approach to the problem. It outlines a logic that handles only the neutral and aggressive cases effectively and does not recognize that there are several Modes of Operation (MO), outlined in figure 6, that a pilot may use during an engagement. In many situations when the opponent has a distinct positional advantage, the AML aircraft will perform "kamikaze" maneuvers, giving up one or more clear shots to the opponent while it maneuvers to a position of "advantage." In these situations the AML aircraft would not survive to exploit the positional advantage, having been "killed" while obtaining it.•AGGRESSIVE•DEFENSIVE•NEUTRAL•EVASIVE•EVADING OPPONENTS' "LOCK AND FIRE"•EVADING MISSILE (AAM & SAM)•GROUND / STALL EVASIONFigure 6. TDG MODES OF OPERATION.The existing trial maneuver versions of AML do a good job of getting behind an opponent, but due to the grain of the trial maneuvers, lack the ability to fine-track the opponent. Several changes were made to the AML program [Burgin 1988] to address this problem. The requirement that only the opponents positional data be passed to the algorithm was relaxed and "complete and accurate information about the the opponent's past and present states" is now provided. The 1986 version of the AML program, AML86 [Burgin 1988], also made several major changes to the tactical decision generation process, abandoning the trial maneuver concept for a rule-based approach and a set of canned "Basic Fighter Maneuvers." [Burgin 1988] contains an extensive history of the "trial maneuver" concept and a description of how the new rule-based version of the program, AML68, was developed. A "pointing" control system was also developed to aid the fine-tracking process. The pointing control system directly commands roll and pitch rates to point the aircraft's longitudinal axis at the opponent. AML86 is a first step towards a multi-dimensional approach and is similar to the decision logic incorporated in the TDG.THE DEVELOPMENT OF THE TDG SYSTEMThe development of the TDG has been a multi-stage process using the COSMIC version of AML as a starting point. The COSMIC version of AML was updated by Dynamics Engineering Incorporated (DEI) while under contract to NASA Langley. This version of AML, (DEI-AML), has a scoring module that uses a set of 15 binary questions and a fixed set of weights to evaluate the trial maneuvers.DEI installed aerodynamic data and engine characteristics provided by the Aircraft Guidance and Controls Branch (AGCB) into the AML data tables and made all changes to the AML software outlined in [Burgin, 1986]. DEI-AML was tested by AGCB to insure symmetry of the engagements given symmetric initial conditions. During the testing process several software bugs were found and corrected. A full description of the bugs and corrections are outlined in [McManus 1989]. The resulting code, dubbed AML´, was again tested for symmetry and a DMS ready version of the code, DMS-AML´ was prepared. AML´ and a DMS ready version, DMS-AML´, are being used as the baseline during development of the TDG system.Figure 7. HOW TDG WORKS.The TDG system, outlined in figure 7, currently uses the trial maneuver concept outlined in the AML program with several extensions. The original set of five to nine trial maneuvers has been expanded to include over 40 trial maneuvers. Although this is a "brute force" solution the new trial maneuvers allow the TDG to perform target tracking more effectively and improve the system's overall performance. The TDG uses an object-oriented programming approach to represent each aircraft and the current state of its offensive systems, defensive systems, and engines. This information is used to help guide the TDG's reasoning process. The original FORTRAN AML throttle controller and the maneuver scoring modules have been redesigned using a rule-based programming approach and ported to the AI workstation. Examples of rules for each of the KBS modules are shown in figure 8.((AND (EQUAL (GET-MISSION PALADIN) *AGGRESSIVE*)(EQUAL (GET-POSITION PALADIN) *NEUTRAL*))((SETF (GET-MODE PALADIN) *AGGRESSIVE*)(AGGRESSIVE-WEIGHT)))EXAMPLE MODE SELECTION RULE.((AND I-SEE-HIM I-CAN-FIRE HE-CANT-FIRE (<= RANGE 12500))((SETF THROTTLE 0.94)) )EXAMPLE THROTTLE CONTROL RULE.((OR (≤ (ABS HIM-UNABLE-TO-FIRE) (I-CAN-FIRE))(AND (> HIM-UNABLE-TO-FIRE O.O) (≥ I-CAN-FIRE 0.0) )(= GUNA 1.0)(= ALLA 1.0)(= HEATA 1.0))(SETF (GET-POSITION PALADIN) *AGGRESSIVE*))EXAMPLE SITUATION ASSESSMENT RULE.Figure 8. Example Rules.KBS MODULES OF THE TDGThe TDG system has a knowledge-based Situation Assessment (SA) module that is executed at each decision interval before the trial maneuvers are evaluated. The SA module is used to determine the TDG's current MO. The SA is executed at each interval, before the maneuver scoring module, and determines the TDG's MO. This determination is based on the TDG's current mission, the current state of the aircraft's systems, the relative geometry between the aircraft and its opponent, and the opponent's instantaneous-intent (*in-int*). Each of the modes shown in figure 6 has a unique set of scoring weights and a decision interval associated with it. The weights for each mode have been adjusted during the design and testing process to maximize the TDG's performance in that mode. Test results have shown that a short decision interval, (0.5 sec.), improves the TDG's fine-tracking performance. The same short decision interval results in a "thrashing" motion in neutral situations resulting in degraded system performance. A longer decision interval, (1.0 sec.), is used in neutral situations. The opponent's *in-int* is defined to be an estimation of the opponent's intent at the current point in time based on available sensor, positional, and geometric data. Currently, there is no attempt to use a history of *in-int* to derive a long-term opponent intent. The flexibility provided by the use of MO's allows the system to more closely model the pilots changing strategies during the engagement. The COSMIC version of AML, and most AML variations before AML86, do not have the ability to change their decision generation strategy based on the changing environment. The TDG Scoring Module (SM) is a KBS that uses a set of 17 fuzzy logicquestions with responses ranging from [0 = NO, ..., 1.0 = YES], (fig. 8), and the set of mode-specific scoring weights selected by the SA module to score each of the trial maneuvers.A rule-based active Throttle Controller (TC) has been developed to replace the existing throttle control subroutine. The TC is called at the start of each decision interval and can set the throttle at any position from idle to full afterburner [0, .., 2]. The logic for the existing AML throttle control subroutine had only three positions (0 = idle, 1 = military, 2 = full afterburner) and had been turned off in the COSMIC version--all engagements were being flown with the throttle set at full afterburner.T D GFigure 10. SET OF 64 INITIAL CONDITIONS.A statistics module is used to calculate the amount of time that each aircraft has its weapons locked on its opponent and the deviation angle and angle-off. The Line-Of-Sight (LOS) vector is defined as the vector between ownship c.g. and opponent's c.g. The Line-Of-Sight (LOS) angle is defined as the angle between the LOS vector and ownship body x-axis; the deviation angle is defined as the angle between the LOS vector and ownship velocity vector; and the angle-off is defined as the angle between the LOS vector and opponent's velocity vector (fig.10).AMLAIRPLANETDGAIRPLANEOWNSHIP VELOCITYVECTORFigure 11. ANGLE DEFINITIONS.The weapons cones used represent a generic all-aspect missile, a generic tail-aspect missile,and a 20 mm cannon (fig. 11). Four metrics are currently used to evaluate each engagement.The first metric is calculated every second and computes the total time that each airplane has its weapons locked on the opponent, the probability that the shot will hit, the distance between the opponents, the angle-off, and the deviation angle. The results are printed in a table format at the completion of each run. The second metric computes a Probability of Survival (PS) using the data computed by the first metric. The missiles are treated as a limited resource and aprobability to hit of 0.65 is required to launch the first missile. The firing threshold increases by 0.05 for each missile launched, and all missiles are required to complete their flight to the target before the next missile is fired. The third scoring metric attempts to determine a Lethal Time (LT) value for each engagement. The LT value for a run is equal to ((TDG gun time -AML gun time) / 2) + 2 * (TDG tail-aspect time - AML tail-aspect time) + (TDG all-aspect time - AML all-aspect time). A positive LT value shows TDG with an advantage, a negative LTshows AML with an advantage. The fourth metric is Time on Offense (TOF). TOF is the sum of all weapons lock time for each each airplane. ∆TOF is computed as TDG TOF minus AML TOF.5° GUN CONE. RANGE 0 TO 5000 FT.-1-051122TDG- AML (time on offense)∆ TOF(seconds)Run Number.Figure 13. ∆TOF FOR SET OF 64 ENGAGEMENTS.TEST RESULTSA set of nine engagements presented in [Eidetics 89] were used to compare theperformance of the TDG system with the performance of the AML´ in the lab, and againstpilots in the DMS. AML´ was used as the A airplane in both sets of lab test engagements, and the human pilot flew the A airplane during the DMS runs. Airplanes with identicalperformance characteristics were used in both the DMS and the lab. The set of nine initial initial conditions, fig.13, favor the Aairplane.2 NM SEPARATION.540 KTS AIRSPEED.15,000 ALTITUDEFigure 14. EIDETICS INITIAL CONDITIONS.The B airplane has five neutral starting positions, runs 3, 5, 6, 7, and 9; one offensive starting position, run 8; and 3 defensive starting positions, runs 1, 2, and 4. There is a 2-nautical mile separation between the opponents and each airplane is at an initial altitude of 15,000 feet and an initial airspeed of 540 knots. All of the engagements were run for 60 seconds. The scoring metric used was an Overall Exchange Ratio (OER), defined as the # of A killed / the # of B killed. The Eidetics study was conducted using a modified version of the AASPEM program and produced an OER of ≈ 0.72. The OER was less than 1.0 due to the use of a non-symmetric set of initial conditions. In the first set of engagements the AML´ program wasflown against itself and the produced an OER of 0.75.2468101214Run Number.Time inSeconds.Figure 15. AML´ vs AML´ TOF.In the second set of engagements the TDG was used to control the B airplane and achieved an OER of 1.50, a 100 percent improvement. The test results, (figs. 14, 15), clearly show the superior performance of the TDG system. It is also interesting to note that the maximum OER Eidetics achieved by modifying aircraft performance characteristics was ≈ 0.85 [Eidetics1989].246810121416123456789Run Number.Time inSeconds.Figure 16. TDG vs AML´ TOF.The DMS runs were conducted using the research pilot with the most DMS flight time against the TDG-DMS as the opponent. The pilot flew against the set of initial conditions three times, providing a total of 27 runs. TOF data for the DMS runs is not available at this time. The OER for the set of 27 runs was 0.83. As stated earlier, studies done in the lab have shown that the reduced set of trial maneuvers used by DMS-TDG cannot fine track an opponent as effectively as the expanded set used by the TDG. The reduced set of trial maneuvers used by DMS-TDG may account for most of the performance difference between the TDG and DMS-TDG.FUTURE WORKSeveral enhancements to the existing TDG system are planned. The maneuver selection logic will be expanded to replace the use of the trial maneuvers for modes of operation where conventional guidance algorithms provide better performance. This change to the logic and selection module will improve the TDG's ability to track its opponent. Initial lab results have shown that the development of mode-specific maneuver sets will increase system efficiency by reducing the number of maneuvers evaluated for some MO's. The development of logic for two-vs-one engagements is underway. The third aircraft will be dynamically allocated to either the TDG or the opponent at the start of each run. This feature will allow researchers to evaluate the TDG in both two-vs-one and one-vs-two engagements. A system for connecting the Symbolics workstation directly to the DMS real-time computing facilities is also being investigated. The development of such a link would allow the full TDG system to be tested in the DMS against human pilots.The TGRES system presents an excellent opportunity to evaluate the use of AI programming techniques and knowledge-based systems in a real-time environment. It also clearly shows that the maneuver selection and scoring techniques developed in the late 1960's and early 1970's cannot perform well in the modern tactical environment and are not well suited for evaluating agile aircraft. Figure 16 shows many of the changes in the tactical and simulation environments since the original AML tactical decision generation logic was developed. The use of KBS and AI programming techniques in developing the TDG has allowed a complex tactical decision generation system to be developed that addresses the modern combat environment and agile aircraft in a clear and concise manner.1968HEAT SEEKING WEAPONS DOMINATE TACTICAL SITUATIONLIMITED COMPUTING AND MODELING RESOURCES. SHORT-RANGE RADAR. SHORT-RANGE WEAPONS.1 vs 11989ALL-ASPECT WEAPONS DOMINATE TACTICAL SITUATION. (LONGER RANGE, FIRE AND FORGET,....)BETTER COMPUTING AND MODELING RESOURCES.LONG-RANGE RADARLONG-RANGE WEAPONS.2 vs 1, M vs N SUPERMANEUVERABLE AIRCRAFT, POINT AND SHOOT CAPABILITYFigure 17. 1968 AML vs 1989 TDG.CONCLUDING REMARKSA KBS TDG is being developed to study WVR air combat engagements. The system incorporates modern airplane simulation techniques, sensors, and weapons systems. The system was developed using several concepts first outlined in the AML program originally developed for use in the LaRC DMS. An updated AML system is being used as a baseline to assess the functional and performance tradeoffs between a conventionally coded system and theAI-based system. Test results have shown that the AI-based TDG system has performed better than AML´ in both the TMS and the DMS. The use of a KBS SA module and MO's allows the TDG to more accurately represent the complex decision making process carried out by a pilot. The use of a more extensive set of trial maneuvers and a KBS TC module allows the TDG tofine track the opponent more effectively than AML´. The KBS decision generation logic has proved to be much easier to modify than the AML´ FORTRAN source code. The ability to integrate the TDG into the DMS offers a unique opportunity to evaluate the performance of theAI-based TDG software in a real-time tactical environment against human pilots.REFERENCES1. Burgin, G. H. ; et al. : An Adaptive Maneuvering Logic Computer Program for theSimulation of One-on-One Air-to-Air Combat. Vol I and II. NASA CR-2582, CR-2583, 1975.2. Burgin, G. H. : Improvements to the Adaptive Maneuvering Logic Program. NASA CR-3985, 1986.3. Burgin, G. H. ; and Sidor, L. B. : Rule-Based Air Combat Simulation. NASA CR-4160,1988.4. Hankins III, W. W. : Computer-Automated Opponent for Manned Air-to-Air CombatSimulations. NASA TP-1518, 1979.5. Kerchner, R. M. ; et al. : The TAC Brawler Air Combat Simulation Analyst Manual(Revision 3. 0). DSA Report #668.6. Buttrill, C. S. ; et al. : Draft NASA TM 1989.7.Taylor, Robert T. ; et al. : Simulated Combat for Advanced Technology AssessmentsUtilizing The Adaptive Maneuvering Logic Concepts. NASA Order no. L-24468C, Coastal Dynamics Technical Report No. 87-001.8.McManus, John W. ; Goodrich, Kenneth H. : Draft NASA TM 1989.9.Goodrich, Kenneth H; McManus John W. :AIAA Paper #...。