• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

    2021-04-14 06:55:18YingxuWangSeniorMemberIEEEMingHouSeniorMemberIEEEKonstantinosPlataniotisFellowIEEESamKwongFellowIEEEHenryLeungFellowIEEEEdwardTunstelFellowIEEEImreRudasFellowIEEEandLjiljanaTrajkovicFellowIEEE
    IEEE/CAA Journal of Automatica Sinica 2021年1期

    Yingxu Wang, Senior Member, IEEE, Ming Hou, Senior Member, IEEE, Konstantinos N. Plataniotis, Fellow, IEEE,Sam Kwong, Fellow, IEEE, Henry Leung, Fellow, IEEE, Edward Tunstel, Fellow, IEEE,Imre J. Rudas, Fellow, IEEE, and Ljiljana Trajkovic, Fellow, IEEE

    Abstract—Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence, cognition, computer, and systems sciences. This paper explores the intelligent and mathematical foundations of autonomous systems. It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A hierarchical intelligence model(HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering. Emerging paradigms of autonomous systems including brain-inspired systems, cognitive robots, and autonomous knowledge learning systems are described. Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

    I. INTRODUCTION

    AUTONOMOUS systems (AS) are an advanced intelligent system for implementing complex cognitive abilities in machines aggregating from reflexive, imperative, and adaptive intelligence to autonomous and cognitive intelligence [1]-[7].The ultimate aim of AS is to implement a brain-inspired system that may think and work with a human counterpart in a hybrid system without imperative instructions. Various AS have been demanded to address the pertinacious challenges to AI due to lack of cognitive, intelligent, computational,systematic, and mathematical readiness for a wide range of real-time and mission-critical applications [1]-[3], [6].

    AS research in early AI, robotics, and control theories has focused on human-system interactions in an intelligent system where humans are in-the-loop cooperating with intelligent machines [2]. Defence Research and Development Canada refers to an AS to be a system that “exhibits goal-oriented and potentially unpredictable and non-fully deterministic behaviors [7].” In the basic research of intelligence and systems sciences, the field of AS investigates intelligent systems for realizing advanced human intelligence by computational systems and neural networks [1], [3], [6],[8]-[26], which embodies high-level machine intelligence beyond those of imperative and adaptive systems with deterministic or prescriptive behaviors. A study of the mechanisms of autonomous organization in social networks[23] identified that social organization is a field of AS where collective intelligence plays a centric role in social computing.

    In the past 60 years of AI and systems engineering, few full AS were developed, because the theoretical foundations for autonomous intelligence and systems were not sufficiently mature [1], [10], [18], [27]-[29]. It is found that the classic mathematical means of logic, numerical methods, probability,and analytics was inadequate to deal with the entities in the brain (see Fig. 1), because they have been out of the domain of real numbers (R) [27], [30]. Many AI systems are still bounded by the intelligence bottleneck of adaptive mechanisms where machine intelligence is constrained by the lower-level reflexive, imperative, and deterministic intelligent abilities [24], [25], [28]. Therefore, it is a fundamental challenge to explore a roadmap to extend the intelligence power of current AI systems towards a highly autonomous level of natural intelligence.

    Fig. 1. Fundamental cognitive entities represented in the brain.

    This work explores the nature and a theoretical framework of AS. A survey of the architecture of intelligence science underpinning AS is introduced in Section II in order to explain the expected type of intelligent power for AS. Then, a theoretical framework of AS is developed in Section III for elaborating the generation of system autonomy and the role of human intelligence in hybrid AS. Section IV derives a set of principles and properties of AS. The relationship between AS and humans in hybrid systems is explained. Paradigms of AS demonstrated in Section V include brain-inspired systems,cognitive robots, and autonomous knowledge learning systems towards smart engineering applications and the emerging hybrid human-machine systems and societies. An index of mathematical symbols and notations used in this paper is provided in the Appendix.

    II. RELATED WORK AND KEY CONCEPTS

    Intelligence is the paramount cognitive ability of humans that may be mimicked by AS implemented by computational intelligence. Intelligence science studies the general form of intelligence, formal principles and properties, as well as engineering applications [8], [10], [20], [21], [24], [25], [28].Typical emerging AS are unsupervised computational intelligence, cognitive systems, brain-inspired systems, general AI,cognitive robots, unmanned systems, human intelligence augmentation systems, and intelligent IoTs [1]-[6], [11], [12],[16], [22]-[36]. Several AS projects undertaken in our labs address abstract intelligence [24], intelligent mathematics for AS [30], a tripartite framework of trustworthy AS [3],autonomous decision making [31], a transdisciplinary theory for cognitive cybernetics, humanity, and systems science [4],cognitive foundations of knowledge science [33], and the abstract system theory for AS [5].

    The exploration of the cognitive and intelligent foundations of AS has shown a wide range of theoretical preparations and engineering paradigms. We introduce a formal description of fundamental AS concepts and notations in order to provide a coherent platform for elaborating the essences of AS and the differences between AS and nonautonomous systems.

    where (2.a) denotes the narrow sense of intelligence, while both (2.a) parallel (||) with (2.b) represent the broad sense of intelligence.

    Definition 3: Intelligence science is a contemporary discipline that studies the mechanisms and properties of intelligence, the theories of intelligence across the neural,cognitive, functional, and mathematical levels, and other cognitive applications.

    The advances of AS are driven by the theoretical and technological development across intelligence science, brain science, cognitive science, robotics, and computational intelligence. The natural and machine intelligence underpinning AS may be inductively generated through the four levels of fundamental cognitive entities: data, information, knowledge,and intelligence as illustrated in Fig. 1. Fig. 1 indicates that intelligence may not be directly aggregated from data as some neural network technologies inferred, because there are multiple inductive layers from data to intelligence. Therefore,a matured AS would be expected to be able to independently discover a law in sciences known as the inductive intelligence or autonomously comprehend the semantics of a joke in natural language known as the inference intelligence. None of them is trivial towards extending the intelligence power of AS beyond traditional data aggregation abilities.

    The cognitive foundations of intelligence, as shown in Fig. 1,explain how intelligence is generated in the brain from lowlevel cognitive entities [8], [10], [24], [25], [28]. The relationship among the cognitive entities in the brain may be formally described by a hierarchical model in general system theories.

    A classification of intelligent systems may be derived based on the forms of inputs and outputs dealt by the system as shown in Table I. The reflexive and imperative systems are able to process deterministic stimuli by deterministic or indeterministic algorithms, respectively. The adaptive systems are designed to deal with indeterministic stimuli by deterministic behaviors predefined at the design time.However, AS are characterized by both indeterministic stimuli and indeterministic (problem-specific or goal-oriented) behaviors pending for run-time contexts.

    Basis on Definitions 1-4 and Table I, the intention and extension of AS may be rigorously expressed as follows.

    TABLE I CLASSIFICATION OF AUTONOMOUS AND NONAUTONOMOUS SYSTEMS

    Definition 5: Autonomous systems (AS) are a category of advanced intelligent systems that function without intervention of humans for implementing complex cognitive abilities aggregating from reflexive, imperative, and adaptive intelligence to autonomous and cognitive intelligence.

    A hierarchical intelligence model (HIM) shown in Fig. 2 is created for revealing the levels of intelligence and their difficulty for implementation in computational intelligence based on the abstract intelligence (αI) theory [24]. In the HIM, the levels of intelligence are aggregated from reflexive,imperative, adaptive, autonomous, and cognitive intelligence with 16 categories of intelligent behaviors. Types of system intelligence across the HIM layers may be formally described in Definition 6 using the pattern of stimulus/event-driven behaviors as summarized in the Appendix.

    Fig. 2. The HIM of AS.

    where T is the type suffix of different forms of intelligence as listed in the Appendix.

    Fig. 3. The mathematical framework of hierarchical AS intelligence.

    III. THE FRAMEWORK OF INTELLIGENCE SCIENCE UNDERPINNING AUTONOMOUS SYSTEMS

    Based of the conceptual models and literature survey in the preceding sections, the theoretical foundations of AS may be elaborated based on a rigorous framework of intelligence science [24]. The conceptual HIM formalized in this section is summarized in Fig. 3 as a theoretical framework of general AS. The AS framework provides an explanation of how the advanced intelligence for AS is generated from the bottom up based on the transformability within the five-level hierarchy of system intelligence. It may be rigorously implemented as a part of a real-time intelligent operating system.

    A. Reflexive Intelligence

    where the type suffixes |REF and |PM of a reflexive event and a process model are defined in the Appendix.

    B. Imperative Intelligence

    C. Adaptive Intelligence

    D. Autonomous Intelligence

    E. Cognitive Intelligence

    The mathematical models developed in Section III-E reveal

    IV. PROPERTIES AND THEORY OF AUTONOMOUS SYSTEMS

    On the basis of the HIM of intelligence science as elaborated in Section III, AS may be derived as a computational implementation of autonomous intelligence aggregated across the five layers from the bottom up.

    A. Properties of System Autonomy and Autonomous Systems

    According to Definition 5 and the HIM, autonomy is a property of intelligent systems that “can change their behavior in response to unanticipated events during operation [6]”“without human intervention [7].”

    Definition 23: The mathematical model of an AS is a highlevel intelligent system for implementing advanced and complex intelligent abilities compatible to human intelligence in systems.

    which extends the power of system intelligence from reflexive, imperative, and adaptive intelligence to autonomous and cognitive intelligence.

    AS implement nondeterministic, context-dependent, and adaptive behaviors of machine intelligence. AS are a nonlinear system that depends not only on current stimuli or demands, but also on internal status and willingness formed by long-term historical events and current rational or emotional goals (Fig. 3). The major AS capabilities need to be extended to the cognitive intelligence level towards highly intelligent systems beyond classic adaptive and imperative systems.

    Lemma 1: The behavioral model AS|§ of AS is inclusively aggregated from the bottom up.

    where |§ is the system suffix and each intelligent behavior has been formally defined in Section III.

    Proof: Lemma 1 may be proven based on the HIM definitions summarized in Fig. 3. ■

    Theorem 1: The relationships among all levels of intelligent behaviors as formally modeled in HIM are hierarchical a) and inclusive b).

    Theorem 1 indicates that any lower layer behavior of an AS is a subset of those of a higher layer. In other words, any higher layer behavior of AS is a natural aggregation of those of lower layers as shown in Figs. 2 and 3 as well as (22) and(23). Therefore, Theorem 1 and Lemma 1 reveal the necessary and sufficient conditions of AS.

    B. The Roles of Humans in Hybrid Autonomous Systems

    Because the most matured paradigm of AS is the brain,advanced AS are naturally open to incorporate human intelligence as indicated by the HIM. This notion leads to a broad form of hybrid AS with coherent human-system interactions. Therefore, human traits and factors play an irreplaceable role in hybrid AS in intelligence and system theories.

    Definition 24: Human factors are the roles and effects of humans in a hybrid AS that introduce special strengths,weaknesses, and/or uncertainty underpinned by human traits.

    The properties of human strengths in AS are recognized such as highly matured autonomous behaviors, complex decision-making, skilled operations, comprehensive senses,flexible adaptivity, perceptive power, and complicated system cooperation. However, the properties of human weaknesses in AS are identified as low efficiency, tiredness, slow reaction,error-proneness, and easy distraction. Furthermore, a set of human uncertainty revealed in AS includes productivity,performance, accuracy, reaction time, persistency, reliability,attitude, motivation, and the tendency to try unknown things even if they are prohibited.

    We have found that human motivation, attitude, and social norms (rules) may affect human perceptive and decisionmaking behaviors as well as their trustworthiness known as the autonomous human behavior model (AHBM) as shown in Fig. 4. AHBM illustrates the interactions of human perceptive behaviors involving emotions, motivations, attitudes, and decisions. In AHBM, a rational motivation, decision, and behavior may be quantitatively derived before an observable action is executed. The highly matured human intelligence illustrated in AHBM may be applied as a reference model for AS and cognitive systems [5], [19].

    Fig. 4. The AHBM for explaining the roles of human intelligence in AS.

    TABLE II THE DOMAIN OF AS AND THEORETICAL/TECHNOLOGICAL CHALLENGES

    According to Lemma 1, Theorem 1, and the AHBM, a hybrid AS with humans in the loop gains strengths towards the implementation of cognitive intelligent systems. The cognitive AS sufficiently enables a powerful intelligent system by the strengths of both human and machine intelligence. This is why intelligence and system sciences may inspire us towards the development of fully autonomous intelligent systems in highly demanded engineering applications [1]-[3], [10], [12], [18], [20], [28], [34].

    V. EMERGING PARADIGMS OF AUTONOMOUS SYSTEMS

    The preceding sections have formally described AS and their theoretical and behavioral foundations. On the basis of the HIM and the theoretical structures of AS, an overall outlook on the domain of AS towards advanced general AI(GAI) systems is provided in this section. It then presents a set of emerging paradigms of AS including brain-inspired systems, cognitive robots, and autonomous knowledge learning systems.

    A perception about the structure of the domain of AS embodying the advanced AI field may be classified into three categories encompassing theoretical foundations, emerging domains of applications, and AS engineering technologies as summarized in Table II. Many topics under study in Table II represent fundamental challenges to AS, which indicate the theoretical and methodological immaturity of AS or their constraints and bottlenecks. The following case studies towards AS demonstrate the scope of problems and the demand for advanced theories and technologies for AS development.

    A. Brain-Inspired Systems

    Brain-inspired systems (BIS) are a typical AS because basic research on the cognitive foundations of natural intelligence may shed light on the general mechanisms of various forms of machine intelligence [4], [33]. Modern cognitive computers,computational intelligence, cognitive robots, and intelligent internet of things (IoT) [11], [12], [17], [35] are typical paradigms of brain-inspired systems. Therefore, understanding the brain as perhaps the most matured intelligent system becomes a foundation towards AS design and implementation.It is recognized that as a computer may be elaborated by hierarchical reduction from its logical models to the chip-level implementations, the comprehension of the brain may also be achieved in a similar reductive approach to BIS development.

    The logical model of the brain may be embodied by the layered reference model of the brain (LRMB) as shown in Fig. 5[25]. LRMB reveals the insights of the brain by a set of 56 cognitive processes at seven layers encompassing: 1)Sensation, 2) Action, 3) Memory, 4) Perception, 5) Cognition,6) Inference, and 7) Intelligence. The lower four layers are the fundamental subconscious functions of the natural intelligence known as the brain’s operating system; while the higher three layers are classified as the acquired conscious intelligent functions, which underpin all real-time behaviors and life functions of humans as a temporary composition of run-time instances. Therefore, the mechanism of the natural intelligence embodied by the brain may also be rigorously reduced from the top down as indicated by (3) and/or (22) and extended in Fig. 3 . Once all the cognitive processes are implemented by computational intelligence, we obtain a powerful AS and a GAI platform may emerge.

    Fig. 5. The layered reference model of the brain (LRMB).

    B. Cognitive Robots

    Cognitive robots (CRs) are a highly representative paradigm of AS and BIS underpinned by transdisciplinary studies on computational intelligence, robotics, and brain sciences [32],[34]. A CR is designed to reflect the fundamental philosophy in contemporary robotics and AS. Although a CR does not necessarily appear humans, it is expected to be capable of learning, inferencing, precepting, thinking, and autonomous decision-making as humans compatible to the mechanism of LRMB in Section V-A.

    It is recognized that traditional robots are implemented by program-controlled technologies at Level 2 to 3 of the HIM with imperative/adaptive intelligence as modeled in Lemma 1 and Fig. 3 . The key functions missed in non-autonomous robots are conscious environment awareness, cognitive inference, knowledge acquisition, semantic comprehension,rational decision-making, real-time behavioral generation, and creative problem-solving. However, CRs beyond classic robots are powered by an AS embodied by the essential ability of run-time intelligent behavior generation for unpredicted problem solving.

    A CR is configured according to LRMB for mimicking the brain [25] as shown in Fig. 6 . The CR architecture encompasses sensory, learning, inference, and behavior generation engines [34]. These engines mimic the brain’s perceptive and conscious engines where the latter is refined by CR’s learning/inference/behavior generation engine, specifically. CR carries out autonomous learning and reasoning for implementing the cognitive, causal, recursive, and inductive intelligence as modeled in Section III. A cognitive knowledge base (CKB) is recognized as a necessary and common foundation for implementing all level intelligences across CR,AS, and the brain. In other words, no autonomous intelligence would be generated without the CKB as evidenced by various forms of memories in the brain as shown in Fig. 6. CR is expected to be capable to acquire deep semantic understanding of human knowledge. It is driven by formal intelligent mathematics (IM) [30] encompassing concept algebra, semantic algebra, real-time behavior process algebra,inference algebra, big data algebra, visual semantic algebra,and system algebra.

    The CR theories and technologies pave a way to transfer human knowledge and their cognition to machines via AS.They enable CRs to coordinate with humans on the AS platform. Pilot experiments [33] have demonstrated that a CR is able to build its own CKB for quantitatively representing conceptual, semantic, and behavioral knowledge, which extends machine intelligence to the advanced level of autonomous and cognitive intelligence according to the HIM.CRs are expected to form the next generation of intelligent computing platform for AS applications.

    C. Autonomous Knowledge Learning Systems

    The design of CKB for CRs reveals that the essence of knowledge is a cognitive mapping between a newly acquired concept against all known ones in CKB of AS [5], [25].Furthermore, the basic unit of knowledge is discovered as a binary relation (bir) [33] as a counterpart to bit as the unit of information. Therefore, knowledge science has emerged as a contemporary discipline for explaining the mechanism of intelligence generation in both AS and human brains [3], [33].As a simulation of unsupervised and autonomous machine knowledge learning by a CR, the hierarchical and interconnected knowledge has been autonomously generated mimicking the neural clusters for knowledge representation in the brain as shown in Fig. 7 . It demonstrates a set of knowledge learning results by a CR for the first 600+frequently used concepts in English powered by the intelligent mathematics of concept and semantic algebras [27], [30]. It took the CR less than a minute to rigorously and quantitatively acquire such a large degree of knowledge, which would require several person-months in traditional ontology-based manual knowledge representation.

    According to the HIM for AS, the ability for knowledge learning and reasoning is the foundation for intelligence generation for both humans and AS. In other words, the traditional data-driven approach may not necessarily lead to direct intelligence generation. This observation reveals a fundamental difference between AS and classical data regression technologies for AI. Therefore, the Bir theory for cognitive knowledge representation [31] has enabled the development of the CKB platform of AS and their shareability between machines and humans. This transdisciplinary theory paves a way to the development of the next generation computers as cognitive computers (CCs) for efficient knowledge processing for AS.

    Fig. 6. The architectural analogy of cognitive robots and the brain.

    Fig. 7. Experiment on autonomous knowledge learning by AS.

    AS theories and technologies shall lead to the design and implementation of advanced GAI systems coordinating humans and intelligent machines in a coherent framework in order to augment human intelligence. The expected advances in AS should enable machinable thinking and reasoning,unsupervised machine learning for knowledge acquisition and behavior generation, and autonomous assistants to human users for problem-solving and decision-making via downloadable intelligence enabled by AS in the future.

    VI. CONCLUSION

    This work has explored the intelligence, cognition, and system science foundations of AS towards the emerging field of general AI theories and methodologies. A hierarchical intelligence framework of AS has been developed for elaborating the properties of autonomous intelligence. Such properties and hierarchical models offer a roadmap toward the design and implementation of AS. The first finding of this work is that the fundamental challenges for AS have been deeply rooted in the fact that almost all AI problems have been out of the domain of real numbers R designated in classic mathematics. The second finding is that the current programming languages cannot express and implement autonomous behaviors, because the run-time indeterministic and uncertain behaviors cannot be expressed and implemented on traditional computing platforms. The third finding is that the basic unit of knowledge for AS and AI is a bir. As a result,a theoretical framework of AS has been developed towards highly demanded engineering applications including braininspired cognitive systems, cognitive robots, unmanned systems, autonomous vehicles, autonomous defence systems,and intelligent IoT.

    APPENDIX

    INDEX OF MATHEMATICAL SYMBOLS AND NOTATIONS| Type suffixes( )1. Reflexive: ˙Ire f Bre f |REF 2. Imperative: ˙Iimp Bimp -2.1 Event-drive: ˙Ieimp Beimp |E 2.2 Time-driven: ˙Itimp Btimp |TM 2.3 Interrupt-driven: ˙Iint Intelligence ˙I Symbol ( ) B| Behavior ( ) |T imp Bintimp |⊙3. Adaptive: ˙Iadp Badp -3.1 Analogy-based: ˙Iabadp Babadp |AB adp Bfmadp |FM 3.3 Environment-aware: ˙Ieaadp Beaadp |EA 4. Autonomous: ˙Iaut Baut -4.1 Perceptive: ˙Ipe 3.2 Feedback-modulated: ˙Ifm aut Bpeaut |PE 4.2 Problem-driven: ˙Ipd aut Bpdaut |PD 4.3 Goal-driven: ˙Igo aut Bgoaut |GO 4.4 Decision-driven: ˙Idd aut Bddaut |DD 4.5 Deductive: ˙Ide aut Bdeaut |DE 5. Cognitive: ˙Icog Bcog -5.1 Knowledge-based: ˙Ikbcog Bkbcog |KB 5.2 Learning-driven: ˙Ildcog Bldcog |LD 5.3 Inference-driven: ˙Iifcog Bifcog |IF 5.4 Inductive: ˙Iidcog Bidcog |ID

    久久人人精品亚洲av| 婷婷色综合大香蕉| av免费观看日本| 久久99热这里只有精品18| 国产亚洲91精品色在线| 69人妻影院| 成人午夜高清在线视频| 老司机影院成人| 久久国内精品自在自线图片| 国产人妻一区二区三区在| 一本久久中文字幕| 日日摸夜夜添夜夜爱| 少妇猛男粗大的猛烈进出视频 | 中文字幕人妻熟人妻熟丝袜美| 婷婷色av中文字幕| 桃色一区二区三区在线观看| 国产在线男女| 男女啪啪激烈高潮av片| 一边亲一边摸免费视频| 亚洲va在线va天堂va国产| 男人狂女人下面高潮的视频| 国产成人一区二区在线| 自拍偷自拍亚洲精品老妇| 又粗又硬又长又爽又黄的视频 | 一本精品99久久精品77| 一个人看的www免费观看视频| 国产午夜精品久久久久久一区二区三区| 一级毛片电影观看 | 欧美色欧美亚洲另类二区| 成人一区二区视频在线观看| 又粗又爽又猛毛片免费看| 亚洲成人av在线免费| 99视频精品全部免费 在线| 亚洲成人av在线免费| 悠悠久久av| 免费观看a级毛片全部| 亚洲18禁久久av| 岛国在线免费视频观看| 春色校园在线视频观看| 免费在线观看成人毛片| 又爽又黄a免费视频| 18+在线观看网站| 在线观看66精品国产| 日韩人妻高清精品专区| 色吧在线观看| 哪个播放器可以免费观看大片| 久久亚洲精品不卡| 久久久精品94久久精品| 18禁在线无遮挡免费观看视频| 午夜激情欧美在线| 日本在线视频免费播放| 中文资源天堂在线| 你懂的网址亚洲精品在线观看 | 我的女老师完整版在线观看| 1024手机看黄色片| 波多野结衣巨乳人妻| 欧美+亚洲+日韩+国产| 国产精品三级大全| 久久精品国产亚洲网站| 特级一级黄色大片| 九九久久精品国产亚洲av麻豆| 久久韩国三级中文字幕| 又黄又爽又刺激的免费视频.| 国产熟女欧美一区二区| 亚洲av成人av| 欧美xxxx性猛交bbbb| 亚洲av中文字字幕乱码综合| 国产午夜精品久久久久久一区二区三区| 久久综合国产亚洲精品| 老司机福利观看| 大香蕉久久网| 亚州av有码| 亚洲欧美日韩高清专用| 精品久久久久久成人av| 色尼玛亚洲综合影院| 久久热精品热| 日韩制服骚丝袜av| 欧美性猛交╳xxx乱大交人| 1024手机看黄色片| 欧美高清性xxxxhd video| 精品少妇黑人巨大在线播放 | 熟妇人妻久久中文字幕3abv| 少妇高潮的动态图| 久久国产乱子免费精品| 国产精品久久久久久av不卡| 精品久久久噜噜| 我的老师免费观看完整版| 久久精品影院6| 能在线免费观看的黄片| 欧美+日韩+精品| 美女大奶头视频| 99国产精品一区二区蜜桃av| 久99久视频精品免费| 久久人人爽人人片av| 国产久久久一区二区三区| 观看免费一级毛片| 亚洲在久久综合| 色噜噜av男人的天堂激情| 女人十人毛片免费观看3o分钟| 国产淫片久久久久久久久| 国产精品一区二区三区四区久久| av福利片在线观看| 国产精品久久久久久亚洲av鲁大| 国产一区亚洲一区在线观看| 99国产精品一区二区蜜桃av| 麻豆乱淫一区二区| 一级av片app| 99热这里只有是精品50| 色综合色国产| 12—13女人毛片做爰片一| 国产高清激情床上av| 欧美性感艳星| 可以在线观看的亚洲视频| 午夜福利成人在线免费观看| 精品久久久久久成人av| 午夜精品一区二区三区免费看| 亚洲丝袜综合中文字幕| 九九在线视频观看精品| 哪里可以看免费的av片| 国产精品伦人一区二区| 亚洲人成网站在线播放欧美日韩| 男女边吃奶边做爰视频| 午夜精品国产一区二区电影 | 日韩三级伦理在线观看| 麻豆av噜噜一区二区三区| 岛国毛片在线播放| 亚州av有码| 狂野欧美白嫩少妇大欣赏| 亚洲国产日韩欧美精品在线观看| 男的添女的下面高潮视频| 久久久成人免费电影| 亚洲美女视频黄频| 日韩大尺度精品在线看网址| 久久久欧美国产精品| 国内精品美女久久久久久| 久久亚洲精品不卡| 69av精品久久久久久| 最近的中文字幕免费完整| 欧美3d第一页| 九九爱精品视频在线观看| 中文字幕久久专区| 波野结衣二区三区在线| 欧美日本亚洲视频在线播放| 一边亲一边摸免费视频| 午夜老司机福利剧场| 干丝袜人妻中文字幕| 久久亚洲精品不卡| 国产一区二区激情短视频| 亚洲av第一区精品v没综合| 午夜爱爱视频在线播放| 内地一区二区视频在线| 91精品一卡2卡3卡4卡| 边亲边吃奶的免费视频| 久久99热这里只有精品18| 国产黄片视频在线免费观看| 国内精品宾馆在线| 国产伦理片在线播放av一区 | 一边摸一边抽搐一进一小说| 国产伦在线观看视频一区| 男女下面进入的视频免费午夜| 一本久久精品| 亚洲无线观看免费| 26uuu在线亚洲综合色| 中国美女看黄片| 国产av麻豆久久久久久久| 美女大奶头视频| 一本久久精品| 日本熟妇午夜| 亚洲国产欧美人成| 中文在线观看免费www的网站| 一区二区三区高清视频在线| 两个人视频免费观看高清| 国产在线精品亚洲第一网站| www日本黄色视频网| 国产精华一区二区三区| 亚洲,欧美,日韩| 精品一区二区免费观看| 国产亚洲欧美98| 免费av不卡在线播放| 99在线人妻在线中文字幕| 干丝袜人妻中文字幕| 国产熟女欧美一区二区| 最近中文字幕高清免费大全6| 老师上课跳d突然被开到最大视频| 欧美+亚洲+日韩+国产| 色播亚洲综合网| 中文字幕久久专区| 久久久成人免费电影| 三级毛片av免费| 看十八女毛片水多多多| 狂野欧美激情性xxxx在线观看| 精品日产1卡2卡| 1000部很黄的大片| videossex国产| 赤兔流量卡办理| 午夜a级毛片| 国产亚洲av嫩草精品影院| 亚洲无线观看免费| 国产一区二区在线av高清观看| 极品教师在线视频| 日韩欧美 国产精品| av在线播放精品| 亚洲在线自拍视频| 成人国产麻豆网| 在线免费十八禁| 黄色一级大片看看| 国产黄a三级三级三级人| 国产v大片淫在线免费观看| 亚洲欧美精品自产自拍| 免费观看人在逋| 亚洲在线自拍视频| 久久亚洲精品不卡| 日本黄色片子视频| 日本黄大片高清| 搡老妇女老女人老熟妇| a级一级毛片免费在线观看| 少妇丰满av| 国产真实乱freesex| 久久久久国产网址| 我要看日韩黄色一级片| 精品国内亚洲2022精品成人| 99热网站在线观看| 国产精品伦人一区二区| 亚洲精品乱码久久久久久按摩| 毛片一级片免费看久久久久| 国产激情偷乱视频一区二区| 久久久久久久久大av| 久久久久性生活片| 美女脱内裤让男人舔精品视频 | 一级毛片aaaaaa免费看小| 色噜噜av男人的天堂激情| 国产麻豆成人av免费视频| 网址你懂的国产日韩在线| 国产精品爽爽va在线观看网站| 国产精品永久免费网站| 3wmmmm亚洲av在线观看| 久久精品国产亚洲网站| 亚洲欧美精品自产自拍| 免费看美女性在线毛片视频| 桃色一区二区三区在线观看| 啦啦啦观看免费观看视频高清| 一区二区三区四区激情视频 | 亚洲精品自拍成人| 日韩三级伦理在线观看| 美女 人体艺术 gogo| 麻豆成人av视频| 国产精品久久久久久精品电影| 中文字幕精品亚洲无线码一区| 特级一级黄色大片| 91av网一区二区| av在线播放精品| 日本三级黄在线观看| 波野结衣二区三区在线| 亚洲一级一片aⅴ在线观看| 亚洲国产精品成人久久小说 | 中国国产av一级| 免费在线观看成人毛片| 国产伦在线观看视频一区| 久久国产乱子免费精品| 日本av手机在线免费观看| 又爽又黄无遮挡网站| 岛国毛片在线播放| 国产蜜桃级精品一区二区三区| 一级毛片我不卡| 日本一本二区三区精品| 久久精品国产亚洲av香蕉五月| 日本撒尿小便嘘嘘汇集6| 永久网站在线| 亚洲va在线va天堂va国产| 天天一区二区日本电影三级| 舔av片在线| 啦啦啦观看免费观看视频高清| 精品国产三级普通话版| 亚洲欧美精品综合久久99| ponron亚洲| 亚洲欧美清纯卡通| 黄色日韩在线| 两个人视频免费观看高清| 在线国产一区二区在线| 亚洲国产欧美人成| 国产av麻豆久久久久久久| 99久久中文字幕三级久久日本| 99久国产av精品| 久久久午夜欧美精品| 好男人在线观看高清免费视频| 欧美最新免费一区二区三区| 亚洲aⅴ乱码一区二区在线播放| 欧美变态另类bdsm刘玥| 男女边吃奶边做爰视频| 一级毛片aaaaaa免费看小| 国产精品,欧美在线| 亚洲av不卡在线观看| 亚洲精品日韩在线中文字幕 | 日本五十路高清| 毛片女人毛片| 欧美日韩一区二区视频在线观看视频在线 | 网址你懂的国产日韩在线| 99九九线精品视频在线观看视频| 午夜免费男女啪啪视频观看| 亚洲国产欧美人成| av专区在线播放| 色吧在线观看| 老女人水多毛片| 日韩精品有码人妻一区| 国产精品蜜桃在线观看 | 简卡轻食公司| 国产免费一级a男人的天堂| 18禁裸乳无遮挡免费网站照片| 看免费成人av毛片| 乱人视频在线观看| 在线国产一区二区在线| 狂野欧美激情性xxxx在线观看| 联通29元200g的流量卡| 哪里可以看免费的av片| 欧美最黄视频在线播放免费| 亚洲国产欧洲综合997久久,| 国内精品久久久久精免费| 国产精品久久久久久精品电影| 成熟少妇高潮喷水视频| 久久精品久久久久久噜噜老黄 | 国产色婷婷99| 在线天堂最新版资源| 国产在线男女| 国产女主播在线喷水免费视频网站 | a级毛片免费高清观看在线播放| 啦啦啦啦在线视频资源| 国产精品一区二区在线观看99 | 亚洲欧美精品自产自拍| av又黄又爽大尺度在线免费看 | 国产伦精品一区二区三区视频9| 亚洲va在线va天堂va国产| 日韩一区二区视频免费看| 欧美日韩在线观看h| 97热精品久久久久久| 联通29元200g的流量卡| 亚洲综合色惰| 老女人水多毛片| www.色视频.com| 亚洲av电影不卡..在线观看| 少妇丰满av| 女的被弄到高潮叫床怎么办| 少妇被粗大猛烈的视频| 蜜桃久久精品国产亚洲av| 国产一区二区在线观看日韩| 内地一区二区视频在线| 久久久成人免费电影| 亚洲精品乱码久久久v下载方式| 少妇丰满av| 精品日产1卡2卡| www日本黄色视频网| 国产一级毛片在线| 亚洲成人中文字幕在线播放| 女的被弄到高潮叫床怎么办| 精品国产三级普通话版| 亚洲国产精品国产精品| 99热只有精品国产| 联通29元200g的流量卡| 成人特级av手机在线观看| 国产精品野战在线观看| 亚洲最大成人av| 麻豆一二三区av精品| 亚洲精品粉嫩美女一区| 国产精品野战在线观看| 国产一区二区三区在线臀色熟女| 久久亚洲国产成人精品v| 日韩强制内射视频| 国产精品野战在线观看| 日韩制服骚丝袜av| 亚洲欧美清纯卡通| 一本久久中文字幕| 夜夜夜夜夜久久久久| 99久久九九国产精品国产免费| 亚洲内射少妇av| 乱码一卡2卡4卡精品| 亚洲成人精品中文字幕电影| 欧美成人一区二区免费高清观看| 欧美日韩国产亚洲二区| 中文精品一卡2卡3卡4更新| 女的被弄到高潮叫床怎么办| 99国产极品粉嫩在线观看| 久久久色成人| 欧美最新免费一区二区三区| 九色成人免费人妻av| 亚洲av不卡在线观看| 国产亚洲5aaaaa淫片| 久久久色成人| 少妇高潮的动态图| АⅤ资源中文在线天堂| 秋霞在线观看毛片| 夫妻性生交免费视频一级片| 国产精品女同一区二区软件| 国产精品蜜桃在线观看 | 欧美不卡视频在线免费观看| 哪里可以看免费的av片| 亚洲人成网站在线观看播放| 美女xxoo啪啪120秒动态图| 91狼人影院| 超碰av人人做人人爽久久| 国产伦在线观看视频一区| 校园人妻丝袜中文字幕| 人人妻人人澡人人爽人人夜夜 | av免费在线看不卡| 午夜精品一区二区三区免费看| 国产国拍精品亚洲av在线观看| 国产精品电影一区二区三区| 国产精品精品国产色婷婷| 国产免费男女视频| 六月丁香七月| 精品熟女少妇av免费看| 午夜爱爱视频在线播放| 亚洲乱码一区二区免费版| 99久国产av精品| 亚洲欧洲国产日韩| 中文在线观看免费www的网站| 亚洲精品色激情综合| 久久久精品大字幕| a级毛片免费高清观看在线播放| 亚洲成av人片在线播放无| 美女xxoo啪啪120秒动态图| 伊人久久精品亚洲午夜| 欧美成人一区二区免费高清观看| 人妻夜夜爽99麻豆av| 欧美色视频一区免费| 99视频精品全部免费 在线| 日日摸夜夜添夜夜爱| 精品久久久噜噜| 联通29元200g的流量卡| 日韩欧美在线乱码| 国产一区二区亚洲精品在线观看| 亚洲精品456在线播放app| 亚洲成人av在线免费| 久久久成人免费电影| 亚洲国产精品久久男人天堂| 黄片无遮挡物在线观看| 中文欧美无线码| 少妇熟女欧美另类| 边亲边吃奶的免费视频| 日本欧美国产在线视频| 欧美最新免费一区二区三区| 两个人视频免费观看高清| 欧美高清成人免费视频www| 久久久久久久久大av| 少妇熟女欧美另类| 欧美+亚洲+日韩+国产| 国产精品一区www在线观看| 亚洲欧美精品自产自拍| 在线观看午夜福利视频| 久久久久国产网址| 在线观看美女被高潮喷水网站| 国产在线男女| 国产精品国产三级国产av玫瑰| 亚洲第一电影网av| 国产午夜精品久久久久久一区二区三区| 日韩 亚洲 欧美在线| 一级黄色大片毛片| 午夜老司机福利剧场| 日韩制服骚丝袜av| 九九热线精品视视频播放| 欧美潮喷喷水| 观看美女的网站| 国语自产精品视频在线第100页| 久久国内精品自在自线图片| 亚洲精品自拍成人| 成年av动漫网址| 免费不卡的大黄色大毛片视频在线观看 | 久久久精品欧美日韩精品| 亚洲国产精品合色在线| 男插女下体视频免费在线播放| 日本免费一区二区三区高清不卡| 久久精品国产99精品国产亚洲性色| 国内揄拍国产精品人妻在线| 大又大粗又爽又黄少妇毛片口| 最近视频中文字幕2019在线8| 大香蕉久久网| 男人和女人高潮做爰伦理| 亚洲精品亚洲一区二区| 蜜桃久久精品国产亚洲av| 亚洲国产欧美在线一区| 国产成人一区二区在线| 一级毛片我不卡| 国产乱人视频| 精品久久久久久成人av| 一边亲一边摸免费视频| 亚洲欧美日韩东京热| 一级毛片我不卡| 日韩一本色道免费dvd| 99热这里只有是精品在线观看| 男女下面进入的视频免费午夜| 国产成人a区在线观看| or卡值多少钱| 三级男女做爰猛烈吃奶摸视频| 久久久久网色| 成人三级黄色视频| 久久久a久久爽久久v久久| a级毛片免费高清观看在线播放| 人妻制服诱惑在线中文字幕| 毛片女人毛片| 91aial.com中文字幕在线观看| 亚洲美女搞黄在线观看| 亚洲欧洲日产国产| 国产精品一区二区三区四区免费观看| 国产老妇伦熟女老妇高清| 久久99精品国语久久久| 国产精品永久免费网站| 91久久精品国产一区二区三区| 看免费成人av毛片| 久久久国产成人免费| 国产精品福利在线免费观看| 国产又黄又爽又无遮挡在线| 国产精品精品国产色婷婷| 一级黄色大片毛片| 少妇熟女欧美另类| 久久99热这里只有精品18| 国产激情偷乱视频一区二区| 少妇猛男粗大的猛烈进出视频 | 中出人妻视频一区二区| 国产高清视频在线观看网站| 搞女人的毛片| 一本一本综合久久| 哪个播放器可以免费观看大片| 亚洲av成人精品一区久久| 在线观看美女被高潮喷水网站| 女同久久另类99精品国产91| 麻豆精品久久久久久蜜桃| 久久99热这里只有精品18| 国产午夜精品一二区理论片| 久久午夜福利片| 男女啪啪激烈高潮av片| 欧洲精品卡2卡3卡4卡5卡区| 97超视频在线观看视频| 免费人成在线观看视频色| 国产一区二区亚洲精品在线观看| 亚洲精华国产精华液的使用体验 | 亚洲自偷自拍三级| 亚洲在线观看片| 国产精品三级大全| 欧美成人精品欧美一级黄| 久久久久久伊人网av| 日本一本二区三区精品| 国产精品日韩av在线免费观看| 亚洲人成网站在线观看播放| 最近视频中文字幕2019在线8| 内射极品少妇av片p| 两个人视频免费观看高清| 国产成人一区二区在线| 高清日韩中文字幕在线| 在线免费观看不下载黄p国产| 精品人妻视频免费看| 久99久视频精品免费| 日韩欧美精品v在线| 久久久国产成人精品二区| 精品久久久久久久久亚洲| 夜夜夜夜夜久久久久| 成人三级黄色视频| 久久午夜亚洲精品久久| 91久久精品电影网| 麻豆精品久久久久久蜜桃| 国产 一区精品| 特大巨黑吊av在线直播| 久久这里只有精品中国| 97超碰精品成人国产| 春色校园在线视频观看| 91av网一区二区| 成人欧美大片| 99久久无色码亚洲精品果冻| 午夜福利在线在线| 国产在线精品亚洲第一网站| 岛国在线免费视频观看| 91午夜精品亚洲一区二区三区| 精品久久久久久久久久久久久| 偷拍熟女少妇极品色| 身体一侧抽搐| 蜜桃久久精品国产亚洲av| 中文字幕熟女人妻在线| 在线天堂最新版资源| 免费看日本二区| 麻豆国产97在线/欧美| 夫妻性生交免费视频一级片| 麻豆av噜噜一区二区三区| 国产亚洲91精品色在线| 亚洲经典国产精华液单| 桃色一区二区三区在线观看| 亚洲国产欧洲综合997久久,| 成年版毛片免费区| 秋霞在线观看毛片| 国产成年人精品一区二区| 国产精品乱码一区二三区的特点| 成人性生交大片免费视频hd| 久久婷婷人人爽人人干人人爱| 91狼人影院| 国产人妻一区二区三区在| 丰满的人妻完整版| 久久精品国产亚洲av涩爱 | 亚洲欧美精品自产自拍| 国产精品久久电影中文字幕| 激情 狠狠 欧美| 精品一区二区三区人妻视频| 亚洲不卡免费看| 免费看美女性在线毛片视频| 一进一出抽搐gif免费好疼| 色尼玛亚洲综合影院| 少妇裸体淫交视频免费看高清| 国产爱豆传媒在线观看| 老女人水多毛片| 亚洲aⅴ乱码一区二区在线播放| 2022亚洲国产成人精品| 黄色欧美视频在线观看| 亚洲婷婷狠狠爱综合网| 亚洲自拍偷在线| 成人午夜精彩视频在线观看| 日本一二三区视频观看| 久久精品久久久久久噜噜老黄 | 亚洲欧美日韩无卡精品| 美女被艹到高潮喷水动态| 久久久久九九精品影院|