Controllers for Robotics and. Manufacturing Devices with Temporal. Logic and the ControlD System. Marco Antoniotti.


 Percival Walton
 1 years ago
 Views:
Transcription
1 Synthesis and Veriæcation of Discrete Controllers for Robotics and Manufacturing Devices with Temporal Logic and the ControlD System. by Marco Antoniotti September 1995 A dissertation in the Department of Computer Science submitted to the faculty ofthe Graduate School of Arts and Science in partial fulællmentof the requirements for the degree of Doctor of Philosophy at New York University Approved: Professor Bhubaneswar Mishra Research Advisor
2 cæ Marco Antoniotti All Rights Reserved 1995
3
4 To my sister Franca, who made this a better world To my nephew Tommaso and my niece Agnese, who will make this a better world To Mary, who makes this a better world iv
5 Acknowledgments I am really indebted to my advisor Bud Mishra for his continuous support and friendship during myyears as a graduate student. He has been a been a guide through æelds that were unknown to meandhas been a guide during troubled times. There are still many æelds I will need to wander through and I am sure I will ænd his traces there. Iwant tothank Professor Mohsen Jafari of Rutgers University for his help with the CRAMTD project and for kindness and enthusiasm since our ærst meetingat a conference. A person who deserves much credit for my achievements isstefania Bandini of the Universitaça degli Studi di Milano, who has always been able to keep me onmytoes and to come tomy aid whenever necessary. She has always been the source of precious advice. Dr. R. Kurshan of AT&T Bell Laboratories was very kind andhelpful duringtheænal phaseofmythesis by providing valuable information on many technical details and developments inthe theory of related topics. Iwant tothank Professor J. T. Schwartz for his valuable commentson my work. Professor R. Wallace is the person who introduced me tothe Walking Machine problem. He designed and worked on the hardware and was very helpful in giving me feedback inorder to keep my research ærmly set on a pragmatic foundation. v
6 Friends like Giovanni Gallo and Alberto Policriti are a blessing for anybody who is doing research andwhoneeds a companion to sneak out tothe movies every once in a while. Iwant tothank Professor K. Perlin, Professor S. Mallat, Professor D. Shasha and Professor R. Boppana for their kindness and for their suggestions over the past years. Special thanks go to many people in Milan who haveinvarious ways contributed to mysuccess as a graduate student: Professor G. Mauriofthe Dipartimento di Scienze dell'informazioneofthe Universitaça degli Studi di Milano and all the friends at Quinary S.p.A., where the machines are named after Lisp functions. Thanks are also due to Anina KarmenMeade ofthe Department of Computer Science of NYU for her patience with myoverly complicated administrative problems andtofred Hansen for all his work inthe Robotics Research Laboratory. A group of people who deserves thanks and other things is constituted by my roommate over the years: Sunder Sethuraman, a great mathematician and abetter soccer player than I'll ever be; David Bacon, Ron Even and Marek Teichmann, mythree oæce mates who endured meandtoto Paxia, who I had to endure. Finally, Ithank Mary, whomilove, who loves meand, luckily, thinks the factorial of any numberis7. vi
7 Contents 1 Introduction 1 2 Manufacturing, Robotics, Control Theory and Veriæcation Models and Control : : : : : : : : : : : : : : : : : : : : : : : : Classical Control Theory : : : : : : : : : : : : : : : : : : : : : Control Theory Applied to DES : : : : : : : : : : : : : : : : : Building Supervisory Controllers for CDES : : : : : : : Veriæcation and Temporal Logic : : : : : : : : : : : : : : : : : Temporal Properties of Systems : : : : : : : : : : : : : Temporal Logics: Linear and Branching Time : : : : : Theorem Proving andmodel Checking : : : : : : : : : Veriæcation with Automata Theory : : : : : : : : : : : Reductions and Compositional Veriæcation : : : : : : : Real TimeVeriæcation : : : : : : : : : : : : : : : : : : Role of Veriæcation Tools : : : : : : : : : : : : : : : : : Hybrid Systems Veriæcation and Control : : : : : : : : : : : : 35 vii
8 3 Temporal Logic Supervisor Synthesis with Veriæcation Notational Preliminaries : : : : : : : : : : : : : : : : : : : : : CDES Notions and Notations : : : : : : : : : : : : : : CTL Direct Synthesis of Supervisors : : : : : : : : : : : : : : : Controlled Semantics as Model Restriction : : : : : : : Main Objectiveand Notable Problems : : : : : : : : : Inductive Construction of the Controlled Semantics : : Circumventingthe Problems : : : : : : : : : : : : : : : CTL Soundness of Controlled Semantics : : : : : : : : : Simple CTLSynthesis Algorithm : : : : : : : : : : : : : : : : : Existence of a Supervisor Synthesis Algorithm : : : : : 88 4 The ControlD System Revised Algorithm and The Implementation : : : : : : : : : : State Space Traversing Algorithm : : : : : : : : : : : : Tradeoæs in the Treatment of Disjunctive Forms : : : : Comparison with Standard Supervisor Synthesis Algorithm : : : : : : : : : : : : : : : : : : : : : : : : : : : : Open Problem: Symbolic Representation : : : : : : : : Veriæcation and Synthesis : : : : : : : : : : : : : : : : : : : : The Environment : : : : : : : : : : : : : : : : : : : : : : : : : ControlD Components anduserinterface : : : : : : :109 viii
9 5 Building a DiscreteèHybrid Controller for a Walking Machine A Brief History of Walking Machines : : : : : : : : : : : : : : Problems for Walking Machines : : : : : : : : : : : : : : : : : Leg Behavior Models and Gaits : : : : : : : : : : : : : StabilityofWalking Machines : : : : : : : : : : : : : : Synchronization and Realtime Control : : : : : : : : : Building Walking Machines Controllers with ControlD : : : : Discrete Controller for the Walking Machine : : : : : : Continuous Control Constraints : : : : : : : : : : : : :129 6 Ensuring Failure Behavior for the CRAMTD Manufacturing Line CRAMTD Project Tray Packing Model : : : : : : : : : : : : : Failure Behavior Control : : : : : : : : : : : : : : : : : : : : : Modeling thetray Packing Line : : : : : : : : : : : : : CTL Speciæcation of Behavior : : : : : : : : : : : : : : Concluding Remarks : : : : : : : : : : : : : : : : : : : : : : :143 7 Conclusion and Future Work 144 ix
10 List of Figures 1.1 The walking machine example : : : : : : : : : : : : : : : : : : Schematic drawing ofthe tray pack lineofthe CRAMTD project The standard arrangement of plant G and supervisor hr;'i. : Schematic æowchart of the Supervisor building steps. : : : : : A case illustrating the requirements for AX supervisors : : : : A counterexample for the intuitive supervisor map construction for disjunctions : : : : : : : : : : : : : : : : : : : : : : : : Restrictions on the language satisfying Aëb U aë. : : : : : : : : Controlled Semantics for :f without uncontrollable events : : Controlled Semantics for :f with uncontrollable events : : : : An illustration of the problems with the controlled semantics for negation : : : : : : : : : : : : : : : : : : : : : : : : : : : : A problematic case involving the supervisor map assignments needed to satisfy AXèpè and f ç AëAXèpè U që. : : : : : : : : A problematic case for the semantic of the EU and AU operators. 83 x
11 4.1 Schematic of the Model Restriction Supervisor Synthesis èmrssè Algorithm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : The label state graph procedure : : : : : : : : : : : : : : : : The function label AU. : : : : : : : : : : : : : : : : : : : : : The procedure label AU next. : : : : : : : : : : : : : : : : : The procedure model synth : : : : : : : : : : : : : : : : : : Asnapshot of the ControlD graphical environment : : : : : : : ControlD edit window : : : : : : : : : : : : : : : : : : : : : : State diagram representing the movementofasingle leg. : : : Hexapod tripod gait : : : : : : : : : : : : : : : : : : : : : : : Hybrid Controller Architecture for Walking Machine : : : : : : Mini Actuator Leg Prototype 1 : : : : : : : : : : : : : : : : : The FSM model of a Leg with uncontrollable event slip : : : : Schematic representation of the walking machine : : : : : : : : Simpliæed Geometric Model of the Walking Machine : : : : : : Schematic of the tray packing line ofthe CRAMTD project : : FSM model for the CRAMTD CheckWeight Station : : : : : FSM model for the CRAMTD Rejectdiverter : : : : : : : : : Model of the measurement communication : : : : : : : : : : : ControlD window with CRAMTD constraints : : : : : : : : :142 xi
12 List of Tables 2.1 Syntax and informal Semantics for PLTL : : : : : : : : : : : : Syntax and informal Semantics for CTL : : : : : : : : : : : : : 27 xii
13 Chapter 1 Introduction A person's ëproductivity" can be measured in many ways. However, independent ofhow this productivityismeasured, it appears that the advent of modern computing devices and software architectures has improved this ability byseveral factors of magnitude 1. One ofthe aims of computer science has been to provide better software tools that would increase our productivity èand hopefully, leave us more time to engage in leisurely activities such as philosophizing or æshingè. Two desirable characteristicsofsuch tools are their ëusefulness" in solving a given problem and the ësoundness" of the theory on which they are founded. 1 In spite ofthe fact that computers have been around for decades, there seems to be a folk consensus that this result has been actually achieved only recently outside research institutions and very hightech industries ècfr. Business Week, July 17th, 1995: cover story on Wages in Americaè. 1
14 This dissertation will describe how problems in robotics and manufacturing prompted the developmentofatheory of Discrete Event Systems èdesè, how this theory has been applied and how achange in perspective leads to the construction of algorithms and tools which improveonthe original formulation. Two motivating examples form robotics and manufacturing will be used to substantiate the claims to bemade. The ærst one isanapplication to the problem of synthesizing a controller for the synchronization of leg movements ofawalking machine. The second isanapplication to the construction of control software for a food processing manufacturing line. èthe latter work has been carried out incollaboration with the DepartmentofIndustrial EngineeringofRutgers Universityè. In the walking machine example the challenge is to build a discrete controller capable of producing reasonable gaits which follow simple principles of coordination. Moreover, the system presents many problems whichhave been recently addressed as ëhybrid" between the continuous and the discrete viewpoints. Figure 1.1 shows a sample graphic output ofthe system. Chapter 5 contains a more thorough description of the example. Manufacturing systems pose very interesting problems from the viewpoint of coordination and failure detection. Figure 1.2 showsaschematicofatray packing lineofthe Combat Ration Automated Manufacturing Technology Demonstration ècramtdè of Rutgers University. Chapter 6 contains a more 2
15 Figure 1.1: The walking machine example. Spacing Conveyor B Reject Conveyor F A Seamer and Main Motor Phasing Conveyor Filling Equipment E Filling Conveyor A C D Reject Diverter I Lids Conveyor H Discharge Conveyor Checkweigher Mound Detector Figure 1.2: Schematic drawing of the tray pack line of the CRAMTD project. 3
16 detailed description of the application. The CRAMTD system is composed of manysubsystems, each of which can be represented by a ænite state model. The result ofthe controller synthesis process for such aplant is packaged in intermediate format which can be translated into avariety of ëprogrammable logic controllers" èplcè and other software tools. In this case, the problems arise from the combinatorial explosion due to the nature of the model. The dissertation is organized as follows. Chapter 2 contains an introduction that provides contexts for the developmentsdescribed later in the dissertation. It contains a brief literature review and adescription of standard tools: e.g. control theory, Ramadge and Wonham's theory of controlled discrete event systems, temporal logic, and veriæcation. Chapter 3 contains a novel interpretation of propositional temporal logic as a tool for the synthesis of controllers for discreteevent systems. Chapter 4 describes an algorithm for controller synthesis and its implementation using the ControlD toolset, and then compares it with the ëstandard" algorithm for the same problem. Chapters5and 6 contain the descriptions of the walking machineand food manufacturing examples, respectively. 4
17 Chapter 2 Manufacturing, Robotics, Control Theory and Veriæcation This thesis studies the semiautomated synthesis and veriæcation of control systems for robotics and manufacturing devices using formal methods in a discrete framework, and bears some resemblance to the theory of controlled discrete event systems ècdesè of Ramadge and Wonham's ë67ë. The aim is to study the integration of CDES theory with the techniques developed for the speciæcation and veriæcation of discrete event systems èdesè. Many of these techniques rely on the application of someæavor of temporal logic ë35ë. This chapter begins with adescription of the main concepts underlying the dissertation, followed by a review of the literature on various related subjects. It is worthwhile to note in advance that such review will necessarily be partial and incomplete, becauseofitsthe scope and the sheer amount of 5
18 material available in the literature. 2.1 Models and Control The notions of robotics and manufacturing devices or systems encompass many artifacts which are currently employed in the production and servicing activities of the economyofmanynations. Giving a precise deænition of these terms is therefore a self defeating task. Nevertheless, science and engineering have provided many abstractions and tools to model such a wide variety of systems. For the purpose of this dissertation, the robotics and manufacturing systems that will be considered, can be formally described in terms of ænite state machines èfsmè ë41ë. Such discretedescription will represent a certain ëview" of the comprehensivebehavior of the robotics or manufacturing system. I.e., it represents amodel of certain behavioral aspects ofthe device. This model is discrete because byitsnature theunderlying formal tool èfsmè is. Continuous characteristics will be best described by diæerent formal tools èe.g. ordinary diæerential equations, linear and nonlinear control theoryè. Models of robotics and manufacturing systems serve diæerent purposes. They may simply be a description of the relationships among the components of a system, to beusedasadocumentation. More often, these models serve the purpose of analyzing the system in order to either explain, predict, or control its behavior. The nature of these models depends on which ofthese 6
19 tasks èor their combinationè is to be performed. Nowadays, explanation, prediction and control are supported by combinations of hardware and software embodied by computer systems. There is therefore a focus on those modelingtools that presentdesirable computational properties such aslow algorithmic complexity. This dissertation will focus on an approach tothe control task based on a computational model whose characteristics make it relatively practical to implement while maintaining expressiveness. The basis of this computational model is the theory of CDES developed by Ramadge and Wonham in the mid 80's. This theory is deeply rooted in the classical control theory from whichit borrows terminology and key concepts. It will be shown that by dropping the traditional control theory heritage, the resulting computational model gains in expressiveness and eæciency. To substantiate these ændings and demonstrate the feasibilityofthe approach, an implementation of the algorithms has been incorporated in the ControlD tool. The tool has been used to construct the controllers of two systems: a walking machine ë12ë and amanufacturing line ë10ë. Before discussingthese results, an introduction to thekey concepts aswell as a èincompleteè review of the related èand vastè literature touching these subjects isinorder. 7
20 2.2 Classical Control Theory The main ideas used in controlling physical devices can be traced back to Maxwell's governor example which also introduced the concept of feedback control for dynamical systems. This traditional control theory approach has been extensively formalized èoriginally by Shannon, Kalman and others í see ë44, 77, 71ëè and has resulted in rich linear and nonlinear theories treating continuous laws. These theories introduce manykey concepts that will be brieæy reviewed in this section, in order to clarify their use in other parts ofthis dissertation. The following brief introduction is closely patterned after ë32, 57ë. Avery abstract view of control theory considers a system which evolves over time èt è while producing outputs èy èinresponseto inputs èuè. The outputs are also dependent onthe system state. Usually, inputs, outputs and system state are denoted with uètè, yètè, and xètè, i.e. as functions of time. The exact nature of these functions and the underlying representation of time are the basis for taxonomy of control models. The evolution of the values produced by these functions are called trajectories, orhistories, ortraces. The focus is usually on statespace trajectories H X 4 = fhx : T! Xg; and ofoutput histories H Y 4 = fhy : T! Y g: 8
21 The dependency of state xètè on the inputs is usually denoted by a state transition equation xètè =çèt; t 0 ;xèt 0 è;uètèè; or by a state evaluation equation xèt 0 è = x 0 ; x 0 ètè = fèt; xètè;uètèè: These functions are coupled with a output equation yètè =gèt; xètè; uètèè: ç, f, and g are application dependent functions. With these deænitions it is now possible to formulatethe control problem for a dynamical system by distinguishing agoal subset of the trajectories in H X. Controlling a dynamical system is equivalent to constraining it within a trajectory in the goal subset. According tothe nature of the goal subset, several æavors of the control problem can be formulated. Two classical ones are the servo problem and the stateavoidance problem. Servo Problem. Given a reference trajectory g 2 H x, deæne the goal subset as G = fh X such that jh X, gjçæ; æ é 0g: I.e. the trajectory g must be ëtracked" as closely as possible. 9
22 Stateavoidance Problem. Given a set of states Q ç X, deæne the goal subset as G = fh X such that 8t:h X ètè 62 Qg: I.e. the trajectory never reaches a state inq èprovided that x 0 62 Qè. In order to constrain the system within the bounds speciæed by the trajectory goal set G, actions must be taken. These actions are represented as inputs tothe system and are referred to ascontrol laws or control policies. Control laws are chosen given the ëcurrent" state ofthe system, or the the ëcurrent" output orboth. I.e. feedback is used in order to decide the ënext" input tothe system. Often, goal trajectories may be unattainable. Control theory deænes notions and methods to describe this situation. The key notion is that ofcon trollability of a system. A point inthe timespace hç;xi2t æ X is said to be controllable with respect to asetc ç X of target states if and only if there exists a control action æ such that ataëtime" téç the resulting point ht; x 0 i in the timespace is within C. hç;xi æ ; ht; x 0 i; and ht; x 0 i2c: A dynamical system is completely controllable if and only if it is possible to transfer any state xèt 0 è 2 X to anyother state inx in a ænite amount of ëtime". 10
23 The deænition of controllability relies on precise information about the state ofthe system. Such information may not be available. The problem of recovering such information is the observability problem of a system. Controllabilityand observability may be related by the socalled duality principle. A wide range of physical systems can be described and controlled in terms of linear, timeinvariant models. For this class of models, which are characterized by a linear matrix form of the deæning state andoutput equations, a very rich set of sophisticated mathematical tools has been developed over the years. Nonlinear dynamical systems present many more diæculties. Classical Control Theory Deæning Traits Control theory has been historically interested in continuous systems èover time T è, i.e. systems whose models are represented with the tools of diæerential equations and linear algebra. Another characteristic of control theory is the uniformity of representation. This principle applies both to basic research incontrol theory and in the practice of designing controllers for physical systems èrobotics and manufacturingè. It is common practice to represent uètè;gètè;fètè, and the control task using the same underlying formalism. This practice has been carried over to the modelingand control of systems which cannot be promptly represented withthetools of continuous mathematics. Section 2.3, contains some introductory remarks about the reformulation 11
24 of control theory for discrete event systems. 2.3 Control Theory Applied to DES While continuous control theory has been very successful, manymodern complex physical devices have proven to be not amenable to itstechniques. There are essentially two sources of problems. Many ofthese devices can be properly described only in a discrete orhybrid èi.e., mixture of discrete and continuous dynamicsè setting. The plant has to bemodeled in terms of a discrete set of states and transitions 1, which poses many problems. Secondly, the behavior desiredofthe ultimate system tends to be fairly complex. In their original work, Ramadge and Wonham ë66ë describe a reinterpretation of the key concepts ofcontrol theory for systems whose underlying dynamics is represented in terms of formal languages. The system to be controlled is considered a generator ègè of a language L èsee ë41ëè. This choice is well suited to represent the discretenature of a wide variety of systems. Ramadge and Wonham introduced the term CDES to indicate this class of systems. As a standard example, consider a machineonashop æoor. A high level 1 Theword discrete assumes two diæerent meanings in control theory_it is used toindicate a discretization of time T,orasadeæning characteristic of the underlying state space. In this dissertation, unless explicitly noted, the term ëdiscrete" always refers to system modeled with anunderlying discrete state space. 12
25 model of its operation may be given in terms of three states idle, running, faulty. The normal operation of the machineisanalternation of the ærst two states, controlled by start and stop signals. Every once in while, the machine will break down, causing the model to movetothe faulty state. In its simplest form, a CDES is deæned in terms of its generator G =èæ;s;æ;s 0 è: æisthe alphabet of the generator èsimilar to the outputs Y of a control theory speciæcationè, whose elements are called events. S is a ænite set of states èthe analogous of Xè. æ : ææs! S is the unregulated transition function, deæned in the standard way. Thestate s 0 is the initial state. The generator is also called, with anobvious analogy, the plant to be controlled. The main assumption on CDES is that the alphabet set is partitioned into two subsets. æ c ç æisasubset of controllable events, æ u =æ, æ c is a subset of uncontrollable events. This assumption is the basis of the whole CDES theory. The control law to beapplied to the plant can disable events inæ c preventing them from being generated. This disabling action constitutes the input ofthe system èin analogy to Uè. An admissible control for a CDES is a set of disabling actions and can simply be represented as a subset of æ c or as a function æ :æ c!f0; 1g: 13
26 A ç 2 æ is enabled if æèçè = 1,disabled otherwise. The set of all æ's is denoted by,. The analogue of the state equation f is the controlled state transition function æ c, which uses the deænition of æ with the extension that æ :æ u! f1g. i.e. æ c :,æ S æ æ! S: 8 é p if æèq; çè = p and æèçè = 1; æ c èæ;q;çè= é:? otherwise: By substituting æ with æ c in the deænition of G, the controlled generator G c is obtained. With these deænition it is now possible to formulate the control problem for CDES's. I.e. design a supervisor device that selects the control inputs in such a way that the given CDES behaves in obedience to various constraints Building Supervisory Controllers for CDES Theanalogy with continuous control theory is carried over in the speciæcation of a supervisor for a CDES plant G c. Since the plant isagenerator of a language èæëgëè, it is natural to assume adevice observing, orrecognizing, the events generated and producing the control inputs asneeded. Such adevice is represented as a recognizer R =èæ;r;ç;r 0 è: 14
27 hr;'i ç admissible control æ G c Figure 2.1: The standard arrangement of plant G and supervisor hr;'i. R is the recognizer set of states, ç is its transition function and r 0 is its initial state. The recognizer has an associated map ' : R!,; which represents the control law for the CDES. The pair hr;' is the supervisor for G c. ' is called the supervisor map and represents the state feedback law for the plant. The supervisor is coupled with the plant inthe standard arrangementshown in ægure 2.1. With these deænitions, Ramadge and Wonham developatheory which gives guarantees about the existence and the ëconstructibility" of supervisors. 15
28 CDES Theory: Main Results The main result ofthe theory of CDES is the existence theorem for supervisors ècfr. ë66ëè. The result is based on a notion of controllability of languages. Assumethe standard language union, intersection, concatenation and preæx closure operations described in ë41ë. If K and L are languages over an alphabet æ partitioned between controllable and uncontrollable events, then the language K is said to becontrollable if çkæ u ë L ç ç K: This condition ensures that given any sequence of events inthe preæx of K èkè, ç any subsequent uncontrollable event will not produce a behavior that a recognizer for K would fail to detect. The other main result ofthe theory concerns the structure of the family of controllable sublanguages of a given language K. This family is closed under union and intersection and contains a supremal element. The supremal controllable sublanguage of K, denoted by K ",istherefore an approximation to K. The existence of this supremal element isthe key to the construction of the actual supervisor for a wide variety of systems. A Pragmatic Methodology The existence of the supremal controllable sublanguage of a given language K, implies a procedure for the construction of a supervisor for a given system. 16
29 Start Build Model of System Language L Build Model of "Desired Behavior" Language K yes no Is K Controllable? no Is K acceptable? yes Build approximation K yes Is K acceptable? no Done Figure 2.2: Schematic æowchart of the Supervisor building steps. The supervisor hr;'i realizes the control law for a given set of constraints. These constraints are expressed in terms of a language K. Ifthis language is not controllable, then its approximation K " can be built. This leads to a pragmatic procedure for the construction of supervisors èsee Figure 2.2è. Such a procedure is not an algorithm directly implementable. It is a guideline for a practitioner employing these notions to actually build a controller. In particular, the step checking for the ëgoodness" of the controlled behavior is completely up to the human. Ramadge and Wonham take this fact into account intheir theory by introducing the notion of ëacceptable" language ècfr. ë66ëè. The procedure is the basis for the design of a software environment for 17
30 the synthesis of discrete controllers for CDES's. In ë13ë a system is described that performs this ëaiding" task. The process of building a supervisor proceeds in two steps: ærst a supremal sublanguage for the speciæed K is built, then the actual supervisor map ' is synthesized. There are restrictions to the kind oflanguages that can be eæectively used with this procedure. The theory works well for regular languages. For larger classes of languages èe.g. contextfreeè the uniqueness of K " cannot be guaranteed. These results are contained in ë64ë. In the same paper, one can ænd an algorithm for the construction of the supremal controllable sublanguage of a K " with respect to a preæx closed language æëgë. This algorithm assumes an FSM representation of G and R and produces the desired automata in OèjSj; jrjè, where jsj is the cardinality of the set of states of the generator G and jrj is the cardinality ofthe set of states of the recognizer R. The synthesis algorithm proposed in this dissertation improves slightly on this result. Though the overall asymptotic complexityofthe proposed synthesis algorithm does not change, it will be argued that the diæerent representation used can signiæcantly improvethe practicalityofthe resulting tool. Other Developments in Ramadge and Wonham's Theory The part of the theory of CDES's described so far is only the basis which constitutes the background for the development of this dissertation. An overview 18
31 of other developmentsinthe theory can be found in ë67ë. The other topics which have been investigated in the CDES framework regard observability and modular synthesis of supervisors ë63ë. The issue of decentralized supervision has been also investigated ë68ë. In this case the question asked is whether the action of several supervisors acting locally can achieve the same eæect as a centralized one. Another research direction concerns what kind of extensions can be introduced in the CDES framework preserving its basic characteristics. A crucial question concerns what classes of languages besides the regular ones admit the unique supremal sublanguage construction or what are the characteristics of other extensions to the basic model. For instance,!languages èand Bíuchi automataè as a basis for CDES are considered in ë65ë, and Vector Addition Systems are considered in ë47, 48ë. The basic CDES model does not include time as a component. The only notion of the passage of timeisderived from the generation of events bythe plant. In this direction, a standard extension to the basic CDES model with discrete time ticks is presented in ë18ë. On the foundation of CDES, other extensions and reinterpretation of the theory have been proposed, mainly with the aim to reuse the wealth of knowledge developed within the temporal logic community. This dissertation falls in this last category. Additional references will be given in Section
Software Modeling and Verification
Software Modeling and Verification Alessandro Aldini DiSBeF  Sezione STI University of Urbino Carlo Bo Italy 34 February 2015 Algorithmic verification Correctness problem Is the software/hardware system
More informationTesting LTL Formula Translation into Büchi Automata
Testing LTL Formula Translation into Büchi Automata Heikki Tauriainen and Keijo Heljanko Helsinki University of Technology, Laboratory for Theoretical Computer Science, P. O. Box 5400, FIN02015 HUT, Finland
More informationFor example, estimate the population of the United States as 3 times 10⁸ and the
CCSS: Mathematics The Number System CCSS: Grade 8 8.NS.A. Know that there are numbers that are not rational, and approximate them by rational numbers. 8.NS.A.1. Understand informally that every number
More informationModel Checking II Temporal Logic Model Checking
1/32 Model Checking II Temporal Logic Model Checking Edmund M Clarke, Jr School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 2/32 Temporal Logic Model Checking Specification Language:
More informationMathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson
Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement
More informationCHAPTER 7 GENERAL PROOF SYSTEMS
CHAPTER 7 GENERAL PROOF SYSTEMS 1 Introduction Proof systems are built to prove statements. They can be thought as an inference machine with special statements, called provable statements, or sometimes
More informationCS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 RealTime Systems. CSCI 522 High Performance Computing
CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate
More informationThe ER èentityrelationshipè data model views the real world as a set of basic objects èentitiesè and
CMPT354Han95.3 Lecture Notes September 20, 1995 Chapter 2 The EntityRelationship Model The ER èentityrelationshipè data model views the real world as a set of basic objects èentitiesè and relationships
More informationCreating, Solving, and Graphing Systems of Linear Equations and Linear Inequalities
Algebra 1, Quarter 2, Unit 2.1 Creating, Solving, and Graphing Systems of Linear Equations and Linear Inequalities Overview Number of instructional days: 15 (1 day = 45 60 minutes) Content to be learned
More informationModel Checking: An Introduction
Announcements Model Checking: An Introduction Meeting 2 Office hours M 1:30pm2:30pm W 5:30pm6:30pm (after class) and by appointment ECOT 621 Moodle problems? Fundamentals of Programming Languages CSCI
More informationAlgorithmic Software Verification
Algorithmic Software Verification (LTL Model Checking) Azadeh Farzan What is Verification Anyway? Proving (in a formal way) that program satisfies a specification written in a logical language. Formal
More informationUPDATES OF LOGIC PROGRAMS
Computing and Informatics, Vol. 20, 2001,????, V 2006Nov6 UPDATES OF LOGIC PROGRAMS Ján Šefránek Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University,
More informationA Logic Approach for LTL System Modification
A Logic Approach for LTL System Modification Yulin Ding and Yan Zhang School of Computing & Information Technology University of Western Sydney Kingswood, N.S.W. 1797, Australia email: {yding,yan}@cit.uws.edu.au
More informationFrom Workflow Design Patterns to Logical Specifications
AUTOMATYKA/ AUTOMATICS 2013 Vol. 17 No. 1 http://dx.doi.org/10.7494/automat.2013.17.1.59 Rados³aw Klimek* From Workflow Design Patterns to Logical Specifications 1. Introduction Formal methods in software
More informationRegular Languages and Finite Automata
Regular Languages and Finite Automata 1 Introduction Hing Leung Department of Computer Science New Mexico State University Sep 16, 2010 In 1943, McCulloch and Pitts [4] published a pioneering work on a
More informationSpecification and Analysis of Contracts Lecture 1 Introduction
Specification and Analysis of Contracts Lecture 1 Introduction Gerardo Schneider gerardo@ifi.uio.no http://folk.uio.no/gerardo/ Department of Informatics, University of Oslo SEFM School, Oct. 27  Nov.
More informationDevelopment of dynamically evolving and selfadaptive software. 1. Background
Development of dynamically evolving and selfadaptive software 1. Background LASER 2013 Isola d Elba, September 2013 Carlo Ghezzi Politecnico di Milano DeepSE Group @ DEIB 1 Requirements Functional requirements
More information! " # The Logic of Descriptions. Logics for Data and Knowledge Representation. Terminology. Overview. Three Basic Features. Some History on DLs
,!0((,.+#$),%$(&.& *,2($)%&2.'3&%!&, Logics for Data and Knowledge Representation Alessandro Agostini agostini@dit.unitn.it University of Trento Fausto Giunchiglia fausto@dit.unitn.it The Logic of Descriptions!$%&'()*$#)
More informationPolynomial Operations and Factoring
Algebra 1, Quarter 4, Unit 4.1 Polynomial Operations and Factoring Overview Number of instructional days: 15 (1 day = 45 60 minutes) Content to be learned Identify terms, coefficients, and degree of polynomials.
More informationReliability Guarantees in Automata Based Scheduling for Embedded Control Software
1 Reliability Guarantees in Automata Based Scheduling for Embedded Control Software Santhosh Prabhu, Aritra Hazra, Pallab Dasgupta Department of CSE, IIT Kharagpur West Bengal, India  721302. Email: {santhosh.prabhu,
More informationThe University of Jordan
The University of Jordan Master in Web Intelligence Non Thesis Department of Business Information Technology King Abdullah II School for Information Technology The University of Jordan 1 STUDY PLAN MASTER'S
More informationReading 13 : Finite State Automata and Regular Expressions
CS/Math 24: Introduction to Discrete Mathematics Fall 25 Reading 3 : Finite State Automata and Regular Expressions Instructors: Beck Hasti, Gautam Prakriya In this reading we study a mathematical model
More informationSECOND YEAR. Major Subject 3 Thesis (EE 300) 3 Thesis (EE 300) 3 TOTAL 3 TOTAL 6. MASTER OF ENGINEERING IN ELECTRICAL ENGINEERING (MEng EE) FIRST YEAR
MASTER OF SCIENCE IN ELECTRICAL ENGINEERING (MS EE) FIRST YEAR Elective 3 Elective 3 Elective 3 Seminar Course (EE 296) 1 TOTAL 12 TOTAL 10 SECOND YEAR Major Subject 3 Thesis (EE 300) 3 Thesis (EE 300)
More informationThe program also provides supplemental modules on topics in geometry and probability and statistics.
Algebra 1 Course Overview Students develop algebraic fluency by learning the skills needed to solve equations and perform important manipulations with numbers, variables, equations, and inequalities. Students
More informationThe Basics of Graphical Models
The Basics of Graphical Models David M. Blei Columbia University October 3, 2015 Introduction These notes follow Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. Many figures
More informationTemporal Logics. Computation Tree Logic
Temporal Logics CTL: definition, relationship between operators, adequate sets, specifying properties, safety/liveness/fairness Modeling: sequential, concurrent systems; maximum parallelism/interleaving
More informationMEng, BSc Computer Science with Artificial Intelligence
School of Computing FACULTY OF ENGINEERING MEng, BSc Computer Science with Artificial Intelligence Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give
More informationFormal Languages and Automata Theory  Regular Expressions and Finite Automata 
Formal Languages and Automata Theory  Regular Expressions and Finite Automata  Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March
More informationCorrespondence analysis for strong threevalued logic
Correspondence analysis for strong threevalued logic A. Tamminga abstract. I apply Kooi and Tamminga s (2012) idea of correspondence analysis for manyvalued logics to strong threevalued logic (K 3 ).
More informationlogic language, static/dynamic models SAT solvers Verified Software Systems 1 How can we model check of a program or system?
5. LTL, CTL Last part: Alloy logic language, static/dynamic models SAT solvers Today: Temporal Logic (LTL, CTL) Verified Software Systems 1 Overview How can we model check of a program or system? Modeling
More informationPerformance Level Descriptors Grade 6 Mathematics
Performance Level Descriptors Grade 6 Mathematics Multiplying and Dividing with Fractions 6.NS.12 Grade 6 Math : SubClaim A The student solves problems involving the Major Content for grade/course with
More informationPA Common Core Standards Standards for Mathematical Practice Grade Level Emphasis*
Habits of Mind of a Productive Thinker Make sense of problems and persevere in solving them. Attend to precision. PA Common Core Standards The Pennsylvania Common Core Standards cannot be viewed and addressed
More informationA Framework for the Semantics of Behavioral Contracts
A Framework for the Semantics of Behavioral Contracts Ashley McNeile Metamaxim Ltd, 48 Brunswick Gardens, London W8 4AN, UK ashley.mcneile@metamaxim.com Abstract. Contracts have proved a powerful concept
More informationT79.186 Reactive Systems: Introduction and Finite State Automata
T79.186 Reactive Systems: Introduction and Finite State Automata Timo Latvala 14.1.2004 Reactive Systems: Introduction and Finite State Automata 11 Reactive Systems Reactive systems are a class of software
More informationIntroducing Formal Methods. Software Engineering and Formal Methods
Introducing Formal Methods Formal Methods for Software Specification and Analysis: An Overview 1 Software Engineering and Formal Methods Every Software engineering methodology is based on a recommended
More informationFundamental Computer Science Concepts Sequence TCSU CSCI SEQ A
Fundamental Computer Science Concepts Sequence TCSU CSCI SEQ A A. Description Introduction to the discipline of computer science; covers the material traditionally found in courses that introduce problem
More informationFrom Control Loops to Software
CNRSVERIMAG Grenoble, France October 2006 Executive Summary Embedded systems realization of control systems by computers Computers are the major medium for realizing controllers There is a gap between
More informationWHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT?
WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT? introduction Many students seem to have trouble with the notion of a mathematical proof. People that come to a course like Math 216, who certainly
More informationSouth Carolina College and CareerReady (SCCCR) Algebra 1
South Carolina College and CareerReady (SCCCR) Algebra 1 South Carolina College and CareerReady Mathematical Process Standards The South Carolina College and CareerReady (SCCCR) Mathematical Process
More informationMEng, BSc Applied Computer Science
School of Computing FACULTY OF ENGINEERING MEng, BSc Applied Computer Science Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give a machine instructions
More information[Refer Slide Time: 05:10]
Principles of Programming Languages Prof: S. Arun Kumar Department of Computer Science and Engineering Indian Institute of Technology Delhi Lecture no 7 Lecture Title: Syntactic Classes Welcome to lecture
More informationEvaluation Tool for Assessment Instrument Quality
REPRODUCIBLE Figure 4.4: Evaluation Tool for Assessment Instrument Quality Assessment indicators Description of Level 1 of the Indicator Are Not Present Limited of This Indicator Are Present Substantially
More information#1 Make sense of problems and persevere in solving them.
#1 Make sense of problems and persevere in solving them. 1. Make sense of problems and persevere in solving them. Interpret and make meaning of the problem looking for starting points. Analyze what is
More informationElementary Number Theory We begin with a bit of elementary number theory, which is concerned
CONSTRUCTION OF THE FINITE FIELDS Z p S. R. DOTY Elementary Number Theory We begin with a bit of elementary number theory, which is concerned solely with questions about the set of integers Z = {0, ±1,
More information3.4 Complex Zeros and the Fundamental Theorem of Algebra
86 Polynomial Functions.4 Complex Zeros and the Fundamental Theorem of Algebra In Section., we were focused on finding the real zeros of a polynomial function. In this section, we expand our horizons and
More informationTHE TURING DEGREES AND THEIR LACK OF LINEAR ORDER
THE TURING DEGREES AND THEIR LACK OF LINEAR ORDER JASPER DEANTONIO Abstract. This paper is a study of the Turing Degrees, which are levels of incomputability naturally arising from sets of natural numbers.
More informationDRAFT. Algebra 1 EOC Item Specifications
DRAFT Algebra 1 EOC Item Specifications The draft Florida Standards Assessment (FSA) Test Item Specifications (Specifications) are based upon the Florida Standards and the Florida Course Descriptions as
More informationCHAPTER 1 INTRODUCTION
1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful
More information6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch
6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 4 Nancy Lynch Today Two more models of computation: Nondeterministic Finite Automata (NFAs)
More informationLinear Codes. Chapter 3. 3.1 Basics
Chapter 3 Linear Codes In order to define codes that we can encode and decode efficiently, we add more structure to the codespace. We shall be mainly interested in linear codes. A linear code of length
More informationGeorgia Standards of Excellence 20152016 Mathematics
Georgia Standards of Excellence 20152016 Mathematics Standards GSE Coordinate Algebra K12 Mathematics Introduction Georgia Mathematics focuses on actively engaging the student in the development of mathematical
More informationMATHEMATICS (CLASSES XI XII)
MATHEMATICS (CLASSES XI XII) General Guidelines (i) All concepts/identities must be illustrated by situational examples. (ii) The language of word problems must be clear, simple and unambiguous. (iii)
More informationMaster s Program in Information Systems
The University of Jordan King Abdullah II School for Information Technology Department of Information Systems Master s Program in Information Systems 2006/2007 Study Plan Master Degree in Information Systems
More informationPRACTICE BOOK COMPUTER SCIENCE TEST. Graduate Record Examinations. This practice book contains. Become familiar with. Visit GRE Online at www.gre.
This book is provided FREE with test registration by the Graduate Record Examinations Board. Graduate Record Examinations This practice book contains one actual fulllength GRE Computer Science Test testtaking
More informationA Propositional Dynamic Logic for CCS Programs
A Propositional Dynamic Logic for CCS Programs Mario R. F. Benevides and L. Menasché Schechter {mario,luis}@cos.ufrj.br Abstract This work presents a Propositional Dynamic Logic in which the programs are
More information1.1 Logical Form and Logical Equivalence 1
Contents Chapter I The Logic of Compound Statements 1.1 Logical Form and Logical Equivalence 1 Identifying logical form; Statements; Logical connectives: not, and, and or; Translation to and from symbolic
More informationELLIPTIC CURVES AND LENSTRA S FACTORIZATION ALGORITHM
ELLIPTIC CURVES AND LENSTRA S FACTORIZATION ALGORITHM DANIEL PARKER Abstract. This paper provides a foundation for understanding Lenstra s Elliptic Curve Algorithm for factoring large numbers. We give
More informationSimulationBased Security with Inexhaustible Interactive Turing Machines
SimulationBased Security with Inexhaustible Interactive Turing Machines Ralf Küsters Institut für Informatik ChristianAlbrechtsUniversität zu Kiel 24098 Kiel, Germany kuesters@ti.informatik.unikiel.de
More informationA Comparison of System Dynamics (SD) and Discrete Event Simulation (DES) Al Sweetser Overview.
A Comparison of System Dynamics (SD) and Discrete Event Simulation (DES) Al Sweetser Andersen Consultng 1600 K Street, N.W., Washington, DC 200062873 (202) 8628080 (voice), (202) 7854689 (fax) albert.sweetser@ac.com
More informationLow Level. Software. Solution. extensions to handle. coarse grained task. compilers with. Data parallel. parallelism.
. 1 History 2 æ 1960s  First Organized Collections Problem Solving Environments for Parallel Scientiæc Computation Jack Dongarra Univ. of Tenn.èOak Ridge National Lab dongarra@cs.utk.edu æ 1970s  Advent
More informationparent ROADMAP MATHEMATICS SUPPORTING YOUR CHILD IN HIGH SCHOOL
parent ROADMAP MATHEMATICS SUPPORTING YOUR CHILD IN HIGH SCHOOL HS America s schools are working to provide higher quality instruction than ever before. The way we taught students in the past simply does
More informationStatic Program Transformations for Efficient Software Model Checking
Static Program Transformations for Efficient Software Model Checking Shobha Vasudevan Jacob Abraham The University of Texas at Austin Dependable Systems Large and complex systems Software faults are major
More informationInteger Operations. Overview. Grade 7 Mathematics, Quarter 1, Unit 1.1. Number of Instructional Days: 15 (1 day = 45 minutes) Essential Questions
Grade 7 Mathematics, Quarter 1, Unit 1.1 Integer Operations Overview Number of Instructional Days: 15 (1 day = 45 minutes) Content to Be Learned Describe situations in which opposites combine to make zero.
More informationFormal Verification and Lineartime Model Checking
Formal Verification and Lineartime Model Checking Paul Jackson University of Edinburgh Automated Reasoning 21st and 24th October 2013 Why Automated Reasoning? Intellectually stimulating and challenging
More informationWhat Is School Mathematics?
What Is School Mathematics? Lisbon, Portugal January 30, 2010 H. Wu *I am grateful to Alexandra AlvesRodrigues for her many contributions that helped shape this document. The German conductor Herbert
More informationVerifying Semantic of System Composition for an AspectOriented Approach
2012 International Conference on System Engineering and Modeling (ICSEM 2012) IPCSIT vol. 34 (2012) (2012) IACSIT Press, Singapore Verifying Semantic of System Composition for an AspectOriented Approach
More informationEastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students
Eastern Washington University Department of Computer Science Questionnaire for Prospective Masters in Computer Science Students I. Personal Information Name: Last First M.I. Mailing Address: Permanent
More informationELECTRICAL ENGINEERING
EE ELECTRICAL ENGINEERING See beginning of Section H for abbreviations, course numbers and coding. The * denotes labs which are held on alternate weeks. A minimum grade of C is required for all prerequisite
More informationOverview of Math Standards
Algebra 2 Welcome to math curriculum design maps for Manhattan Ogden USD 383, striving to produce learners who are: Effective Communicators who clearly express ideas and effectively communicate with diverse
More informationSoftware Verification and Testing. Lecture Notes: Temporal Logics
Software Verification and Testing Lecture Notes: Temporal Logics Motivation traditional programs (whether terminating or nonterminating) can be modelled as relations are analysed wrt their input/output
More informationElements of Abstract Group Theory
Chapter 2 Elements of Abstract Group Theory Mathematics is a game played according to certain simple rules with meaningless marks on paper. David Hilbert The importance of symmetry in physics, and for
More informationEntry Level College Mathematics: Algebra or Modeling
Entry Level College Mathematics: Algebra or Modeling Dan Kalman Dan Kalman is Associate Professor in Mathematics and Statistics at American University. His interests include matrix theory, curriculum development,
More informationIndiana University East Faculty Senate
Indiana University East Faculty Senate General Education Curriculum for Baccalaureate Degree Programs at Indiana University East The purpose of the General Education Curriculum is to ensure that every
More informationCORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREERREADY FOUNDATIONS IN ALGEBRA
We Can Early Learning Curriculum PreK Grades 8 12 INSIDE ALGEBRA, GRADES 8 12 CORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREERREADY FOUNDATIONS IN ALGEBRA April 2016 www.voyagersopris.com Mathematical
More informationMA651 Topology. Lecture 6. Separation Axioms.
MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples
More informationParametric Domaintheoretic models of Linear Abadi & Plotkin Logic
Parametric Domaintheoretic models of Linear Abadi & Plotkin Logic Lars Birkedal Rasmus Ejlers Møgelberg Rasmus Lerchedahl Petersen IT University Technical Report Series TR007 ISSN 600 600 February 00
More information136 CHAPTER 4. INDUCTION, GRAPHS AND TREES
136 TER 4. INDUCTION, GRHS ND TREES 4.3 Graphs In this chapter we introduce a fundamental structural idea of discrete mathematics, that of a graph. Many situations in the applications of discrete mathematics
More informationReasons for need for Computer Engineering program From Computer Engineering Program proposal
Reasons for need for Computer Engineering program From Computer Engineering Program proposal Department of Computer Science School of Electrical Engineering & Computer Science circa 1988 Dedicated to David
More informationPowerTeaching i3: Algebra I Mathematics
PowerTeaching i3: Algebra I Mathematics Alignment to the Common Core State Standards for Mathematics Standards for Mathematical Practice and Standards for Mathematical Content for Algebra I Key Ideas and
More informationQuotient Rings and Field Extensions
Chapter 5 Quotient Rings and Field Extensions In this chapter we describe a method for producing field extension of a given field. If F is a field, then a field extension is a field K that contains F.
More informationLikewise, we have contradictions: formulas that can only be false, e.g. (p p).
CHAPTER 4. STATEMENT LOGIC 59 The rightmost column of this truth table contains instances of T and instances of F. Notice that there are no degrees of contingency. If both values are possible, the formula
More informationSoftware Verification: InfiniteState Model Checking and Static Program
Software Verification: InfiniteState Model Checking and Static Program Analysis Dagstuhl Seminar 06081 February 19 24, 2006 Parosh Abdulla 1, Ahmed Bouajjani 2, and Markus MüllerOlm 3 1 Uppsala Universitet,
More informationCS 3719 (Theory of Computation and Algorithms) Lecture 4
CS 3719 (Theory of Computation and Algorithms) Lecture 4 Antonina Kolokolova January 18, 2012 1 Undecidable languages 1.1 ChurchTuring thesis Let s recap how it all started. In 1990, Hilbert stated a
More informationFormal Verification of Software
Formal Verification of Software Sabine Broda Department of Computer Science/FCUP 12 de Novembro de 2014 Sabine Broda (DCCFCUP) Formal Verification of Software 12 de Novembro de 2014 1 / 26 Formal Verification
More informationModel Checking of Software
Model Checking of Software Patrice Godefroid Bell Laboratories, Lucent Technologies SpecNCheck Page 1 August 2001 A Brief History of Model Checking Prehistory: transformational programs and theorem proving
More informationAlgebra Unpacked Content For the new Common Core standards that will be effective in all North Carolina schools in the 201213 school year.
This document is designed to help North Carolina educators teach the Common Core (Standard Course of Study). NCDPI staff are continually updating and improving these tools to better serve teachers. Algebra
More informationCloud Computing is NPComplete
Working Paper, February 2, 20 Joe Weinman Permalink: http://www.joeweinman.com/resources/joe_weinman_cloud_computing_is_npcomplete.pdf Abstract Cloud computing is a rapidly emerging paradigm for computing,
More information2.2. Instantaneous Velocity
2.2. Instantaneous Velocity toc Assuming that your are not familiar with the technical aspects of this section, when you think about it, your knowledge of velocity is limited. In terms of your own mathematical
More informationStructure of Presentation. The Role of Programming in Informatics Curricula. Concepts of Informatics 2. Concepts of Informatics 1
The Role of Programming in Informatics Curricula A. J. Cowling Department of Computer Science University of Sheffield Structure of Presentation Introduction The problem, and the key concepts. Dimensions
More informationManaging Variability in Software Architectures 1 Felix Bachmann*
Managing Variability in Software Architectures Felix Bachmann* Carnegie Bosch Institute Carnegie Mellon University Pittsburgh, Pa 523, USA fb@sei.cmu.edu Len Bass Software Engineering Institute Carnegie
More informationThis unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions.
Algebra I Overview View unit yearlong overview here Many of the concepts presented in Algebra I are progressions of concepts that were introduced in grades 6 through 8. The content presented in this course
More informationDegrees of Truth: the formal logic of classical and quantum probabilities as well as fuzzy sets.
Degrees of Truth: the formal logic of classical and quantum probabilities as well as fuzzy sets. Logic is the study of reasoning. A language of propositions is fundamental to this study as well as true
More informationRandom vs. StructureBased Testing of AnswerSet Programs: An Experimental Comparison
Random vs. StructureBased Testing of AnswerSet Programs: An Experimental Comparison Tomi Janhunen 1, Ilkka Niemelä 1, Johannes Oetsch 2, Jörg Pührer 2, and Hans Tompits 2 1 Aalto University, Department
More informationOverview. Essential Questions. Precalculus, Quarter 4, Unit 4.5 Build Arithmetic and Geometric Sequences and Series
Sequences and Series Overview Number of instruction days: 4 6 (1 day = 53 minutes) Content to Be Learned Write arithmetic and geometric sequences both recursively and with an explicit formula, use them
More informationSome Polynomial Theorems. John Kennedy Mathematics Department Santa Monica College 1900 Pico Blvd. Santa Monica, CA 90405 rkennedy@ix.netcom.
Some Polynomial Theorems by John Kennedy Mathematics Department Santa Monica College 1900 Pico Blvd. Santa Monica, CA 90405 rkennedy@ix.netcom.com This paper contains a collection of 31 theorems, lemmas,
More informationVilnius University. Faculty of Mathematics and Informatics. Gintautas Bareikis
Vilnius University Faculty of Mathematics and Informatics Gintautas Bareikis CONTENT Chapter 1. SIMPLE AND COMPOUND INTEREST 1.1 Simple interest......................................................................
More informationRecurrent Neural Networks
Recurrent Neural Networks Neural Computation : Lecture 12 John A. Bullinaria, 2015 1. Recurrent Neural Network Architectures 2. State Space Models and Dynamical Systems 3. Backpropagation Through Time
More information4: SINGLEPERIOD MARKET MODELS
4: SINGLEPERIOD MARKET MODELS Ben Goldys and Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2015 B. Goldys and M. Rutkowski (USydney) Slides 4: SinglePeriod Market
More informationStandards for Mathematical Practice: Commentary and Elaborations for 6 8
Standards for Mathematical Practice: Commentary and Elaborations for 6 8 c Illustrative Mathematics 6 May 2014 Suggested citation: Illustrative Mathematics. (2014, May 6). Standards for Mathematical Practice:
More informationNotes on Complexity Theory Last updated: August, 2011. Lecture 1
Notes on Complexity Theory Last updated: August, 2011 Jonathan Katz Lecture 1 1 Turing Machines I assume that most students have encountered Turing machines before. (Students who have not may want to look
More information