Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

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An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Considers the deadlock avoidance problem for the class of conjunctive/disjunctive (sequential) resource allocation systems (C/D-RAS), which allows for multiple resource acquisitions and flexible routings. First, a siphon-based characterization for the liveness of Petri nets (PNs) modeling C/D-RAS is developed, and subsequently, this characterization facilitates the development of a polynomial-complexity deadlock avoidance policy (DAP) that is appropriate for the considered RAS class. The resulting policy is characterized as C/D-RUN. The last part of the paper exploits the aforementioned siphon-based characterization of C/D-RAS liveness, in order to develop a sufficiency condition for C/D-RAS liveness that takes the convenient form of a mixed integer programming (MIP) formulation. The availability of this MIP formulation subsequently allows the "automatic" correctness verification of any tentative C/D-RAS DAP for which the controlled system behavior remains in the class of PNs modeling C/D-RAS, and the effective flexibility enhancement of the aforementioned C/D-RUN DAP implementations. Finally, we notice that, in addition to extending and complementing the current theory on deadlock-free sequential resource allocation to the most powerful class of C/D-RAS, the presented results also (i) nontrivially generalize important concepts and techniques of ordinary PN structural analysis to the broader class of nonordinary PNs, while (ii) from a practical standpoint, they can find direct application in the (work-) flow management of modern production, service and/or transportation environments.

http://www.isye.gatech.edu/~spyros/publications/cor-TAC01.pdf

Deadlock avoidance in sequential resource allocation systems with multiple resource acquisitions and flexible routings

The development of efficient deadlock avoidance policies (DAPs) for sequential resource allocation systems (RASs) is a problem of increasing interest in the scientific community, largely because of its relevance to the design of large-scale flexibly automated manufacturing systems. Much of the work on this problem existing in the literature is focused on the so-called single-unit RAS model, which is the simplest model in the considered class of RASs. Furthermore, due to a well-established result stating that, even for single-unit RASs, the computation of the maximally permissive DAP is intractable (NP-hard), many researchers (including our group) have focused on obtaining good suboptimal policies which are computationally tractable (scalable) and provably correct. In the first part of the paper, it is shown, however, that for a large subset (in fact, a majority) of single-unit RASs, the optimal DAP can be obtained in real-time with a computational cost which is a polynomial function of the system size (i.e., the number of resource types and the distinct route stages of the processes running through the system). The implications of this result for the entire class of single-unit RASs are also explored. With a result on the design of optimal DAPs for single-unit RASs, the second part of the paper concentrates on the development of scalable and provably correct DAPs for the more general case of conjunctive RASs.

Polynomial-complexity deadlock avoidance policies for sequential resource allocation systems

Currently, conflict-free routing in AGV systems is established by means of one of the following three approaches: (i) the problem elimination through the adoption of a segmented path flow or tandem queue configuration; (ii) the identification of imminent collisions through forward sensing and their aversion through vehicle backtracking and/or rerouting; or (iii) the imposition of zone control and extensive route pre-planning, typically based on deterministic timing of the vehicle traveling and docking stages. Among these three approaches, the segmented path flow-based approach presents the highest robustness to the system stochasticities/randomness, but at the cost of restricted vehicle routings and the need for complicated handling operations. This paper proposes an alternative conflict resolution strategy that will ensure robust AGV conflict resolution, while maintaining the operational flexibility provided by free vehicle travel on arbitrarily structured guidepath networks. Specifically, the approach advocated in this paper also employs zone control, but it determines vehicle routes incrementally, one zone at a time. Routing decisions are the result of a sequence of safety and performance considerations, with the former being primarily based on structural/logical rather than timing aspects of the system behavior. The resulting control problem is characterized as the AGV structural control. After defining the notion of AGV structural control, the paper proceeds to the formal characterization and analysis of the problem, and to the development of a structural control policy appropriate for the class of AGV resource allocation systems. The paper concludes with some discussion on the accommodation of emerging AGV operational features in the proposed modeling and analysis framework, and the integration of AGV structural control with the broader control of material-flow among the shop-floor workstations.

https://link.springer.com/content/pdf/10.1023/A:1007663100796.pdf

Conflict resolution in AGV systems

This paper suggests a framework for developing provably correct and scalable avoidance policies for the automated manufacturing cell (AMC) model. We provide a formal description of the AMC operation and establish its correspondence to a finite state automaton (FSA). The AMC model's deadlock characteristics are studied with the aid of the FSA state transition diagram. We provide a new characterization of AMC state safety which serves as the basis of a new framework for systematic development of avoidance policies. We provide two examples of the framework functionality. First, the framework is used to formally prove the correctness of the resource upstream neighborhood (RUN) avoidance policy (Gaarder, 1993). Then, it is used to provide an alternative proof for the correctness of B-K deadlock avoidance algorithm (BKDAA) (Banaszak et al., 1990). Furthermore, an interesting relationship existing between RUN and BKDAA policies is brought out. Finally, the results are summarized, conclusions drawn, and possible extensions and generalizations of the underlying ideas discussed.

/pdf/deadlock-avoidance-policies-for-automated-manufacturing-1poz39zqk2.pdf

Deadlock avoidance policies for automated manufacturing cells

Configuration flexibility and deadlock-free operation are two essential properties of control systems for highly automated flexible manufacturing systems. Configuration flexibility, the ability to quickly modify manufacturing system components and their logical relationships, requires automatic generation of control executables from high level system specifications. These control executables must guarantee deadlock-free operation. The resource order policy is a configurable controller that provides the deadlock-free guarantee for buffer space allocation. It uses a total ordering of system machines and routing information to generate a set of configuration specific linear constraints. These constraints encode the system state along with a buffer capacity function and define a deadlock-free region of operation. Constraint generation and execution are of polynomial complexity.

Spyros Reveliotis

Papers

Deadlock avoidance in sequential resource allocation systems with multiple resource acquisitions and flexible routings

Polynomial-complexity deadlock avoidance policies for sequential resource allocation systems

Conflict resolution in AGV systems

Deadlock avoidance policies for automated manufacturing cells

A correct and scalable deadlock avoidance policy for flexible manufacturing systems