Generative Marginalization Models

We introduce marginalization models (MaMs), a new family of generative models for high-dimensional discrete data. They offer scalable and flexible generative modeling with tractable likelihoods by explicitly modeling all induced marginal distributions. Marginalization models enable fast evaluation of arbitrary marginal probabilities with a single forward pass of the neural network, which overcomes a major limitation of methods with exact marginal inference, such as autoregressive models (ARMs). We propose scalable methods for learning the marginals, grounded in the concept of "marginalization self-consistency". Unlike previous methods, MaMs support scalable training of any-order generative models for high-dimensional problems under the setting of energy-based training, where the goal is to match the learned distribution to a given desired probability (specified by an unnormalized (log) probability function such as energy function or reward function). We demonstrate the effectiveness of the proposed model on a variety of discrete data distributions, including binary images, language, physical systems, and molecules, for maximum likelihood and energy-based training settings. MaMs achieve orders of magnitude speedup in evaluating the marginal probabilities on both settings. For energy-based training tasks, MaMs enable any-order generative modeling of high-dimensional problems beyond the capability of previous methods. Code is at https://github.com/PrincetonLIPS/MaM

Communicating Processes with Data for Supervisory Coordination

We employ supervisory controllers to safely coordinate high-level discrete(-event) behavior of distributed components of complex systems. Supervisory controllers observe discrete-event system behavior, make a decision on allowed activities, and communicate the control signals to the involved parties. Models of the supervisory controllers can be automatically synthesized based on formal models of the system components and a formalization of the safe coordination (control) requirements. Based on the obtained models, code generation can be used to implement the supervisory controllers in software, on a PLC, or an embedded (micro)processor. In this article, we develop a process theory with data that supports a model-based systems engineering framework for supervisory coordination. We employ communication to distinguish between the different flows of information, i.e., observation and supervision, whereas we employ data to specify the coordination requirements more compactly, and to increase the expressivity of the framework. To illustrate the framework, we remodel an industrial case study involving coordination of maintenance procedures of a printing process of a high-tech Oce printer.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

Centralized and distributed algorithms for on-line synthesis of maximal control policies under partial observation

This paper deals with the on-line control of partially observed discrete event systems (DES). The goal is to restrict the behavior of the system within a prefix-closed legal language while accounting for the presence of uncontrollable and unobservable events. In the spirit of recent work on the on-line control of partially observed DES (Heymann and Lin 1994) and on variable lookahead control of fully observed DES (Ben Hadj-Alouane et al. 1994c), we propose an approach where, following each observable event, a control action is computed on-line using an algorithm of linear worst-case complexity. This algorithm, called VLP-PO , has the following additional properties: (i) the resulting behavior is guaranteed to be a maximal controllable and observable sublanguage of the legal language; (ii) different maximals may be generated by varying the priorities assigned to the controllable events, a parameter of VLP-PO ; (iii) a maximal containing the supremal controllable and normal sublanguage of the legal language can be generated by a proper selection of controllable event priorities; and (iv) no off-line calculations are necessary. We also present a parallel/distributed version of the VLP-PO algorithm called DI-VLP-PO . This version uses several communicating agents that simultaneously run (on-line) identical versions of the algorithm but on possibly different parts of the system model and the legal language, according to the structural properties of the system and the specifications. While achieving the same behavior as VLO-PO, DI-VLP-PO runs at a total complexity (for computation and communication) that is significantly lower than its sequential counterpart.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45126/1/10626_2005_Article_BF01797138.pd

Supervisory control using variable lookahead policies

This paper deals with the efficient on-line calculation of supervisory controls for discrete event systems (DES's) in the framework of limited lookahead control policies (or LLPs) that we introduced in previous papers. In the LLP scheme, the control action after a given trace of events has been executed is calculated on-line on the basis of an N -step ahead projection of the behavior of the DES. To compute these controls, one must calculate after the execution of each event the supremal controllable sublanguage of a finite language with respect to another finite larger language. In our previous work, we showed how the required supremal controllable sublanguage calculation can be performed by using a backward dynamic programming algorithm over the nodes of the tree representation of these two languages. In this paper, we pursue the same approach for the calculation of LLP controls, but instead we adopt a forward calculation procedure over the N -level tree of interest. This forward procedure improves upon previous work by avoiding the explicit consideration of all the nodes of the N -level tree, while still permitting tree-to-tree recursiveness as enabled events are executed by the system. The forward search ends whenever a control decision can be made unambiguously or whenever the boundary of the N -level tree is reached, whichever comes first. This motivates the name “Variable Lookahead Policy” (or VLP) for this implementation of the LLP supervisory control scheme. This paper presents a general VLP algorithm and studies the properties of several special cases of it. The paper also discusses the implementation of the VLP algorithms and presents computational results regarding the application of these algorithms to a “time-varying” DES.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45124/1/10626_2005_Article_BF01438709.pd

Distributed Supervisory Control of Discrete-Event Systems with Communication Delay

This paper identifies a property of delay-robustness in distributed supervisory control of discrete-event systems (DES) with communication delays. In previous work a distributed supervisory control problem has been investigated on the assumption that inter-agent communications take place with negligible delay. From an applications viewpoint it is desirable to relax this constraint and identify communicating distributed controllers which are delay-robust, namely logically equivalent to their delay-free counterparts. For this we introduce inter-agent channels modeled as 2-state automata, compute the overall system behavior, and present an effective computational test for delay-robustness. From the test it typically results that the given delay-free distributed control is delay-robust with respect to certain communicated events, but not for all, thus distinguishing events which are not delay-critical from those that are. The approach is illustrated by a workcell model with three communicating agents

PSPACE-completeness of Modular Supervisory Control Problems*

In this paper we investigate computational issues associated with the supervision of concurrent processes modeled as modular discrete-event systems. Here, modular discrete-event systems are sets of deterministic finite-state automata whose interaction is modeled by the parallel composition operation. Even with such a simple model process model, we show that in general many problems related to the supervision of these systems are PSPACE-complete. This shows that although there may be space-efficient methods for avoiding the state-explosion problem inherent to concurrent processes, there are most likely no time-efficient solutions that would aid in the study of such “large-scale” systems. We show our results using a reduction from a special class of automata intersection problem introduced here where behavior is assumed to be prefix-closed. We find that deciding if there exists a supervisor for a modular system to achieve a global specification is PSPACE-complete. We also show many verification problems for system supervision are PSPACE-complete, even for prefix-closed cases. Supervisor admissibility and online supervision operations are also discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45090/1/10626_2004_Article_6210.pd