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118
A Framework for Comparing Models of Computation
 IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems
, 1998
"... Abstract—We give a denotational framework (a “meta model”) within which certain properties of models of computation can be compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if, given the constraints imposed by the inputs, there are e ..."
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Cited by 244 (54 self)
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Abstract—We give a denotational framework (a “meta model”) within which certain properties of models of computation can be compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if, given the constraints imposed by the inputs, there are exactly one or exactly zero behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is through signals, which are collections of events. Each event is a valuetag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous processes have only synchronous signals as behaviors. Strict causality (in timed tag systems) and continuity (in untimed tag systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, Petri nets, and discreteevent systems. I.
Hierarchical Finite State Machines with Multiple Concurrency Models
 IEEE Transactions on Computeraided Design of Integrated Circuits and Systems
, 1999
"... This paper studies the semantics of hierarchical finite state machines (FMS's) that are composed using various concurrency models, particularly dataflow, discreteevents, and synchronous/reactive modeling. It is argued that all three combinations are useful, and that the concurrency model can be sel ..."
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Cited by 116 (36 self)
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This paper studies the semantics of hierarchical finite state machines (FMS's) that are composed using various concurrency models, particularly dataflow, discreteevents, and synchronous/reactive modeling. It is argued that all three combinations are useful, and that the concurrency model can be selected independently of the decision to use hierarchical FSM's. In contrast, most formalisms that combine FSM's with concurrency models, such as Statecharts (and its variants) and hybrid systems, tightly integrate the FSM semantics with the concurrency semantics. An implementation that supports three combinations is described.
Elevator Group Control Using Multiple Reinforcement Learning Agents
 Machine Learning
, 1998
"... . Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithms have appeared that approximate dynamic programming on an incremental basis. They can be trained on the basis of real or simulated experiences, focusing their computation on ..."
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Cited by 80 (2 self)
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. Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithms have appeared that approximate dynamic programming on an incremental basis. They can be trained on the basis of real or simulated experiences, focusing their computation on areas of state space that are actually visited during control, making them computationally tractable on very large problems. If each member of a team of agents employs one of these algorithms, a new collective learning algorithm emerges for the team as a whole. In this paper we demonstrate that such collective RL algorithms can be powerful heuristic methods for addressing largescale control problems. Elevator group control serves as our testbed. It is a difficult domain posing a combination of challenges not seen in most multiagent learning research to date. We use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reinforcement ...
Temporal Abstraction in Reinforcement Learning
, 2000
"... Decision making usually involves choosing among different courses of action over a broad range of time scales. For instance, a person planning a trip to a distant location makes highlevel decisions regarding what means of transportation to use, but also chooses lowlevel actions, such as the moveme ..."
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Cited by 59 (2 self)
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Decision making usually involves choosing among different courses of action over a broad range of time scales. For instance, a person planning a trip to a distant location makes highlevel decisions regarding what means of transportation to use, but also chooses lowlevel actions, such as the movements for getting into a car. The problem of picking an appropriate time scale for reasoning and learning has been explored in artificial intelligence, control theory and robotics. In this dissertation we develop a framework that allows novel solutions to this problem, in the context of Markov Decision Processes (MDPs) and reinforcement learning. In this dissertation, we present a general framework for prediction, control and learning at multipl...
Deadlock Avoidance Policies for Automated Manufacturing Systems Using Finite State Automata
"... This chapter considers the problem of deadlock avoidance in flexibly automated manufacturing systems, one of the most prevalent supervisory control problems that challenges the effective deployment of these environments. The problem is addressed through the modeling abstraction of the (sequential) r ..."
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Cited by 40 (25 self)
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This chapter considers the problem of deadlock avoidance in flexibly automated manufacturing systems, one of the most prevalent supervisory control problems that challenges the effective deployment of these environments. The problem is addressed through the modeling abstraction of the (sequential) resource allocation system (RAS), and the pursued analysis uses concepts and results from the formal modeling framework of finite state automata (FSA). A notion of optimality is defined through the notion of maximal permissiveness, but the computation of the optimal DAP is shown to be NPHard. Hence, the last part of the chapter discusses some approaches that have been developed by the relevant research community in its effort to deal with this negative complexity result.
Synthesis of Parallel Hardware Implementations from Synchronous Dataflow Graph Specifications
, 1998
"... ..."
A programming model for timesynchronized distributed realtime systems
 In 13th IEEE Real Time and Embedded Technology and Applications Symposium, 2007. RTAS ’07
, 2007
"... Discreteevent (DE) models are formal system specifications that have analyzable deterministic behaviors. Using a global, consistent notion of time, DE components communicate via timestamped events. DE models have primarily been used in performance modeling and simulation, where time stamps are a m ..."
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Cited by 35 (24 self)
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Discreteevent (DE) models are formal system specifications that have analyzable deterministic behaviors. Using a global, consistent notion of time, DE components communicate via timestamped events. DE models have primarily been used in performance modeling and simulation, where time stamps are a modeling property bearing no relationship to real time during execution of the model. In this paper, we extend DE models with the capability of relating certain events to physical time. We propose a programming model, called PTIDES (Programming Temporally Integrated Distributed Embedded Systems), which has DE semantics, but with carefully chosen relations between model time and real time. Key to making this model effective is to ensure that constraints that guarantee determinacy in the semantics are preserved at runtime. To accomplish this, we give a distributed execution strategy that obeys DE semantics without the penalty of totally ordered executions based on time stamps. Our technique relies on having a distributed common notion of time, known to some precision. Based on causality analysis of DE models, we define relevant dependency and relevant orders to enable outoforder execution without compromising determinism and without requiring backtracking. 1
Discreteevent simulation of Fluid Stochastic Petri Nets
 IEEE Transactions on Software Engineering
, 1999
"... The purpose of this paper is to describe a method for simulation of recently introduced fluid stochastic Petri nets. Since such nets result in rather complex set of partial differential equations, numerical solution becomes a formidable task. Because of a mixed, discrete and continuous state space, ..."
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Cited by 29 (6 self)
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The purpose of this paper is to describe a method for simulation of recently introduced fluid stochastic Petri nets. Since such nets result in rather complex set of partial differential equations, numerical solution becomes a formidable task. Because of a mixed, discrete and continuous state space, simulative solution also poses some interesting challenges, which are addressed in the paper. 1
Comparing Models of Computation
 IN PROC. ICCAD
, 1996
"... We give a denotational framework (a "meta model") within which certain properties of models of computation can be understood and compared. It describes concurrent processes as sets of possible behaviors. Compositions of processes are given as intersections of their behaviors. The interaction between ..."
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Cited by 28 (1 self)
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We give a denotational framework (a "meta model") within which certain properties of models of computation can be understood and compared. It describes concurrent processes as sets of possible behaviors. Compositions of processes are given as intersections of their behaviors. The interaction between processes is through signals, which are collections of events. Each event is a valuetag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous systems contain synchronous signals. Strict causality (in timed systems) and continuity (in untimed systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, Petri nets, and discreteevent systems.