Relevant bibliographies by topics / Actor computations

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Actor computations'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "Actor computations"

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Santo, Michele, and Franco Frattolillo. "Time Costs in Actor Computations." JUCS - Journal of Universal Computer Science 11, no. (6) (2005): 850–73. https://doi.org/10.3217/jucs-011-06-0850.

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Actor programs give rise to computation structures that evolve dynamically and unpredictably both in shape and size. Therefore, their execution times cannot be statically determined. This paper describes an approach to the problem of estimating time costs of actor programs. The approach takes into account the constraints imposed both by the semantics and implementation of the model. In particular, implementation constraints can be captured and exploited to drastically reduce the number of computations generable by the program, thus simplifying the execution time evaluation. Moreover, execution times are expressed in a parametric form by using a variant of the LogP model able to synthetically characterize the target hardware/software platform.
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Janssens, D. "Equivalence of computations in actor grammars." Theoretical Computer Science 109, no. 1-2 (1993): 145–80. http://dx.doi.org/10.1016/0304-3975(93)90067-4.

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Van, den Vonder Sam, Thierry Renaux, Bjarno Oeyen, Koster Joeri De, and Meuter Wolfgang De. "Tackling the Awkward Squad for Reactive Programming: The Actor-Reactor Model." Leibniz International Proceedings in Informatics (LIPIcs) 166 (November 6, 2020): 19:1–19:29. https://doi.org/10.4230/LIPIcs.ECOOP.2020.19.

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Reactive programming is a programming paradigm whereby programs are internally represented by a dependency graph, which is used to automatically (re)compute parts of a program whenever its input changes. In practice reactive programming can only be used for some parts of an application: a reactive program is usually embedded in an application that is still written in ordinary imperative languages such as JavaScript or Scala. In this paper we investigate this embedding and we distill "the awkward squad for reactive programming" as 3 concerns that are essential for real-world software development, but that do not fit within reactive programming. They are related to long lasting computations, side-effects, and the coordination between imperative and reactive code. To solve these issues we design a new programming model called the Actor-Reactor Model in which programs are split up in a number of actors and reactors. Actors and reactors enforce a strict separation of imperative and reactive code, and they can be composed via a number of composition operators that make use of data streams. We demonstrate the model via our own implementation in a language called Stella.
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Li, Huilin, Jiaqi Yang, and Ziquan Xiang. "A Fuzzy Linguistic Multi-Criteria Decision-Making Approach to Assess Emergency Suppliers." Sustainability 14, no. 20 (2022): 13114. http://dx.doi.org/10.3390/su142013114.

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Under the influence of COVID-19, various emergency supplies have repeatedly broken links, seriously affecting normal life and hindering the sustainable development of enterprises and society. Therefore, suitable emergency suppliers are crucial. To prioritize and select suitable emergency suppliers, key indicators were determined, and evaluation models were established based on the characteristics of epidemic situations and epidemic prevention materials. The application of the MCDM (multi-criteria decision-making) issue combining fuzzy SWARA (the stepwise weight assessment ratio analysis) and the actor analysis method to emergency supplier selection studies, called the fuzzy SWARA-based actor analysis method, is used to identify appropriate suppliers for optimizing pre-preparation. Results of evaluation system weight computations by the Fuzzy SWARA-based actor analysis method show that the overall prioritization of four non-economic factors in ranking orders are “Delivery Capacity”, “Flexible Supply Capacity”, “Quality”, and “Social Evaluation and Reputation”. For the inclusion of conflicting standards and the unquantifiable feature, economic and non-economic factors were discussed separately and evaluated by language variables. Additionally, the fuzzy actor analysis indicated that economic factors and non-economic factors need to be considered comprehensively for emergency supplier selection. This method has good operability and reference value, convenient for the final choice making according to actual situation.
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Yanchin, Ivan A., and Oleg N. Petrov. "ON AUTONOMOUS AND SMART SHIPS: CHALLENGES AND BENEFITS FOR COMPUTE SCIENCES AND TELECOMMUNICATIONS." T-Comm 14, no. 11 (2020): 46–56. http://dx.doi.org/10.36724/2072-8735-2020-14-11-46-56.

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The paper is dedicated to the overview of the autonomous ship onboard control system functionality. The paper is dedicated to autonomous maritime navigation, i.e. to planning a safe and optimal route for an autonomous ship amid the absence of crew on board and to handling an autonomous ship when underway, ensuring seaworthiness and route correctness. Moreover, the paper describes communications of several autonomous ships and autonomous and ordinary ships to infer a collective decision on how to pass safely through a particular area. Since it is expected that autonomous ships are going to be equipped with dozens of sensors and detectors, the paper describes remote monitoring of an autonomous ship when underway. Since an autonomous ship highly depends on its onboard control system, the paper also pays attention to the robustness of the system. The paper suggests evolutionary computations as a solution for the route planning problem because this approach enables to perform multicriteria optimization of a set of solutions simultaneously. For autonomous ship handling and seaworthiness control, the paper suggests machine learning techniques because these techniques can solve problems in case of uncertainty and the environmental mutability. For inter-ship communications, the paper suggests distributed consensus algorithms, widely used in parallel and distributed computation systems. To ensure the onboard control system robustness, the paper suggests the actor approach that represents that the entire software system consists of a set of elementary agents communicating in the distributed computational environment.
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Tessereau, Charline, Reuben O’Dea, Stephen Coombes, and Tobias Bast. "Reinforcement learning approaches to hippocampus-dependent flexible spatial navigation." Brain and Neuroscience Advances 5 (January 2021): 239821282097563. http://dx.doi.org/10.1177/2398212820975634.

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Humans and non-human animals show great flexibility in spatial navigation, including the ability to return to specific locations based on as few as one single experience. To study spatial navigation in the laboratory, watermaze tasks, in which rats have to find a hidden platform in a pool of cloudy water surrounded by spatial cues, have long been used. Analogous tasks have been developed for human participants using virtual environments. Spatial learning in the watermaze is facilitated by the hippocampus. In particular, rapid, one-trial, allocentric place learning, as measured in the delayed-matching-to-place variant of the watermaze task, which requires rodents to learn repeatedly new locations in a familiar environment, is hippocampal dependent. In this article, we review some computational principles, embedded within a reinforcement learning framework, that utilise hippocampal spatial representations for navigation in watermaze tasks. We consider which key elements underlie their efficacy, and discuss their limitations in accounting for hippocampus-dependent navigation, both in terms of behavioural performance (i.e. how well do they reproduce behavioural measures of rapid place learning) and neurobiological realism (i.e. how well do they map to neurobiological substrates involved in rapid place learning). We discuss how an actor–critic architecture, enabling simultaneous assessment of the value of the current location and of the optimal direction to follow, can reproduce one-trial place learning performance as shown on watermaze and virtual delayed-matching-to-place tasks by rats and humans, respectively, if complemented with map-like place representations. The contribution of actor–critic mechanisms to delayed-matching-to-place performance is consistent with neurobiological findings implicating the striatum and hippocampo-striatal interaction in delayed-matching-to-place performance, given that the striatum has been associated with actor–critic mechanisms. Moreover, we illustrate that hierarchical computations embedded within an actor–critic architecture may help to account for aspects of flexible spatial navigation. The hierarchical reinforcement learning approach separates trajectory control via a temporal-difference error from goal selection via a goal prediction error and may account for flexible, trial-specific, navigation to familiar goal locations, as required in some arm-maze place memory tasks, although it does not capture one-trial learning of new goal locations, as observed in open field, including watermaze and virtual, delayed-matching-to-place tasks. Future models of one-shot learning of new goal locations, as observed on delayed-matching-to-place tasks, should incorporate hippocampal plasticity mechanisms that integrate new goal information with allocentric place representation, as such mechanisms are supported by substantial empirical evidence.
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Zelenskii, Aleksandr Aleksandrovich, and Andrei Armovich Gribkov. "Configuration of memory-oriented motion control system." Программные системы и вычислительные методы, no. 3 (March 2024): 12–25. http://dx.doi.org/10.7256/2454-0714.2024.3.71073.

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The paper investigates the possibilities of configuring the control cycle, i.e., determining the distribution of time intervals required for the execution of individual control operations across execution threads, which ensures the realizability of control. The object of research in this article are control systems with object-oriented architecture, assuming a combined vertical-horizontal integration of functional blocks and modules that distribute all control tasks among themselves. This architecture is realized by means of an actor instrumental model using metaprogramming. Such control systems are best at reducing control cycle time by performing computational and other control operations in parallel. Several approaches to control cycle configuration are considered: without optimization, with combinatorial optimization in time, with combinatorial optimization in system resources. Also, achieving a near-optimal configuration can be achieved by using adaptive configuration. Research shows that the control system cycle configuration problem has several solutions. Practical obtaining a solution to the configuration problem in the case of combinatorial optimization is associated with significant difficulties due to the high algorithmic complexity of the problem and a large amount of required computations, rapidly growing as the number of operations at the stages of the control cycle. A possible means of overcoming these difficulties is the use of stochastic methods, which sharply reduce the required amount of computation. Also, a significant reduction in the complexity of the task of configuring the control system cycle can be achieved by using adaptive configuration, which has two variants of realization. The first variant is the real-time configuration of the control system cycle. The second variant is the determination of quasi-optimal configuration on the basis of multiple configurations with different initial data and subsequent comparison of the obtained results.
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FRÜHWIRTH, THOM. "Parallelism, concurrency and distribution in constraint handling rules: A survey." Theory and Practice of Logic Programming 18, no. 5-6 (2018): 759–805. http://dx.doi.org/10.1017/s1471068418000078.

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AbstractConstraint Handling Rules (CHR) is both an effective concurrent declarative programming language and a versatile computational logic formalism. In CHR, guarded reactive rules rewrite a multi-set of constraints. Concurrency is inherent, since rules can be applied to the constraints in parallel. In this comprehensive survey, we give an overview of the concurrent, parallel as well as distributed CHR semantics, standard and more exotic, that have been proposed over the years at various levels of refinement. These semantics range from the abstract to the concrete. They are related by formal soundness results. Their correctness is proven as a correspondence between parallel and sequential computations. On the more practical side, we present common concise example CHR programs that have been widely used in experiments and benchmarks. We review parallel and distributed CHR implementations in software as well as hardware. The experimental results obtained show a parallel speed-up for unmodified sequential CHR programs. The software implementations are available online for free download and we give the web links. Due to its high level of abstraction, the CHR formalism can also be used to implement and analyse models for concurrency. To this end, the Software Transaction Model, the Actor Model, Colored Petri Nets and the Join-Calculus have been faithfully encoded in CHR. Finally, we identify and discuss commonalities of the approaches surveyed and indicate what problems are left open for future research.
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Amado, Leonardo, Reuth Mirsky, and Felipe Meneguzzi. "Goal Recognition as Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9644–51. http://dx.doi.org/10.1609/aaai.v36i9.21198.

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Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key issues. First, encoding these dynamics requires careful design by a domain expert, which is often not robust to noise at recognition time. Second, existing approaches often need costly real-time computations to reason about the likelihood of each potential goal. In this paper, we develop a framework that combines model-free reinforcement learning and goal recognition to alleviate the need for careful, manual domain design, and the need for costly online executions. This framework consists of two main stages: Offline learning of policies or utility functions for each potential goal, and online inference. We provide a first instance of this framework using tabular Q-learning for the learning stage, as well as three measures that can be used to perform the inference stage. The resulting instantiation achieves state-of-the-art performance against goal recognizers on standard evaluation domains and superior performance in noisy environments.
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Singh, Sunil K., Steven D. Pieper, Jethran Guinness, and Dan O. Popa. "Control and Coordination of Head, Eyes, and Facial Expressions of Virtual Actors in Virtual Environments." Presence: Teleoperators and Virtual Environments 5, no. 4 (1996): 402–15. http://dx.doi.org/10.1162/pres.1996.5.4.402.

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This paper addresses the modeling and computational issues associated with the control and coordination of head, eyes, and facial expressions of virtual human actors. The emphasis, as much as possible, is on using accurate physics-based computations for motion computation. Some key issues discussed in this work include the use of kinematics and inverse kinematics, trajectory planning, and the use of finite element methods to model soft tissue deformations.
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Dissertations / Theses on the topic "Actor computations"

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Peruzzi, Manuel. "Distributing Aggregate Computations on top of Akka Actors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17016/.

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In the context of the Internet of Things, development of large-scale, adaptive systems usually focuses on the behavior of the single device. Aggregate programming is a paradigm that provides an alternative approach, in which the basic unit of computing is a cooperating collection of devices, instead of a single device. scafi is a Scala framework for aggregate programming, and provides an Akka-based platform for aggregate applications, supporting both peer-to-peer and server-based networks. Moreover, scafi offers a simulator module for the simulation of an aggregate system. The work described in this thesis consists in the analysis of scafi, in the partial re-engineering of its internal actor platform, and in the development of new features. The main goal is to enhance the flexibility of scafi in a distributed context, promoting its adoption for programming spatial systems. First of all, communication between distributed nodes is enabled, by defining a JSON-based serialization strategy, which promotes interoperability. A hybrid platform is also introduced, exploiting a peer-to-peer communication between devices, with a central unit that manages all the relevant space related information. This platform fills the main gap of the peer to-peer approach in a distributed environment: tracking of remote devices. Moreover, a code mobility approach is implemented, allowing the assignment of new programs to devices, at runtime. Lastly, the concept of monitoring a distributed aggregate system emerged, leading to the development of a graphical user interface, observing the devices in a running system. In this thesis, I present the new architecture and API of the actor platform of scafi, designed with the aim of ensure a more flexible approach for the development of distributed applications with aggregate computing.
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Bergdahl, Joakim. "Asynchronous Advantage Actor-Critic with Adam Optimization and a Layer Normalized Recurrent Network." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220698.

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State-of-the-art deep reinforcement learning models rely on asynchronous training using multiple learner agents and their collective updates to a central neural network. In this thesis, one of the most recent asynchronous policy gradientbased reinforcement learning methods, i.e. asynchronous advantage actor-critic (A3C), will be examined as well as improved using prior research from the machine learning community. With application of the Adam optimization method and addition of a long short-term memory (LSTM) with layer normalization, it is shown that the performance of A3C is increased.<br>Moderna modeller inom förstärkningsbaserad djupinlärning förlitar sig på asynkron träning med hjälp av ett flertal inlärningsagenter och deras kollektiva uppdateringar av ett centralt neuralt nätverk. I denna studie undersöks en av de mest aktuella policygradientbaserade förstärkningsinlärningsmetoderna, i.e. asynchronous advantage actor-critic (A3C) med avsikt att förbättra dess prestanda med hjälp av tidigare forskning av maskininlärningssamfundet. Genom applicering av optimeringsmetoden Adam samt långt korttids minne (LSTM) med nätverkslagernormalisering visar det sig att prestandan för A3C ökar.
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Casadei, Roberto. "Aggregate Programming in Scala: a Core Library and Actor-Based Platform for Distributed Computational Fields." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10341/.

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La programmazione aggregata è un paradigma che supporta la programmazione di sistemi di dispositivi, adattativi ed eventualmente a larga scala, nel loro insieme -- come aggregati. L'approccio prevalente in questo contesto è basato sul field calculus, un calcolo formale che consente di definire programmi aggregati attraverso la composizione funzionale di campi computazionali, creando i presupposti per la specifica di pattern di auto-organizzazione robusti. La programmazione aggregata è attualmente supportata, in modo più o meno parziale e principalmente per la simulazione, da DSL dedicati (cf., Protelis), ma non esistono framework per linguaggi mainstream finalizzati allo sviluppo di applicazioni. Eppure, un simile supporto sarebbe auspicabile per ridurre tempi e sforzi d'adozione e per semplificare l'accesso al paradigma nella costruzione di sistemi reali, nonché per favorire la ricerca stessa nel campo. Il presente lavoro consiste nello sviluppo, a partire da un prototipo della semantica operazionale del field calculus, di un framework per la programmazione aggregata in Scala. La scelta di Scala come linguaggio host nasce da motivi tecnici e pratici. Scala è un linguaggio moderno, interoperabile con Java, che ben integra i paradigmi ad oggetti e funzionale, ha un sistema di tipi espressivo, e fornisce funzionalità avanzate per lo sviluppo di librerie e DSL. Inoltre, la possibilità di appoggiarsi, su Scala, ad un framework ad attori solido come Akka, costituisce un altro fattore trainante, data la necessità di colmare l'abstraction gap inerente allo sviluppo di un middleware distribuito. Nell'elaborato di tesi si presenta un framework che raggiunge il triplice obiettivo: la costruzione di una libreria Scala che realizza la semantica del field calculus in modo corretto e completo, la realizzazione di una piattaforma distribuita Akka-based su cui sviluppare applicazioni, e l'esposizione di un'API generale e flessibile in grado di supportare diversi scenari.
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"Actors : a model of concurrent computation in distributed systems." MIT Press, 1986. http://hdl.handle.net/1721.1/1692.

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Agha, Gul Abdulnabi. "ACTORS: A Model of Concurrent Computation in Distributed Systems." 1985. http://hdl.handle.net/1721.1/6952.

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A foundational model of concurrency is developed in this thesis. We examine issues in the design of parallel systems and show why the actor model is suitable for exploiting large-scale parallelism. Concurrency in actors is constrained only by the availability of hardware resources and by the logical dependence inherent in the computation. Unlike dataflow and functional programming, however, actors are dynamically reconfigurable and can model shared resources with changing local state. Concurrency is spawned in actors using asynchronous message-passing, pipelining, and the dynamic creation of actors. This thesis deals with some central issues in distributed computing. Specifically, problems of divergence and deadlock are addressed. For example, actors permit dynamic deadlock detection and removal. The problem of divergence is contained because independent transactions can execute concurrently and potentially infinite processes are nevertheless available for interaction.
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BELLA, Giovanni. "The hydrogen bond: actor and stage in the theater of chemistry." Doctoral thesis, 2022. https://hdl.handle.net/11570/3222901.

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Hydrogen bond plays a key role in a wide range of inorganic, organic, as well as biological systems. The understanding on how the chemical environment can affect this kind of interaction is crucial to predict its binding strength, and consequently the robustness and the dynamic properties of many supramolecular systems. Both in nature and in artificial system hydrogen bonding offers a plethora of possible assembling and arrangement between molecules. In this thesis, a theoretical approach was adopted to spotlight the intimate nature of hydrogen interactions in two unprecedented chemical H-bonded architecture: 1) an AA-DD pattern in a amine-borane azacrown involving a double dihydrogen interaction; 2) a BODIPY dyad generated by two complementary chromophores directly bearing in meso position an acceptor-donor-acceptor (A-D-A) and a donor-acceptor-donor (D-A-D) frontier geometry. Both cases were computationally treated by means density functional theory (DFT) in order to highlight hydrogen bond strengths, directionality and function-structure relationship.
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Books on the topic "Actor computations"

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Dressler, Falko. Self-organization in sensor and actor networks. John Wiley & Sons, 2007.

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Actors: A model of concurrent computation in distributed systems. MIT Press, 1986.

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Agha, Gul. ACTORS: A Model of Concurrent Computation in Distributed Systems. The MIT Press, 1986.

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Self-Organization in Sensor and Actor Networks. Wiley, 2008.

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Dressler, Falko. Self-Organization in Sensor and Actor Networks. Wiley & Sons, Incorporated, John, 2008.

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Dressler, Falko. Self-Organization in Sensor and Actor Networks. Wiley & Sons, Incorporated, John, 2008.

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Dressler, Falko. Self-Organization in Sensor and Actor Networks. Wiley & Sons, Limited, John, 2007.

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Agha, Gul. ACTORS: A Model of Concurrent Computation in Distributed Systems (The Mit Press Series in Artificial Intelligence). The MIT Press, 1986.

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Patisaul, Heather B., and Scott M. Belcher. The Path Forward. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199935734.003.0008.

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This chapter focuses on the contemporary approaches of research being used to understand the actions of EDCs and emerging high-throughput screening approaches to examine new and existing chemicals for endocrine-disrupting activities. Concepts arising from the 2007 NRC report “Toxicity Testing in the 21st Century: A Vision and a Strategy” are delineated and the ongoing development of predictive computational toxicology approaches are addressed. The screening strategies being developed under the Tox21 and Toxicity Forecaster (ToxCast) programs are described, with a review of advantages, challenges, and progress to date. There is a brief overview of the EPA’s Interactive Chemical Safety for Sustainability (iCSS) Dashboard as a portal for accessing the ToxCast data through ToxCastDB, and the EPA’s Aggregated Computational Toxicology data warehouse (ACToR), which contains all publicly available EPA chemical toxicity data. Additional challenges related to the inability of current screening approaches to address complex physiology involved in neuroendocrine disruption are addressed.
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Desmarais, Bruce A., and Skyler J. Cranmer. Statistical Inference in Political Networks Research. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.8.

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Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. This chapter reviews the most prominent methods of inferential network analysis for both cross-sectionally and longitudinally observed networks, including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, the chapter summarizes its analytic form, identifies prominent published applications in political science, and discusses computational considerations. It concludes with a set of guidelines for selecting a method for a given application.
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Book chapters on the topic "Actor computations"

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Agha, Gul, WooYoung Kim, and Rajendra Panwar. "Actor languages for specification of parallel computations." In Specification of Parallel Algorithms. American Mathematical Society, 1994. http://dx.doi.org/10.1090/dimacs/018/16.

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Agha, Gul, Ian A. Mason, Scott Smith, and Carolyn Talcott. "Towards a theory of actor computation." In CONCUR '92. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0084816.

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Iacob, S. M., C. H. M. Nieuwenhuis, N. J. E. Wijngaards, G. Pavlin, and J. B. van Veelen. "Actor-Agent Communities: Design Approaches." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03214-1_25.

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Tihomirov, Yunes, Roman Rybka, Alexey Serenko, and Alexander Sboev. "Actor-Critic Spiking Neural Network with RSTDP Actor Learning and TD-LTP Critic Learning." In Studies in Computational Intelligence. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76516-2_41.

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Hartmann, Lars, Neil D. Jones, Jakob Grue Simonsen, and Søren Bjerregaard Vrist. "Computational Biology: A Programming Perspective." In Formal Modeling: Actors, Open Systems, Biological Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24933-4_20.

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Szarek, Kamil, Wojciech Turek, Łukasz Bratek, Marek Kisiel-Dorohinicki, and Aleksander Byrski. "Actor-Based Scalable Simulation of N-Body Problem." In Computational Science – ICCS 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36021-3_48.

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Agha, Gul. "Semantic considerations in the actor paradigm of concurrent computation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/3-540-15670-4_8.

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Eker, Steven. "Fast Sort Computations for Order-Sorted Matching and Unification." In Formal Modeling: Actors, Open Systems, Biological Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24933-4_15.

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Chiu, Christopher, and Zenon Chaczko. "Designing Biomimetic-Inspired Middleware for Anticipative Sensor-Actor Networks." In Studies in Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15720-7_10.

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Reinhardt, Wolfgang, Adrian Wilke, Matthias Moi, Hendrik Drachsler, and Peter Sloep. "Mining and Visualizing Research Networks Using the Artefact-Actor-Network Approach." In Computational Social Networks. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_10.

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Conference papers on the topic "Actor computations"

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Templier, Paul, Emmanuel Rachelson, Antoine Cully, and Dennis G. Wilson. "Genetic Drift Regularization: On Preventing Actor Injection from Breaking Evolution Strategies." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611871.

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Dong, Yihong, Kangcheng Luo, Xue Jiang, Zhi Jin, and Ge Li. "PACE: Improving Prompt with Actor-Critic Editing for Large Language Model." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.436.

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Han, Senyu, Lu Chen, Li-Min Lin, Zhengshan Xu, and Kai Yu. "IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.88.

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Batz-Lineiro, Aglaya, Merlin P. Grueso-Hinestroza, Lizeth F. Serrano-Cardenas, David F. D’Croz-Baron, and Carlos A. Ojeda-Sanchez. "Recognizing Overarching Themes and Actors in Peacebuilding: A Longitudinal Analysis of Press Content in Latin America." In 2024 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). IEEE, 2024. http://dx.doi.org/10.1109/colcaci63187.2024.10666530.

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Mutanna, М. S. А., and А. А. Tselykh. "Intelligent Offloading of LoRaWAN Computations for Industrial IoT Using Soft Actor-Critic Algorithm." In XII All-Russian Scientific Conference &quot;System Synthesis and Applied Synergetics&quot;. Southern Federal University, 2024. https://doi.org/10.18522/syssyn-2024-23.

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Ahn, Jeong-Soo, Kyihwan Park, and Richard H. Crawford. "Design Actor-Based Representation of Iterative Design Process for Embodiment Design." In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0054.

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Abstract Design activities consists not only of product design, but also of development of the process by which the product will be designed. However, development and documentation of computational design processes are largely unsupported by commercial CAD systems. This paper proposes a new computational architecture for procedural representation of embodiment design processes. A design actor is defined as an independent computational unit of the design process. The proposed architecture models a design process as a sequence of design tasks by representing individual parameters and tasks as design actors, and the sequence of design tasks as a network of design actors assembled according to their functional dependencies. The use of design actors promotes modularity in representing design problems and solution processes. Iterative design processes can be represented since the architecture provides explicit feedforward and feedback information exchange between design actors. The paper describes an object-oriented implementation of the design actor architecture, and demonstrates the approach with an example design of an air-core solenoid in an optical disk drive.
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Laigner, Rodrigo, Sérgio Lifschitz, Marcos Kalinowski, Marcus Poggi, and Marcos Antonio Vaz Salles. "Towards a Technique for Extracting Relational Actors from Monolithic Applications." In XXXIV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbbd.2019.8814.

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Relational actors, or reactors for short, integrate the actor model with the relational data model, providing an abstraction for enabling actor-relational database systems. However, as a novel model of computation for databases, there is no extensive work on reasoning about reactor modeling. To close this gap, this paper aims to propose as well as evaluate a technique to extract reactors from a monolithic system. For evaluation, we selected a REST-based open-source OLTP system in which a decomposition to microservices was conducted and applied our technique on its predecessor monolithic version. Our technique led to the same set of decisions, regarding table and behavior selection, taken by experts when decomposing the same system into microservices. The proposed technique can be seen as a first step towards supporting practitioners in decomposing OLTP systems into reactors.
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Neal Reilly, William, and Leonard Eusebi. "Approaches to Extending Game-Theoretic Analyses to Complex, Real-World Scenarios." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001853.

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In domains ranging from military engagements to business to politics to games, competitors take actions to gain an advantage over others. Game theory has been used extensively since the 1950s to analyze such domains and to gain insights into the best moves for all competitors. While it is a powerful tool for analysis, game theory often falls short when applied to real-world encounters. Game-theoretic approaches over-simplify by assuming each side is composed of rational actors that attempt to maximize a single-valued utility function. Even with that simplification, real-world scenarios are often difficult to formalize as a solvable “game,” and even when the problem can be defined as a game, it is computationally expensive to calculate the best actions for each actor.We will present research that extends game theory to include multiple forms of utility for each actor. This enables us to recast traditional, albeit simple game-theory games like the Prisoners’ Dilemma and the Ultimatum Game, which produce results at odds with real-world expectations when confined to traditional measures of utility (i.e., minimizing jail time and maximizing money). By adding utility measures like commitment and fairness, we can generate a Pareto-optimal set of solutions that are better at recreating and explaining real-world behavior than traditional single-utility game theory. In our formulation, the actors are still acting rationally, they are just factoring in a more complex set of tradeoffs that our multi-utility game theory can naturally model.We will also present research into a game representation scheme that lets the scenario modeler express real-world action-to-action constraints like “enables” and “blocks.” These constraints support basic reasoning about ordering of actions without having to build full search tress or reason about time generally. Accounting for these constraints also significantly reduces the space of possible solutions, making it tractable to find exact solutions for certain classes of complex scenarios.Finally, we will present a software toolkit that simplifies the process of defining a game and analyzing the plausible outcomes. The model building tool helps analysts capture the goals and motivations of each actor, the actions available, and how those actions affect goals or other actions. Using these models, the analysis suite calculates the Pareto-optimal choices for each actor in that scenario and helps analysts navigate the plausible outcomes. With these tools, decision makers can assess the value of their strategic options, even in cases where adversaries may choose actions traditional game theory would label incorrect.We have used the software toolkit to create and analyze several models, from simple games like rock-paper-scissors to a real-world political gray-zone conflict with 3 nation states, 23 possible actions, 18 different motivations, and 10^21 possible solutions. The results were computed in seconds and align with behavior of the real-world actors. Policy analysts without a background in computational modeling have also used the toolkit to create “backcasting” models of historical situations. These models successfully explained the behavior of the actors involved. These evaluations show that the toolkit is both useful and usable for analyzing real-world multi-actor interactions.
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Peters, James F. "Granular Computing in Actor-Critic Learning." In 2007 IEEE Symposium on Foundations of Computational Intelligence (FOCI 2007). IEEE, 2007. http://dx.doi.org/10.1109/foci.2007.372148.

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Butcher, Stephyn G. W., and John W. Sheppard. "An actor model implementation of distributed factored evolutionary algorithms." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3208261.

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Reports on the topic "Actor computations"

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.
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