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1 – 10 of over 100000Peter Arcidiacono, Patrick Bayer, Federico A. Bugni and Jonathan James
Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating…
Abstract
Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.
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Miao Ye, Lin Qiang Huang, Xiao Li Wang, Yong Wang, Qiu Xiang Jiang and Hong Bing Qiu
A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.
Abstract
Purpose
A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.
Design/methodology/approach
First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.
Findings
Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.
Originality/value
Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.
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Abstract
This paper deals with the discrimination problem of the states which involve two types of uncertainty: “randomness” and “fuzziness.” This problem is very important in the fields of soft science such as management science, sociology, eta, since the object of discrimination involves these types of uncertainty. In this paper, we propose a discrimination system of fuzzy states on a probability space and derive the decision rule which minimizes the average of error probability of discrimination. In our formulation of the discrimination system there exists the case that a large number of observations does not necessarily make the average of error probability small, so that an index for decision of an upper limit of number of observations is also derived.
Victor Aguirregabiria and Arvind Magesan
We derive marginal conditions of optimality (i.e., Euler equations) for a general class of Dynamic Discrete Choice (DDC) structural models. These conditions can be used to…
Abstract
We derive marginal conditions of optimality (i.e., Euler equations) for a general class of Dynamic Discrete Choice (DDC) structural models. These conditions can be used to estimate structural parameters in these models without having to solve for approximate value functions. This result extends to discrete choice models the GMM-Euler equation approach proposed by Hansen and Singleton (1982) for the estimation of dynamic continuous decision models. We first show that DDC models can be represented as models of continuous choice where the decision variable is a vector of choice probabilities. We then prove that the marginal conditions of optimality and the envelope conditions required to construct Euler equations are also satisfied in DDC models. The GMM estimation of these Euler equations avoids the curse of dimensionality associated to the computation of value functions and the explicit integration over the space of state variables. We present an empirical application and compare estimates using the GMM-Euler equations method with those from maximum likelihood and two-step methods.
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Saeed Shamaghdari and S.K.Y. Nikravesh
The purpose of this paper is to present a nonlinear model along with stability analysis of a flexible supersonic flight vehicle system.
Abstract
Purpose
The purpose of this paper is to present a nonlinear model along with stability analysis of a flexible supersonic flight vehicle system.
Design/methodology/approach
The mathematical state space nonlinear model of the system is derived using Lagrangian approach such that the applied force, moment, and generalized force are all assumed to be nonlinear functions of the system states. The condition under which the system would be unstable is derived and when the system is stable, the region of attraction of the system equilibrium state is determined using the Lyapunov theory and sum of squares optimization method. The method is applied to a slender flexible body vehicle, which is referenced by the other researchers in the literature.
Findings
It is demonstrated that neglecting the nonlinearity in external force, moment and generalized force, as it was assumed by other researchers, can cause significant variations in stability conditions. Moreover, when the system is stable, it is shown analytically here that a reduction in dynamic pressure can make a larger region of attraction, and thus instability will occur in a larger angle of attack, greater angular velocity and elastic displacement.
Practical implications
In order to carefully study the behavior of aeroelastic flight vehicle, a nonlinear model and analysis is definitely necessary. Moreover, for the design of the airframe and/or control purposes, it is essential to investigate region of attraction of equilibrium state of the stable flight vehicle.
Originality/value
Current stability analysis methods for nonlinear elastic flight vehicles are unable to determine the state space region where the system is stable. Nonlinear modeling affects the determination of the stability region and instability condition. This paper presents a new approach to stability analysis of the nonlinear flexible flight vehicle. By determining the region of attraction when the system is stable, it is demonstrated analytically, in this research, that decreasing the dynamic pressure can produce larger region of attraction.
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Yang Guan, Shengbo Eben Li, Jingliang Duan, Wenjun Wang and Bo Cheng
Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model…
Abstract
Purpose
Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.
Design/methodology/approach
In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.
Findings
Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.
Originality/value
This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.
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Degan Zhang, Guanping Zeng, Enyi Chen and Baopeng Zhang
Active service is one of key problems of ubiquitous computing paradigm. Context‐aware computing is helpful to carry out this service. Because the context is changing with the…
Abstract
Active service is one of key problems of ubiquitous computing paradigm. Context‐aware computing is helpful to carry out this service. Because the context is changing with the movement or shift of the user, its uncertainty often exists. Context‐aware computing with uncertainty includes obtaining context information, forming model, fusing of aware context and managing context information. In this paper, we focus on modeling and computing of aware context information with uncertainty for making dynamic decision during seamless mobility. Our insight is to combine dynamic context‐aware computing with improved Random Set Theory (RST) and extended D‐S Evidence Theory (EDS). We re‐examine formalism of random set, argue the limitations of the direct numerical approaches, give new modeling mode based on RST for aware context and propose our computing approach of modeled aware context.In addition, we extend classic D‐S Evidence Theory after considering context’s reliability, time‐efficiency and relativity, compare relative computing methods. After enumerating experimental examples of our active space, we provide the evaluation. By comparisons, the validity of new context‐aware computing approach based on RST or EDS for ubiquitous active service with uncertainty information has been successfully tested.
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English original movies played an important role in English learning and communication. In order to find the required movies for us from a large number of English original movies…
Abstract
Purpose
English original movies played an important role in English learning and communication. In order to find the required movies for us from a large number of English original movies and reviews, this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies. In fact, although the conventional movies recommendation algorithms have solved the problem of information overload, they still have their limitations in the case of cold start-up and sparse data.
Design/methodology/approach
To solve the aforementioned problems of conventional movies recommendation algorithms, this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning, which uses the deep deterministic policy gradient (DDPG) algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one. Meanwhile, a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.
Findings
In order to verify the feasibility and validity of the proposed algorithm, the state of the art and the proposed algorithm are compared in indexes of RMSE, recall rate and accuracy based on the MovieLens English original movie data set for the experiments. Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.
Originality/value
Applying the proposed algorithm to recommend English original movies, DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.
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Abdelkader Behdenna, Clare Dixon and Michael Fisher
The purpose of this paper is to consider the logical specification, and automated verification, of high‐level robotic behaviours.
Abstract
Purpose
The purpose of this paper is to consider the logical specification, and automated verification, of high‐level robotic behaviours.
Design/methodology/approach
The paper uses temporal logic as a formal language for providing abstractions of foraging robot behaviour, and successively extends this to multiple robots, items of food for the robots to collect, and constraints on the real‐time behaviour of robots. For each of these scenarios, proofs of relevant properties are carried out in a fully automated way. In addition to automated deductive proofs in propositional temporal logic, the possibility of having arbitrary numbers of robots involved is considered, thus allowing representations of robot swarms. This leads towards the use of first‐order temporal logics (FOTLs).
Findings
The proofs of many properties are achieved using automatic deductive temporal provers for the propositional and FOTLs.
Research limitations/implications
Many details of the problem, such as location of the robots, avoidance, etc. are abstracted away.
Practical implications
Large robot swarms are beyond the current capability of propositional temporal provers. Whilst representing and proving properties of arbitrarily large swarms using FOTLs is feasible, the representation of infinite numbers of pieces of food is outside of the decidable fragment of FOTL targeted, and practically, the provers struggle with even small numbers of pieces of food.
Originality/value
The work described in this paper is novel in that it applies automatic temporal theorem provers to proving properties of robotic behaviour.
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