Deceptive Path Planning via Count-Based Reinforcement Learning under Specific Time Constraint

Discrete domain swarm robotics is an emerging and challenging field of research. Unlike continuous domain where working place of the robots is a two-dimensional plane, in the discrete domain their working place is modelled by a graph. Robots are deployed on the nodes of a given graph, and they are allowed to move only along the edges of that graph. Consequently, the models used in the continuous domain are not always applicable in the discrete domain. Exemplified by the gathering problem, this article critically reviews models, assumptions, and approaches that have been proposed in solving the problems in the discrete domain.

Stochastic Satisfiability Modulo Theories (SSMT) [1] is a quantitative extension of classical Satisfiability Modulo Theories (SMT) inspired by stochastic logics. It extends SMT by the usual as well as randomized quantifiers, facilitating capture of stochastic game properties in the logic, like reachability analysis of hybrid-state Markov decision processes. Solving for SSMT formulae with quantification over finite and thus discrete domain has been addressed by Tino Teige et al. [2]. In this paper, we extend their work to SSMT over continuous quantifier domains (CSSMT) in order to enable capture of continuous disturbances and uncertainty in hybrid systems. We extend the semantics of SSMT and introduce a corresponding solving procedure. A simple case study is pursued to demonstrate applicability of our framework to reachability problems in hybrid systems.

A representation for specifying the timing constraints of real‐time systems is presented. Four parts of descriptions in a timing constraint: the path of events, timer, types of constraints and quantities of time, are explained. Under the relations among the paths, the hierarchical and network structures can be conducted. The former can be associated with the structure resulting from applying top‐down decomposition in software development. The latter represents the execution dependence of the paths. Based upon these clear and manageable structures, the completeness and consistency of timing constraints specification can be rigorously analysed. Thus, the quality of the specification of timing constraints can be improved during software development. A frame‐like software requirements specification language (FSRSL) is extended to specify timing constraints and the related descriptions in the paper.

In this paper, a multi-strategy enhanced chimpanzee optimization algorithm (MSEChOA) acting on path planning for delivery vehicles is proposed to achieve the goal of shortening global path lengths for the delivery unmanned vehicles and obtaining safer paths. In the initialization phase, the algorithm introduces a hybrid good point set and chaos initialization strategy, combining the advantages of both to enhance the randomness and homogeneity of the initial population. After that, it incorporates a benchmark weight strategy and Gaussian-modulated cosine factor to adaptively adjust algorithm parameters, thus balancing the global and local search capabilities and improving the search efficiency. In the end, the algorithm incorporates a global search enhancer (GEE) to further enhance the global search capability in the later phases, thereby avoiding local optima. Experiments on several benchmark test functions show that MSEChOA outperforms traditional ChOA and other optimization algorithms in optimization accuracy and convergence speed. In simulation experiments, MSEChOA shows stronger path planning ability and good computational efficiency in both simple and complex environments, proving its feasibility and superiority in the field of path planning.

Natural heuristic methods, like the particle swarm optimization and many others, enjoy fast convergence towards optimal solution via inter-particle communications. Many applications of such methods are applied to the optimization in engineering, but only a few to the optimization in statistics. It is especially difficult to implement in the optimization problems of experimental designs as the search space is mostly discrete, while most natural heuristic methods are limited to searching continuous domains. This paper introduces a new natural heuristic method called Swarm Intelligence Based method for optimizing problem with a discrete domain. It includes two new operations, MIX and MOVE, for combining two particles and selecting the best particle respectively. This method is ready for the search of both continuous and discrete domains, and its global best particle is guaranteed to monotonically move towards the optimum. Several demonstrations on the optimization of experimental designs are given at the end of this paper.

We focus on the problem of graph matching that is fundamental in computer vision and machine learning. Many state-of-the-arts frequently formulate it as integer quadratic programming, which incorporates both unary and second-order terms. This formulation is in general NP-hard thus obtaining an exact solution is computationally intractable. Therefore most algorithms seek the approximate optimum by relaxing techniques. This paper commences with the finding of the “circular” character of solution chain obtained by the iterative Gradient Assignment (via Hungarian method) in the discrete domain, and proposes a method for guiding the solver converging to a fixed point, resulting a convergent algorithm for graph matching in discrete domain. Furthermore, we extend the algorithms to their counterparts in continuous domain, proving the classical graduated assignment algorithm will converge to a double-circular solution chain, and the proposed Soft Constrained Graduated Assignment (SCGA) method will converge to a fixed (discrete) point, both under wild conditions. Competitive performances are reported in both synthetic and real experiments.

This paper describes two methods to specify timing constraints in behavioral VHDL for high-level synthesis purposes. The first method specifies timing constraints on sequences of statements by using predefined procedures. The second method provides support for specification of timing constraints across process borders based on concurrent assert statements on signal events. The paper discusses also an approach to synthesize hardware with timing constraints and concentrates in particular on how to ensure consistency between the behavior of the simulation model and that of the synthesized hardware.

Recently, the ant colony algorithm has been widely used in the field of path planning, which is a key technology required for ship operations in offshore wind farms to improve navigation efficiency and power generation. However, the ant colony algorithm has the defects of a long search time and stagnation in the early stage of the operation and maintenance path planning of offshore wind farms, and it easily falls into the problem of local optima; furthermore, the ant colony algorithm uses incremental construction to build a complete itinerary path, it takes a lot of time to search the path, and only the suboptimal solution is obtained. To address the above problems, in this paper, we propose a multi‐agent‐based operation and maintenance model for offshore wind farms. Specifically, the introduction of the heuristic factor can optimize the local optimal solution and make the ant colony algorithm clearer when searching for the target. Based on this, increasing the pheromone adjustment factor can eliminate the invalid path from searching and select a high‐quality path. In addition, by integrating the genetic algorithm, it is possible to select, cross, and mutate to simulate the natural evolution process to search for the optimal solution, reduce the similarity of the paths constructed by the ant colony, reduce the probability of algorithm stagnation, improve the convergence speed, and improve the time efficiency and solution accuracy of the algorithm. Simulation experiments on a series of benchmark datasets show that the proposed GA‐PACO algorithm achieves better performance in global search and path planning than the existing three algorithms.

We study the problem of providing real-time service to hard and soft real-time messages in wavelength division multiplexing (WDM) optical networks. We propose and evaluate a set of scheduling algorithms which schedule message transmissions in single-hop WDM passive star networks based on specific time constraints. We compare the performances of our algorithms with that of the typical WDM scheduling algorithm which does not consider the time constraint of the transmitted messages by discrete-event simulations and an analytical model. We find that the improvement on real-time performance can be attributed to our scheduling algorithms where the time constraint of messages has been taken into consideration. This study suggests that when scheduling real-time messages in WDM networks, one has to consider not only the problem of resources allocation in the network but also the problem of sequencing messages based on their time constraints.

Modular design is a key challenge for enabling large-scale reuse of hardware modules. Unlike software, however, hardware designs correspond to physical circuits and inherit constraints from them. Timing constraints—which cycle a signal arrives, when an input is read—and structural constraints—how often a multiplier accepts new inputs—are fundamental to hardware interfaces. Existing hardware design languages do not provide a way to encode these constraints; a user must read documentation, build scripts, or in the worst case, a module’s implementation to understand how to use it. We present Filament, a language for modular hardware design that supports the specification and enforcement of timing and structural constraints for statically scheduled pipelines. Filament usestimeline types, which describe the intervals of clock-cycle time when a given signal is available or required. Filament enablessafe compositionof hardware modules, ensures that the resulting designs are correctly pipelined, and predictably lowers them to efficient hardware.

Adaptive Self-Tuning PIDD2 Algorithm

Many variations in design methods for aircraft digital flight control have been proposed in the literature. In general, the methods fall into two categories: those where the design is done in the continuous domain (or s-plane), and those where the design is done in the discrete domain (or z-plane). This paper evaluates several variations of each category and compares them for various flight control modes of the Langley TCV Boeing 737 aircraft. Design method fidelity is evaluated by examining closed loop root movement and the frequency response of the discretely controlled continuous aircraft. It was found that all methods provided acceptable performance for sample rates greater than 10 cps except the 'uncompensated s-plane design' method which was acceptable above 20 cps. A design procedure based on optimal control methods was proposed that provided the best fidelity at very slow sample rates and required no design iterations for changing sample rates.

Noise is inherent in real world datasets and modeling noise is critical during training as it is effective in regularization. Recently, novel semi-supervised deep learning techniques have demonstrated tremendous potential when learning with very limited labeled training data in image processing tasks. A critical aspect of these semi-supervised learning techniques is augmenting the input or the network with noise to be able to learn robust models. While modeling noise is relatively straightforward in continuous domains such as image classification, it is not immediately apparent how noise can be modeled in discrete domains such as language. Our work aims to address this gap by exploring different noise strategies for the semi-supervised named entity classification task, including statistical methods such as adding Gaussian noise to input embeddings, and linguistically-inspired ones such as dropping words and replacing words with their synonyms. We compare their performance on two benchmark datasets (OntoNotes and CoNLL) for named entity classification. Our results indicate that noise strategies that are linguistically informed perform at least as well as statistical approaches, while being simpler and requiring minimal tuning.

Multiscale moment-based transformations, such as the multiscale Harris corner point detector, have been used in image processing applications for several years. Typically, such transforms are used to identify objects-of-interest in a given image, which, in turn, facilitate target tracking and registration. Though these transforms are usually applied to digitally sampled images, many of their properties were previously only known to hold for images over continuous domains. We prove that many of these properties indeed generalize to images over discrete domains. In particular, after introducing a mathematically well-behaved method for rotating an image over the two-dimensional integer lattice, we show that this rotation commutes with the moment-based transform in the expected manner.

Anisotropic diffusion is widely used for noise reduction. The performance of anisotropic diffusion, in general, depends on the shape of the energy surface. The partial differential equation model is established and analyzed in the continuous domain while is implemented in the discrete domain. Therefore, the anisotropic diffusion bears some fuzziness due to the approximation. We present a novel noise removal algorithm based on fuzzy logic and anisotropic diffusion theory. The experimental results demonstrate that the proposed method has the advantage of maximizing noise reduction and preserving fine details of the images. In addition, the method can enhance the contrast of the images well.