In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face t...
详细信息
We focus on the α-domination problem, which is capable of modeling influence phenomena in social networks. It formally asks for a minimum cardinality subset of vertices of a given graph such that any vertex is either...
详细信息
Supply chain (SC) risk management is influenced by both spatial and temporal attributes of different entities (suppliers, retailers, and customers). Each entity has given capacity and lead time for processing and tran...
详细信息
Anytime Multi-Agent Path Finding (MAPF) is a promising paradigm for finding fast and (near-)optimal solutions to large-scale multi-agent systems within a fixed time budget. the currently leading approach builds on Lar...
详细信息
We consider a situation where agents are updating their probabilistic opinions on a set of issues with respect to the confidence they have in each other's judgements. We adapt the framework for reaching a consensu...
详细信息
ISBN:
(纸本)9783031134487;9783031134470
We consider a situation where agents are updating their probabilistic opinions on a set of issues with respect to the confidence they have in each other's judgements. We adapt the framework for reaching a consensus introduced in [2] and modified in [1] to our case of uncertain probabilistic judgements on logically related issues. We discuss possible alternative solutions for the instances where the requirements for reaching a consensus are not satisfied.
Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In con...
详细信息
ISBN:
(数字)9783031080111
ISBN:
(纸本)9783031080111;9783031080104
Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In contrast, classical dynamic programming (DP) algorithms guarantee optimal solutions, but scale badly withthe problem size. We propose Deep Policy Dynamic programming (DPDP), which aims to combine the strengths of learned neural heuristics withthose of DP algorithms. DPDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions. We evaluate our framework on the travelling salesman problem (TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and show that the neural policy improves the performance of (restricted) DP algorithms, making them competitive to strong alternatives such as LKH, while also outperforming most other 'neural approaches' for solving TSPs, VRPs and TSPTWs with 100 nodes.
the proceedings contain 68 papers. the topics discussed include: a smart trap for counting olive moths based on the internet of things and deep learning;resource allocation and scheduling of linear workflow applicatio...
ISBN:
(纸本)9798350310085
the proceedings contain 68 papers. the topics discussed include: a smart trap for counting olive moths based on the internet of things and deep learning;resource allocation and scheduling of linear workflow applications with ageing priorities and transient failures;vehicular cloud computing for population evacuation optimization;modeling user-centric threats in smart city: a hybrid threat modeling method;towards an intelligent adaptive security framework for preventing and detecting credit card fraud;clustering techniques for software product line feature identification;towards a human-in-the-loop curation: a qualitative perspective;strategic attacks on recommender systems: an obfuscation scenario;optimal beacon placement for indoor positioning using constraint programming;on the impact of deep learning and feature extraction for Arabic audio classification and speaker identification;machine learning models for early prediction of asthma attacks based on bio-signals and environmental triggers;towards an efficient and interpretable machine learning approach for energy prediction in industrial buildings: a case study in the steel industry;and time series fault detection for power line condition monitoring: a comparative study.
the ability to sample solutions of a constrained combinatorial space has important applications in areas such as probabilistic reasoning and hardware/software verification. A highly desirable property of such samples ...
详细信息
ISBN:
(纸本)9783031080111;9783031080104
the ability to sample solutions of a constrained combinatorial space has important applications in areas such as probabilistic reasoning and hardware/software verification. A highly desirable property of such samples is that they should be drawn uniformly at random, or at least nearly so. For combinatorial spaces expressed as SAT models, approaches based on universal hashing provide probabilistic guarantees about sampling uniformity. In this short paper, we apply that same approach to CP models, for which hashing functions take the form of linear constraints in modular arithmetic. We design an algorithm to generate an appropriate combination of linear modular constraints given a desired sample size. We evaluate empirically the sampling uniformity and runtime efficiency of our approach, showing it to be near-uniform at a fraction of the time needed to draw from the complete set of solutions.
this paper presents a new framework for generating test-case scenarios for autonomous vehicles. We address two challenges in automatic test-case generation: first, a formal notion of test-case complexity, and second, ...
详细信息
ISBN:
(纸本)9781665409674
this paper presents a new framework for generating test-case scenarios for autonomous vehicles. We address two challenges in automatic test-case generation: first, a formal notion of test-case complexity, and second, an algorithm to generate more-complex test-cases. We characterize the complexity of a test-case by its set of solutions, and compare two complexities by the subset relation. the novelty of our definition is that it only relies on the pass-fail criteria of the test-case, rather than indirect or subjective assessments of what may challenge an ego vehicle to pass a test-case. Given a testcase, we model the problem of generating a more complex test-case as a constraint-satisfaction search problem. the search variables are the changes to the given test-case, and the search constraints define a solution to the search problem. the constraints include steering geometry of cars, the geometry of lanes, the shape of cars, traffic rules, bounds on longitudinal acceleration of cars, etc. To conquer the computational challenge, we divide the constraints to three categories and satisfy them with simulation, answer set programming, and SMT solving. We have implemented our algorithm using the Scenic libraries and the CARLA simulator and generate test-cases for several 3-way and 4-way intersections with different topologies. Our experiments demonstrate that both CARLA's autopilot and autopilot-plus-RSS (Responsibility-Sensitive Safety) can fail as the complexity of test-cases increase.
this paper focuses on the development and design of a type-2 fuzzy logic controller (T2-FLC) for the control of a variable-speed wind energy conversion system (WECS). In this context, the maximum power point tracking ...
详细信息
暂无评论